From 6392d8037da8d9162650200655618121a1cfd57a Mon Sep 17 00:00:00 2001 From: Keshav Sharma <61562452+keshav340@users.noreply.github.com> Date: Sat, 9 Oct 2021 05:53:58 -0700 Subject: [PATCH 01/12] Bonus lesson added --- 2-Working-With-Data/07-python/bonus-lesson.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 2-Working-With-Data/07-python/bonus-lesson.ipynb diff --git a/2-Working-With-Data/07-python/bonus-lesson.ipynb b/2-Working-With-Data/07-python/bonus-lesson.ipynb new file mode 100644 index 0000000..44dcbb3 --- /dev/null +++ b/2-Working-With-Data/07-python/bonus-lesson.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"name":"ir","display_name":"R","language":"R"},"language_info":{"name":"R","codemirror_mode":"r","pygments_lexer":"r","mimetype":"text/x-r-source","file_extension":".r","version":"4.0.5"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This R environment comes with many helpful analytics packages installed\n# It is defined by the kaggle/rstats Docker image: https://github.com/kaggle/docker-rstats\n# For example, here's a helpful package to load\n\nlibrary(tidyverse) # metapackage of all tidyverse packages\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nlist.files(path = \"../input\")\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"051d70d956493feee0c6d64651c6a088724dca2a","_execution_state":"idle","execution":{"iopub.status.busy":"2021-10-08T18:11:24.952103Z","iopub.execute_input":"2021-10-08T18:11:24.954562Z","iopub.status.idle":"2021-10-08T18:11:26.309552Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"library(zoo)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(ggfortify)\nlibrary(dplyr)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:15:22.966831Z","iopub.execute_input":"2021-10-09T12:15:22.968941Z","iopub.status.idle":"2021-10-09T12:15:23.546202Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"## Series","metadata":{}},{"cell_type":"markdown","source":"### Series is like a list or 1D-array, but with index.It is generally used to represent data associated with time.","metadata":{}},{"cell_type":"code","source":"stock_prices = c(799,1174.8,865.1,1334.6,635.4,918.5,685.5,998.6,784.2,985,882.8,1071)\nstock_prices_glennmark = ts(stock_prices,start = c(2020,1,1),frequency = 12)\nstock_prices_glennmark\nplot(stock_prices_glennmark)\n","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:15:54.612622Z","iopub.execute_input":"2021-10-09T12:15:54.735899Z","iopub.status.idle":"2021-10-09T12:15:55.008804Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"# CREATING A TIME SERIES\nrandomData<- rnorm(100)\nmonth <- ts(randomData,start=c(2020,1),frequency=12)\nplot(month)\n## it bit much longer a it is 100/4 takes much longer year","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:16:03.558553Z","iopub.execute_input":"2021-10-09T12:16:03.560254Z","iopub.status.idle":"2021-10-09T12:16:03.679285Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"**DataFrame**\n#### It is a two dimensional data structure in which data is inserted in tabular form.","metadata":{}},{"cell_type":"code","source":"STOCK_PRICE <- data.frame(\nUSD=c(1.02,1.085,2.05,3.45,4.05,5.32,3.45,4.65,6.65,9.23,10.25,12.69),\nEURO = c(0.85,1.02,1.85,3.02,3.35,4.35,2.65,3.95,5.55,8.90,9.80,11.50))\nSTOCK_PRICES <- ts(STOCK_PRICE,start = c(2020,1,1),frequency = 12)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:24:50.750390Z","iopub.execute_input":"2021-10-09T12:24:50.752297Z","iopub.status.idle":"2021-10-09T12:24:50.767609Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"STOCK_PRICES","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:24:53.233062Z","iopub.execute_input":"2021-10-09T12:24:53.234839Z","iopub.status.idle":"2021-10-09T12:24:53.260478Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"## Reading Data into R","metadata":{}},{"cell_type":"markdown","source":"### 1. Read a CSV File into R","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"dataset <- read.table(file=\" \",header = TRUE, sep = \",\")","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Data Manipluation","metadata":{}},{"cell_type":"code","source":"dim(STOCK_PRICES )","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:16:21.200787Z","iopub.execute_input":"2021-10-09T12:16:21.202859Z","iopub.status.idle":"2021-10-09T12:16:21.224888Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"head(STOCK_PRICES )","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:16:26.089604Z","iopub.execute_input":"2021-10-09T12:16:26.091545Z","iopub.status.idle":"2021-10-09T12:16:26.114797Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"summary(STOCK_PRICES )","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:16:28.433758Z","iopub.execute_input":"2021-10-09T12:16:28.435883Z","iopub.status.idle":"2021-10-09T12:16:28.457663Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"#### We ae slicing to find subsets columns of 5-7 of datasets","metadata":{}},{"cell_type":"code","source":"STOCK_PRICES [5,]\n# series","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:16:35.848118Z","iopub.execute_input":"2021-10-09T12:16:35.850327Z","iopub.status.idle":"2021-10-09T12:16:35.871805Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"STOCK_PRICES [3:8,]","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:16:50.004019Z","iopub.execute_input":"2021-10-09T12:16:50.006116Z","iopub.status.idle":"2021-10-09T12:16:50.034007Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"slice(STOCK_PRICE ,3:8)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:25:09.777288Z","iopub.execute_input":"2021-10-09T12:25:09.778989Z","iopub.status.idle":"2021-10-09T12:25:09.806709Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"### Pip OPERATOR","metadata":{}},{"cell_type":"code","source":"STOCK_PRICE %>% slice(3:5)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:25:25.256136Z","iopub.execute_input":"2021-10-09T12:25:25.257863Z","iopub.status.idle":"2021-10-09T12:25:25.278594Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":"### SLICE SUBSETS the first n rows of dataframe and slice tail subsets the last n rows of dataframe","metadata":{}},{"cell_type":"code","source":"slice_head(STOCK_PRICE,n=2)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:25:55.079932Z","iopub.execute_input":"2021-10-09T12:25:55.081670Z","iopub.status.idle":"2021-10-09T12:25:55.103342Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"slice_tail(STOCK_PRICE,n=2)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:26:04.997678Z","iopub.execute_input":"2021-10-09T12:26:04.999435Z","iopub.status.idle":"2021-10-09T12:26:05.021799Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"markdown","source":"### Slice_max subsets the n rows of a dataset with n largest value with respect to a variable","metadata":{}},{"cell_type":"code","source":"slice_max(STOCK_PRICE,EURO, n=3)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:26:29.905938Z","iopub.execute_input":"2021-10-09T12:26:29.907674Z","iopub.status.idle":"2021-10-09T12:26:29.934518Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"slice_sample(STOCK_PRICE,n=5)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:26:42.047503Z","iopub.execute_input":"2021-10-09T12:26:42.049113Z","iopub.status.idle":"2021-10-09T12:26:42.071494Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"markdown","source":"## Filtering","metadata":{}},{"cell_type":"markdown","source":"### filters out the rows of dataframe which doesn't meet criteria","metadata":{}},{"cell_type":"code","source":"STOCK_PRICE[STOCK_PRICE$EURO>3.35,]","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:29:42.107479Z","iopub.execute_input":"2021-10-09T12:29:42.109116Z","iopub.status.idle":"2021-10-09T12:29:42.131224Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"## Grouping ","metadata":{}},{"cell_type":"markdown","source":"### group_by function group rows of data and frame with respect to one or more variabes","metadata":{}},{"cell_type":"code","source":"library(dplyr)","metadata":{"execution":{"iopub.status.busy":"2021-10-08T13:13:57.808888Z","iopub.execute_input":"2021-10-08T13:13:57.810559Z","iopub.status.idle":"2021-10-08T13:13:57.843966Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df1 <- group_by(STOCK_PRICE,EURO)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:36:27.270590Z","iopub.execute_input":"2021-10-09T12:36:27.272456Z","iopub.status.idle":"2021-10-09T12:36:27.298281Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"summarise(df1,mymean =mean(EURO))","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:36:38.887295Z","iopub.execute_input":"2021-10-09T12:36:38.889081Z","iopub.status.idle":"2021-10-09T12:36:38.919223Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"df2<-group_by(df1,USD>3.2)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:36:53.022367Z","iopub.execute_input":"2021-10-09T12:36:53.023993Z","iopub.status.idle":"2021-10-09T12:36:53.051015Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"summarise(df2,mymean = mean(USD))","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:37:05.348959Z","iopub.execute_input":"2021-10-09T12:37:05.350547Z","iopub.status.idle":"2021-10-09T12:37:05.374010Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"markdown","source":"## SELECT","metadata":{}},{"cell_type":"code","source":"df<-select(STOCK_PRICE,EURO,USD)\ndf","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:37:45.562234Z","iopub.execute_input":"2021-10-09T12:37:45.563814Z","iopub.status.idle":"2021-10-09T12:37:45.591553Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"df<- select(df,-EURO)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:37:55.423706Z","iopub.execute_input":"2021-10-09T12:37:55.425323Z","iopub.status.idle":"2021-10-09T12:37:55.440506Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:37:58.655523Z","iopub.execute_input":"2021-10-09T12:37:58.657351Z","iopub.status.idle":"2021-10-09T12:37:58.687523Z"},"trusted":true},"execution_count":49,"outputs":[]},{"cell_type":"markdown","source":"## Arrange","metadata":{}},{"cell_type":"code","source":"df <- arrange(STOCK_PRICE, desc(USD))","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:38:19.525371Z","iopub.execute_input":"2021-10-09T12:38:19.526944Z","iopub.status.idle":"2021-10-09T12:38:19.545423Z"},"trusted":true},"execution_count":51,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:38:22.301574Z","iopub.execute_input":"2021-10-09T12:38:22.303359Z","iopub.status.idle":"2021-10-09T12:38:22.326992Z"},"trusted":true},"execution_count":52,"outputs":[]},{"cell_type":"code","source":"df <- arrange(STOCK_PRICE, desc(USD),EURO)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:38:45.428273Z","iopub.execute_input":"2021-10-09T12:38:45.429970Z","iopub.status.idle":"2021-10-09T12:38:45.446438Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:38:48.362531Z","iopub.execute_input":"2021-10-09T12:38:48.364239Z","iopub.status.idle":"2021-10-09T12:38:48.390254Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"rename(STOCK_PRICE, DOLLAR = USD)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:39:28.495960Z","iopub.execute_input":"2021-10-09T12:39:28.497625Z","iopub.status.idle":"2021-10-09T12:39:28.525934Z"},"trusted":true},"execution_count":57,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file From f4567ed623f74e68bf518d44c8f11af4fb098fe2 Mon Sep 17 00:00:00 2001 From: Keshav Sharma <61562452+keshav340@users.noreply.github.com> Date: Sat, 9 Oct 2021 20:21:44 -0700 Subject: [PATCH 02/12] Delete bonus-lesson.ipynb --- 2-Working-With-Data/07-python/bonus-lesson.ipynb | 1 - 1 file changed, 1 deletion(-) delete mode 100644 2-Working-With-Data/07-python/bonus-lesson.ipynb diff --git a/2-Working-With-Data/07-python/bonus-lesson.ipynb b/2-Working-With-Data/07-python/bonus-lesson.ipynb deleted file mode 100644 index 44dcbb3..0000000 --- a/2-Working-With-Data/07-python/bonus-lesson.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"metadata":{"kernelspec":{"name":"ir","display_name":"R","language":"R"},"language_info":{"name":"R","codemirror_mode":"r","pygments_lexer":"r","mimetype":"text/x-r-source","file_extension":".r","version":"4.0.5"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This R environment comes with many helpful analytics packages installed\n# It is defined by the kaggle/rstats Docker image: https://github.com/kaggle/docker-rstats\n# For example, here's a helpful package to load\n\nlibrary(tidyverse) # metapackage of all tidyverse packages\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nlist.files(path = \"../input\")\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"051d70d956493feee0c6d64651c6a088724dca2a","_execution_state":"idle","execution":{"iopub.status.busy":"2021-10-08T18:11:24.952103Z","iopub.execute_input":"2021-10-08T18:11:24.954562Z","iopub.status.idle":"2021-10-08T18:11:26.309552Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"library(zoo)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(ggfortify)\nlibrary(dplyr)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:15:22.966831Z","iopub.execute_input":"2021-10-09T12:15:22.968941Z","iopub.status.idle":"2021-10-09T12:15:23.546202Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"## Series","metadata":{}},{"cell_type":"markdown","source":"### Series is like a list or 1D-array, but with index.It is generally used to represent data associated with time.","metadata":{}},{"cell_type":"code","source":"stock_prices = c(799,1174.8,865.1,1334.6,635.4,918.5,685.5,998.6,784.2,985,882.8,1071)\nstock_prices_glennmark = ts(stock_prices,start = c(2020,1,1),frequency = 12)\nstock_prices_glennmark\nplot(stock_prices_glennmark)\n","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:15:54.612622Z","iopub.execute_input":"2021-10-09T12:15:54.735899Z","iopub.status.idle":"2021-10-09T12:15:55.008804Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"# CREATING A TIME SERIES\nrandomData<- rnorm(100)\nmonth <- ts(randomData,start=c(2020,1),frequency=12)\nplot(month)\n## it bit much longer a it is 100/4 takes much longer year","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:16:03.558553Z","iopub.execute_input":"2021-10-09T12:16:03.560254Z","iopub.status.idle":"2021-10-09T12:16:03.679285Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"**DataFrame**\n#### It is a two dimensional data structure in which data is inserted in tabular form.","metadata":{}},{"cell_type":"code","source":"STOCK_PRICE <- data.frame(\nUSD=c(1.02,1.085,2.05,3.45,4.05,5.32,3.45,4.65,6.65,9.23,10.25,12.69),\nEURO = c(0.85,1.02,1.85,3.02,3.35,4.35,2.65,3.95,5.55,8.90,9.80,11.50))\nSTOCK_PRICES <- ts(STOCK_PRICE,start = c(2020,1,1),frequency = 12)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:24:50.750390Z","iopub.execute_input":"2021-10-09T12:24:50.752297Z","iopub.status.idle":"2021-10-09T12:24:50.767609Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"STOCK_PRICES","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:24:53.233062Z","iopub.execute_input":"2021-10-09T12:24:53.234839Z","iopub.status.idle":"2021-10-09T12:24:53.260478Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"## Reading Data into R","metadata":{}},{"cell_type":"markdown","source":"### 1. Read a CSV File into R","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"dataset <- read.table(file=\" \",header = TRUE, sep = \",\")","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Data Manipluation","metadata":{}},{"cell_type":"code","source":"dim(STOCK_PRICES )","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:16:21.200787Z","iopub.execute_input":"2021-10-09T12:16:21.202859Z","iopub.status.idle":"2021-10-09T12:16:21.224888Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"head(STOCK_PRICES )","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:16:26.089604Z","iopub.execute_input":"2021-10-09T12:16:26.091545Z","iopub.status.idle":"2021-10-09T12:16:26.114797Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"summary(STOCK_PRICES )","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:16:28.433758Z","iopub.execute_input":"2021-10-09T12:16:28.435883Z","iopub.status.idle":"2021-10-09T12:16:28.457663Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"#### We ae slicing to find subsets columns of 5-7 of datasets","metadata":{}},{"cell_type":"code","source":"STOCK_PRICES [5,]\n# series","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:16:35.848118Z","iopub.execute_input":"2021-10-09T12:16:35.850327Z","iopub.status.idle":"2021-10-09T12:16:35.871805Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"STOCK_PRICES [3:8,]","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:16:50.004019Z","iopub.execute_input":"2021-10-09T12:16:50.006116Z","iopub.status.idle":"2021-10-09T12:16:50.034007Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"slice(STOCK_PRICE ,3:8)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:25:09.777288Z","iopub.execute_input":"2021-10-09T12:25:09.778989Z","iopub.status.idle":"2021-10-09T12:25:09.806709Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"### Pip OPERATOR","metadata":{}},{"cell_type":"code","source":"STOCK_PRICE %>% slice(3:5)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:25:25.256136Z","iopub.execute_input":"2021-10-09T12:25:25.257863Z","iopub.status.idle":"2021-10-09T12:25:25.278594Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":"### SLICE SUBSETS the first n rows of dataframe and slice tail subsets the last n rows of dataframe","metadata":{}},{"cell_type":"code","source":"slice_head(STOCK_PRICE,n=2)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:25:55.079932Z","iopub.execute_input":"2021-10-09T12:25:55.081670Z","iopub.status.idle":"2021-10-09T12:25:55.103342Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"slice_tail(STOCK_PRICE,n=2)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:26:04.997678Z","iopub.execute_input":"2021-10-09T12:26:04.999435Z","iopub.status.idle":"2021-10-09T12:26:05.021799Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"markdown","source":"### Slice_max subsets the n rows of a dataset with n largest value with respect to a variable","metadata":{}},{"cell_type":"code","source":"slice_max(STOCK_PRICE,EURO, n=3)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:26:29.905938Z","iopub.execute_input":"2021-10-09T12:26:29.907674Z","iopub.status.idle":"2021-10-09T12:26:29.934518Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"slice_sample(STOCK_PRICE,n=5)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:26:42.047503Z","iopub.execute_input":"2021-10-09T12:26:42.049113Z","iopub.status.idle":"2021-10-09T12:26:42.071494Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"markdown","source":"## Filtering","metadata":{}},{"cell_type":"markdown","source":"### filters out the rows of dataframe which doesn't meet criteria","metadata":{}},{"cell_type":"code","source":"STOCK_PRICE[STOCK_PRICE$EURO>3.35,]","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:29:42.107479Z","iopub.execute_input":"2021-10-09T12:29:42.109116Z","iopub.status.idle":"2021-10-09T12:29:42.131224Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"## Grouping ","metadata":{}},{"cell_type":"markdown","source":"### group_by function group rows of data and frame with respect to one or more variabes","metadata":{}},{"cell_type":"code","source":"library(dplyr)","metadata":{"execution":{"iopub.status.busy":"2021-10-08T13:13:57.808888Z","iopub.execute_input":"2021-10-08T13:13:57.810559Z","iopub.status.idle":"2021-10-08T13:13:57.843966Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df1 <- group_by(STOCK_PRICE,EURO)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:36:27.270590Z","iopub.execute_input":"2021-10-09T12:36:27.272456Z","iopub.status.idle":"2021-10-09T12:36:27.298281Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"summarise(df1,mymean =mean(EURO))","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:36:38.887295Z","iopub.execute_input":"2021-10-09T12:36:38.889081Z","iopub.status.idle":"2021-10-09T12:36:38.919223Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"df2<-group_by(df1,USD>3.2)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:36:53.022367Z","iopub.execute_input":"2021-10-09T12:36:53.023993Z","iopub.status.idle":"2021-10-09T12:36:53.051015Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"summarise(df2,mymean = mean(USD))","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:37:05.348959Z","iopub.execute_input":"2021-10-09T12:37:05.350547Z","iopub.status.idle":"2021-10-09T12:37:05.374010Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"markdown","source":"## SELECT","metadata":{}},{"cell_type":"code","source":"df<-select(STOCK_PRICE,EURO,USD)\ndf","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:37:45.562234Z","iopub.execute_input":"2021-10-09T12:37:45.563814Z","iopub.status.idle":"2021-10-09T12:37:45.591553Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"df<- select(df,-EURO)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:37:55.423706Z","iopub.execute_input":"2021-10-09T12:37:55.425323Z","iopub.status.idle":"2021-10-09T12:37:55.440506Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:37:58.655523Z","iopub.execute_input":"2021-10-09T12:37:58.657351Z","iopub.status.idle":"2021-10-09T12:37:58.687523Z"},"trusted":true},"execution_count":49,"outputs":[]},{"cell_type":"markdown","source":"## Arrange","metadata":{}},{"cell_type":"code","source":"df <- arrange(STOCK_PRICE, desc(USD))","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:38:19.525371Z","iopub.execute_input":"2021-10-09T12:38:19.526944Z","iopub.status.idle":"2021-10-09T12:38:19.545423Z"},"trusted":true},"execution_count":51,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:38:22.301574Z","iopub.execute_input":"2021-10-09T12:38:22.303359Z","iopub.status.idle":"2021-10-09T12:38:22.326992Z"},"trusted":true},"execution_count":52,"outputs":[]},{"cell_type":"code","source":"df <- arrange(STOCK_PRICE, desc(USD),EURO)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:38:45.428273Z","iopub.execute_input":"2021-10-09T12:38:45.429970Z","iopub.status.idle":"2021-10-09T12:38:45.446438Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:38:48.362531Z","iopub.execute_input":"2021-10-09T12:38:48.364239Z","iopub.status.idle":"2021-10-09T12:38:48.390254Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"rename(STOCK_PRICE, DOLLAR = USD)","metadata":{"execution":{"iopub.status.busy":"2021-10-09T12:39:28.495960Z","iopub.execute_input":"2021-10-09T12:39:28.497625Z","iopub.status.idle":"2021-10-09T12:39:28.525934Z"},"trusted":true},"execution_count":57,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file From 8028c0f6717b28b50625700323acafb195145473 Mon Sep 17 00:00:00 2001 From: Keshav Sharma Date: Sat, 9 Oct 2021 20:24:19 -0700 Subject: [PATCH 03/12] Create pandas.ipynb --- 2-Working-With-Data/R/pandas.ipynb | 936 +++++++++++++++++++++++++++++ 1 file changed, 936 insertions(+) create mode 100644 2-Working-With-Data/R/pandas.ipynb diff --git a/2-Working-With-Data/R/pandas.ipynb b/2-Working-With-Data/R/pandas.ipynb new file mode 100644 index 0000000..323f013 --- /dev/null +++ b/2-Working-With-Data/R/pandas.ipynb @@ -0,0 +1,936 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 73, + "id": "c105e391", + "metadata": {}, + "outputs": [], + "source": [ + "library(dplyr)\n", + "library(tidyverse)" + ] + }, + { + "cell_type": "markdown", + "id": "00c41f19", + "metadata": {}, + "source": [ + "## Series" + ] + }, + { + "cell_type": "markdown", + "id": "d3490356", + "metadata": {}, + "source": [ + "a<- 1:9" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "2ef3725d", + "metadata": {}, + "outputs": [], + "source": [ + "b = c(\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\")" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "14e6152d", + "metadata": {}, + "outputs": [], + "source": [ + "a1 = length(a)\n", + "b1 = length(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "432b0c2b", + "metadata": {}, + "outputs": [], + "source": [ + "a = data.frame(a,row.names = c(1:a1))" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "adb4391f", + "metadata": {}, + "outputs": [], + "source": [ + "b = data.frame(b,row.names = c(1:b1))" + ] + }, + { + "cell_type": "markdown", + "id": "9d27aa12", + "metadata": {}, + "source": [ + "idx<- seq.Date(from = as.Date(\"20-01-01\"), to = as.Date(\"2001-03-31\"), by = \"day\",)\n", + "print(length(idx))\n" + ] + }, + { + "cell_type": "markdown", + "id": "ee4be842", + "metadata": {}, + "source": [ + "## DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "id": "a8585654", + "metadata": {}, + "outputs": [], + "source": [ + "a = data.frame(a,row.names = c(1:a1))" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "2b8c18b3", + "metadata": {}, + "outputs": [], + "source": [ + "b = data.frame(b,row.names = c(1:b1))" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "60b643d5", + "metadata": {}, + "outputs": [ + { + "ename": "ERROR", + "evalue": "Error in bar.plot(data): could not find function \"bar.plot\"\n", + "output_type": "error", + "traceback": [ + "Error in bar.plot(data): could not find function \"bar.plot\"\nTraceback:\n" + ] + } + ], + "source": [ + "bar.plot(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "113578b5", + "metadata": {}, + "outputs": [], + "source": [ + "df <- data.frame(a,b)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "ef1dff63", + "metadata": {}, + "outputs": [], + "source": [ + "df = df %>% \n", + " rename(\n", + " A = a,\n", + " B = b\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "1d80bf43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
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\n" + ], + "text/latex": [ + "A data.frame: 5 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & A & B & DivA & LenB\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4 & 1\\\\\n", + "\t2 & 2 & like & -3 & 4\\\\\n", + "\t3 & 3 & to & -2 & 2\\\\\n", + "\t4 & 4 & use & -1 & 3\\\\\n", + "\t5 & 5 & Python & 0 & 6\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 5 × 4\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | I | -4 | 1 |\n", + "| 2 | 2 | like | -3 | 4 |\n", + "| 3 | 3 | to | -2 | 2 |\n", + "| 4 | 4 | use | -1 | 3 |\n", + "| 5 | 5 | Python | 0 | 6 |\n", + "\n" + ], + "text/plain": [ + " A B DivA LenB\n", + "1 1 I -4 1 \n", + "2 2 like -3 4 \n", + "3 3 to -2 2 \n", + "4 4 use -1 3 \n", + "5 5 Python 0 6 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[0:5,]" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "ccebdda2", + "metadata": {}, + "outputs": [], + "source": [ + " df1 = group_by(df,LenB)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "f2dcb719", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 5 × 2
LenBmymean
<int><dbl>
11
22
33
44
66
\n" + ], + "text/latex": [ + "A tibble: 5 × 2\n", + "\\begin{tabular}{ll}\n", + " LenB & mymean\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t 1 & 1\\\\\n", + "\t 2 & 2\\\\\n", + "\t 3 & 3\\\\\n", + "\t 4 & 4\\\\\n", + "\t 6 & 6\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 5 × 2\n", + "\n", + "| LenB <int> | mymean <dbl> |\n", + "|---|---|\n", + "| 1 | 1 |\n", + "| 2 | 2 |\n", + "| 3 | 3 |\n", + "| 4 | 4 |\n", + "| 6 | 6 |\n", + "\n" + ], + "text/plain": [ + " LenB mymean\n", + "1 1 1 \n", + "2 2 2 \n", + "3 3 3 \n", + "4 4 4 \n", + "5 6 6 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summarise(df1,mymean =mean(LenB))" + ] + }, + { + "cell_type": "markdown", + "id": "d8eb00bc", + "metadata": {}, + "source": [ + "## Printing and Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b96b6be0", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = read.csv(\"file name\")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "3b1a4735", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 4
ABDivALenB
<int><chr><dbl><int>
11I -41
22like -34
33to -22
44use -13
55Python 06
66and 13
\n" + ], + "text/latex": [ + "A data.frame: 6 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & A & B & DivA & LenB\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4 & 1\\\\\n", + "\t2 & 2 & like & -3 & 4\\\\\n", + "\t3 & 3 & to & -2 & 2\\\\\n", + "\t4 & 4 & use & -1 & 3\\\\\n", + "\t5 & 5 & Python & 0 & 6\\\\\n", + "\t6 & 6 & and & 1 & 3\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 4\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | I | -4 | 1 |\n", + "| 2 | 2 | like | -3 | 4 |\n", + "| 3 | 3 | to | -2 | 2 |\n", + "| 4 | 4 | use | -1 | 3 |\n", + "| 5 | 5 | Python | 0 | 6 |\n", + "| 6 | 6 | and | 1 | 3 |\n", + "\n" + ], + "text/plain": [ + " A B DivA LenB\n", + "1 1 I -4 1 \n", + "2 2 like -3 4 \n", + "3 3 to -2 2 \n", + "4 4 use -1 3 \n", + "5 5 Python 0 6 \n", + "6 6 and 1 3 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "0af4e0e5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(df$A,type = 'o',xlab = \"no\",ylab = \"A\")" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "08103a39", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "barplot(df$A, ylab = 'A',xlab = 'no')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "768d4300", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.1.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 55670f0bcd4bdfc4b90731a5cfbdcdc8fad2740e Mon Sep 17 00:00:00 2001 From: Keshav Sharma <61562452+keshav340@users.noreply.github.com> Date: Sat, 9 Oct 2021 20:26:17 -0700 Subject: [PATCH 04/12] Delete 2-Working-With-Data/R directory --- 2-Working-With-Data/R/pandas.ipynb | 936 ----------------------------- 1 file changed, 936 deletions(-) delete mode 100644 2-Working-With-Data/R/pandas.ipynb diff --git a/2-Working-With-Data/R/pandas.ipynb b/2-Working-With-Data/R/pandas.ipynb deleted file mode 100644 index 323f013..0000000 --- a/2-Working-With-Data/R/pandas.ipynb +++ /dev/null @@ -1,936 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 73, - "id": "c105e391", - "metadata": {}, - "outputs": [], - "source": [ - "library(dplyr)\n", - "library(tidyverse)" - ] - }, - { - "cell_type": "markdown", - "id": "00c41f19", - "metadata": {}, - "source": [ - "## Series" - ] - }, - { - "cell_type": "markdown", - "id": "d3490356", - "metadata": {}, - "source": [ - "a<- 1:9" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "2ef3725d", - "metadata": {}, - "outputs": [], - "source": [ - "b = c(\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\")" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "14e6152d", - "metadata": {}, - "outputs": [], - "source": [ - "a1 = length(a)\n", - "b1 = length(b)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "432b0c2b", - "metadata": {}, - "outputs": [], - "source": [ - "a = data.frame(a,row.names = c(1:a1))" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "adb4391f", - "metadata": {}, - "outputs": [], - "source": [ - "b = data.frame(b,row.names = c(1:b1))" - ] - }, - { - "cell_type": "markdown", - "id": "9d27aa12", - "metadata": {}, - "source": [ - "idx<- seq.Date(from = as.Date(\"20-01-01\"), to = as.Date(\"2001-03-31\"), by = \"day\",)\n", - "print(length(idx))\n" - ] - }, - { - "cell_type": "markdown", - "id": "ee4be842", - "metadata": {}, - "source": [ - "## DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "id": "a8585654", - "metadata": {}, - "outputs": [], - "source": [ - "a = data.frame(a,row.names = c(1:a1))" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "id": "2b8c18b3", - 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A data.frame: 9 × 2
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A data.frame: 9 × 3
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A data.frame: 9 × 4
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A tibble: 5 × 2
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A data.frame: 6 × 4
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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 420, - "width": 420 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "plot(df$A,type = 'o',xlab = \"no\",ylab = \"A\")" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "08103a39", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 420, - "width": 420 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "barplot(df$A, ylab = 'A',xlab = 'no')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "768d4300", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "4.1.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From f76221fac3eac574ac70f4f311c3daa724d7ac7d Mon Sep 17 00:00:00 2001 From: Keshav Sharma Date: Sat, 9 Oct 2021 20:28:25 -0700 Subject: [PATCH 05/12] Update pandas.ipynb --- 2-Working-With-Data/R/pandas.ipynb | 69 +++++++++++++----------------- 1 file changed, 30 insertions(+), 39 deletions(-) diff --git a/2-Working-With-Data/R/pandas.ipynb b/2-Working-With-Data/R/pandas.ipynb index 323f013..901a51c 100644 --- a/2-Working-With-Data/R/pandas.ipynb +++ b/2-Working-With-Data/R/pandas.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 73, - "id": "c105e391", + "id": "f356bf26", "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "00c41f19", + "id": "717d5825", "metadata": {}, "source": [ "## Series" @@ -21,7 +21,7 @@ }, { "cell_type": "markdown", - "id": "d3490356", + "id": "03ac7b71", "metadata": {}, "source": [ "a<- 1:9" @@ -30,7 +30,7 @@ { "cell_type": "code", "execution_count": 75, - "id": "2ef3725d", + "id": "f4149d4e", "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ { "cell_type": "code", "execution_count": 76, - "id": "14e6152d", + "id": "5633dd5e", "metadata": {}, "outputs": [], "source": [ @@ -51,7 +51,7 @@ { "cell_type": "code", "execution_count": 77, - "id": "432b0c2b", + "id": "834b824d", "metadata": {}, "outputs": [], "source": [ @@ -61,7 +61,7 @@ { "cell_type": "code", "execution_count": 78, - "id": "adb4391f", + "id": "f25cedc2", "metadata": {}, "outputs": [], "source": [ @@ -70,16 +70,7 @@ }, { "cell_type": "markdown", - "id": "9d27aa12", - "metadata": {}, - "source": [ - "idx<- seq.Date(from = as.Date(\"20-01-01\"), to = as.Date(\"2001-03-31\"), by = \"day\",)\n", - "print(length(idx))\n" - ] - }, - { - "cell_type": "markdown", - "id": "ee4be842", + "id": "aea16930", "metadata": {}, "source": [ "## DataFrame" @@ -88,7 +79,7 @@ { "cell_type": "code", "execution_count": 140, - "id": "a8585654", + "id": "0793b3b7", "metadata": {}, "outputs": [], "source": [ @@ -98,7 +89,7 @@ { "cell_type": "code", "execution_count": 141, - "id": "2b8c18b3", + "id": "bde43cd9", "metadata": {}, "outputs": [], "source": [ @@ -108,7 +99,7 @@ { "cell_type": "code", "execution_count": 109, - "id": "60b643d5", + "id": "5db4a116", "metadata": {}, "outputs": [ { @@ -127,7 +118,7 @@ { "cell_type": "code", "execution_count": 79, - "id": "113578b5", + "id": "e5e6313c", "metadata": {}, "outputs": [], "source": [ @@ -137,7 +128,7 @@ { "cell_type": "code", "execution_count": 80, - "id": "ef1dff63", + "id": "8358faeb", "metadata": {}, "outputs": [], "source": [ @@ -151,7 +142,7 @@ { "cell_type": "code", "execution_count": 81, - "id": "1d80bf43", + "id": "c439f0f3", "metadata": {}, "outputs": [ { @@ -234,7 +225,7 @@ { "cell_type": "code", "execution_count": 82, - "id": "831630d7", + "id": "26c75894", "metadata": {}, "outputs": [ { @@ -325,7 +316,7 @@ { "cell_type": "code", "execution_count": 83, - "id": "055bc484", + "id": "7f6885ba", "metadata": {}, "outputs": [ { @@ -388,7 +379,7 @@ { "cell_type": "code", "execution_count": 84, - "id": "05c2c45a", + "id": "0018c9fc", "metadata": {}, "outputs": [ { @@ -439,7 +430,7 @@ { "cell_type": "code", "execution_count": 85, - "id": "ac8a234f", + "id": "079a3b19", "metadata": {}, "outputs": [], "source": [ @@ -449,7 +440,7 @@ { "cell_type": "code", "execution_count": 86, - "id": "fb7ee6bd", + "id": "64dac80e", "metadata": {}, "outputs": [ { @@ -532,7 +523,7 @@ { "cell_type": "code", "execution_count": 87, - "id": "62e9c8dd", + "id": "c2fdab9c", "metadata": {}, "outputs": [], "source": [ @@ -542,7 +533,7 @@ { "cell_type": "code", "execution_count": 88, - "id": "849c33cc", + "id": "c523989b", "metadata": {}, "outputs": [ { @@ -625,7 +616,7 @@ { "cell_type": "code", "execution_count": 89, - "id": "0a65495d", + "id": "c095b403", "metadata": {}, "outputs": [ { @@ -692,7 +683,7 @@ { "cell_type": "code", "execution_count": 91, - "id": "ccebdda2", + "id": "f6f37d10", "metadata": {}, "outputs": [], "source": [ @@ -702,7 +693,7 @@ { "cell_type": "code", "execution_count": 93, - "id": "f2dcb719", + "id": "c1908f62", "metadata": {}, "outputs": [ { @@ -768,7 +759,7 @@ }, { "cell_type": "markdown", - "id": "d8eb00bc", + "id": "f927a466", "metadata": {}, "source": [ "## Printing and Plotting" @@ -777,7 +768,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b96b6be0", + "id": "6e20f11d", "metadata": {}, "outputs": [], "source": [ @@ -787,7 +778,7 @@ { "cell_type": "code", "execution_count": 94, - "id": "3b1a4735", + "id": "6bb554db", "metadata": {}, "outputs": [ { @@ -858,7 +849,7 @@ { "cell_type": "code", "execution_count": 151, - "id": "0af4e0e5", + "id": "14fa8a58", "metadata": {}, "outputs": [ { @@ -884,7 +875,7 @@ { "cell_type": "code", "execution_count": 96, - "id": "08103a39", + "id": "0db236b1", "metadata": {}, "outputs": [ { @@ -910,7 +901,7 @@ { "cell_type": "code", "execution_count": null, - "id": "768d4300", + "id": "37d719c4", "metadata": {}, "outputs": [], "source": [] From 8665bf9a86eebcacff72ad27c3cb814c92b5e4d9 Mon Sep 17 00:00:00 2001 From: Keshav Sharma Date: Sat, 9 Oct 2021 20:32:20 -0700 Subject: [PATCH 06/12] added --- 2-Working-With-Data/R/{pandas.ipynb => Data.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename 2-Working-With-Data/R/{pandas.ipynb => Data.ipynb} (100%) diff --git a/2-Working-With-Data/R/pandas.ipynb b/2-Working-With-Data/R/Data.ipynb similarity index 100% rename from 2-Working-With-Data/R/pandas.ipynb rename to 2-Working-With-Data/R/Data.ipynb From ab2ba5382cbeae62084ee6bfff1d6747f6a25fb6 Mon Sep 17 00:00:00 2001 From: Keshav Sharma Date: Sat, 9 Oct 2021 21:16:03 -0700 Subject: [PATCH 07/12] Delete Data.ipynb --- 2-Working-With-Data/R/Data.ipynb | 927 ------------------------------- 1 file changed, 927 deletions(-) delete mode 100644 2-Working-With-Data/R/Data.ipynb diff --git a/2-Working-With-Data/R/Data.ipynb b/2-Working-With-Data/R/Data.ipynb deleted file mode 100644 index 901a51c..0000000 --- a/2-Working-With-Data/R/Data.ipynb +++ /dev/null @@ -1,927 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 73, - "id": "f356bf26", - "metadata": {}, - "outputs": [], - "source": [ - "library(dplyr)\n", - "library(tidyverse)" - ] - }, - { - "cell_type": "markdown", - "id": "717d5825", - "metadata": {}, - "source": [ - "## Series" - ] - }, - { - "cell_type": "markdown", - "id": "03ac7b71", - "metadata": {}, - "source": [ - "a<- 1:9" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "f4149d4e", - "metadata": {}, - "outputs": [], - "source": [ - "b = c(\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\")" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "5633dd5e", - "metadata": {}, - "outputs": [], - "source": [ - "a1 = length(a)\n", - "b1 = length(b)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "834b824d", - "metadata": {}, - "outputs": [], - "source": [ - "a = data.frame(a,row.names = c(1:a1))" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "f25cedc2", - "metadata": {}, - "outputs": [], - "source": [ - "b = data.frame(b,row.names = c(1:b1))" - ] - }, - { - "cell_type": "markdown", - "id": "aea16930", - "metadata": {}, - "source": [ - "## DataFrame" - ] - }, - { - "cell_type": "code", - 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A data.frame: 9 × 4
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A tibble: 5 × 2
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\n" - ], - "text/latex": [ - "A tibble: 5 × 2\n", - "\\begin{tabular}{ll}\n", - " LenB & mymean\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t 1 & 1\\\\\n", - "\t 2 & 2\\\\\n", - "\t 3 & 3\\\\\n", - "\t 4 & 4\\\\\n", - "\t 6 & 6\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 5 × 2\n", - "\n", - "| LenB <int> | mymean <dbl> |\n", - "|---|---|\n", - "| 1 | 1 |\n", - "| 2 | 2 |\n", - "| 3 | 3 |\n", - "| 4 | 4 |\n", - "| 6 | 6 |\n", - "\n" - ], - "text/plain": [ - " LenB mymean\n", - "1 1 1 \n", - "2 2 2 \n", - "3 3 3 \n", - "4 4 4 \n", - "5 6 6 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "summarise(df1,mymean =mean(LenB))" - ] - }, - { - "cell_type": "markdown", - "id": "f927a466", - "metadata": {}, - "source": [ - "## Printing and Plotting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6e20f11d", - "metadata": {}, - "outputs": [], - "source": [ - "dataset = read.csv(\"file name\")" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "6bb554db", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 6 × 4
ABDivALenB
<int><chr><dbl><int>
11I -41
22like -34
33to -22
44use -13
55Python 06
66and 13
\n" - ], - "text/latex": [ - "A data.frame: 6 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & A & B & DivA & LenB\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t1 & 1 & I & -4 & 1\\\\\n", - "\t2 & 2 & like & -3 & 4\\\\\n", - "\t3 & 3 & to & -2 & 2\\\\\n", - "\t4 & 4 & use & -1 & 3\\\\\n", - "\t5 & 5 & Python & 0 & 6\\\\\n", - "\t6 & 6 & and & 1 & 3\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 6 × 4\n", - "\n", - "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", - "|---|---|---|---|---|\n", - "| 1 | 1 | I | -4 | 1 |\n", - "| 2 | 2 | like | -3 | 4 |\n", - "| 3 | 3 | to | -2 | 2 |\n", - "| 4 | 4 | use | -1 | 3 |\n", - "| 5 | 5 | Python | 0 | 6 |\n", - "| 6 | 6 | and | 1 | 3 |\n", - "\n" - ], - "text/plain": [ - " A B DivA LenB\n", - "1 1 I -4 1 \n", - "2 2 like -3 4 \n", - "3 3 to -2 2 \n", - "4 4 use -1 3 \n", - "5 5 Python 0 6 \n", - "6 6 and 1 3 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "id": "14fa8a58", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 420, - "width": 420 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "plot(df$A,type = 'o',xlab = \"no\",ylab = \"A\")" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "0db236b1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 420, - "width": 420 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "barplot(df$A, ylab = 'A',xlab = 'no')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37d719c4", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "4.1.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 4b71d144aee60cc8b25cec3dcf55dc432dc39dca Mon Sep 17 00:00:00 2001 From: Keshav Sharma Date: Sat, 9 Oct 2021 21:16:52 -0700 Subject: [PATCH 08/12] Create pandas.ipynb --- 2-Working-With-Data/R/pandas.ipynb | 978 +++++++++++++++++++++++++++++ 1 file changed, 978 insertions(+) create mode 100644 2-Working-With-Data/R/pandas.ipynb diff --git a/2-Working-With-Data/R/pandas.ipynb b/2-Working-With-Data/R/pandas.ipynb new file mode 100644 index 0000000..cb92883 --- /dev/null +++ b/2-Working-With-Data/R/pandas.ipynb @@ -0,0 +1,978 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "304296e3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Attaching package: 'dplyr'\n", + "\n", + "\n", + "The following objects are masked from 'package:stats':\n", + "\n", + " filter, lag\n", + "\n", + "\n", + "The following objects are masked from 'package:base':\n", + "\n", + " intersect, setdiff, setequal, union\n", + "\n", + "\n", + "-- \u001b[1mAttaching packages\u001b[22m ------------------------------------------------------------------------------- tidyverse 1.3.1 --\n", + "\n", + "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.5 \u001b[32mv\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.4\n", + "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.1.5 \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n", + "\u001b[32mv\u001b[39m \u001b[34mtidyr \u001b[39m 1.1.4 \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1\n", + "\u001b[32mv\u001b[39m \u001b[34mreadr \u001b[39m 2.0.2 \n", + "\n", + "-- \u001b[1mConflicts\u001b[22m ---------------------------------------------------------------------------------- tidyverse_conflicts() --\n", + "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n", + "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n", + "\n" + ] + } + ], + "source": [ + "library(dplyr)\n", + "library(tidyverse)" + ] + }, + { + "cell_type": "markdown", + "id": "d786e051", + "metadata": {}, + "source": [ + "## Series" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f659f553", + "metadata": {}, + "outputs": [], + "source": [ + "a<- 1:9" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9acc193d", + "metadata": {}, + "outputs": [], + "source": [ + "b = c(\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f577ec14", + "metadata": {}, + "outputs": [], + "source": [ + "a1 = length(a)\n", + "b1 = length(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "31e069a0", + "metadata": {}, + "outputs": [], + "source": [ + "a = data.frame(a,row.names = c(1:a1))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "29ce166e", + "metadata": {}, + "outputs": [], + "source": [ + "b = data.frame(b,row.names = c(1:b1))" + ] + }, + { + "cell_type": "markdown", + "id": "945feffd", + "metadata": {}, + "source": [ + "## DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "88a435ec", + "metadata": {}, + "outputs": [], + "source": [ + "a = data.frame(a,row.names = c(1:a1))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c4e2a6c1", + "metadata": {}, + "outputs": [], + "source": [ + "b = data.frame(b,row.names = c(1:b1))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2bb5177c", + "metadata": {}, + "outputs": [], + "source": [ + "df<- data.frame(a,b)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8f45d3a5", + "metadata": {}, + "outputs": [], + "source": [ + "df = \n", + " rename(df,\n", + " A = a,\n", + " B = b,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0efbf2d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 2
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A data.frame: 9 × 1
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A data.frame: 4 × 2
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A data.frame: 1 × 2
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A data.frame: 9 × 3
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A data.frame: 9 × 4
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A data.frame: 5 × 4
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A tibble: 5 × 2
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A data.frame: 6 × 4
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}, + { + "cell_type": "code", + "execution_count": 28, + "id": "515c95b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(df$A,type = 'o',xlab = \"no\",ylab = \"A\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "41b872c9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "barplot(df$A, ylab = 'A',xlab = 'no')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11001454", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "670db495", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.1.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c61b3810a40ed66ce26b373a23f49ffe2d6dbaa1 Mon Sep 17 00:00:00 2001 From: Keshav Sharma Date: Mon, 11 Oct 2021 13:59:27 -0700 Subject: [PATCH 09/12] Delete pandas.ipynb --- 2-Working-With-Data/R/pandas.ipynb | 978 ----------------------------- 1 file changed, 978 deletions(-) delete mode 100644 2-Working-With-Data/R/pandas.ipynb diff --git a/2-Working-With-Data/R/pandas.ipynb b/2-Working-With-Data/R/pandas.ipynb deleted file mode 100644 index cb92883..0000000 --- a/2-Working-With-Data/R/pandas.ipynb +++ /dev/null @@ -1,978 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "304296e3", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Attaching package: 'dplyr'\n", - "\n", - "\n", - "The following objects are masked from 'package:stats':\n", - "\n", - " filter, lag\n", - "\n", - "\n", - "The following objects are masked from 'package:base':\n", - "\n", - " intersect, setdiff, setequal, union\n", - "\n", - "\n", - "-- \u001b[1mAttaching packages\u001b[22m ------------------------------------------------------------------------------- tidyverse 1.3.1 --\n", - "\n", - "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.5 \u001b[32mv\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.4\n", - "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.1.5 \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n", - "\u001b[32mv\u001b[39m \u001b[34mtidyr \u001b[39m 1.1.4 \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1\n", - "\u001b[32mv\u001b[39m \u001b[34mreadr \u001b[39m 2.0.2 \n", - "\n", - "-- \u001b[1mConflicts\u001b[22m ---------------------------------------------------------------------------------- tidyverse_conflicts() --\n", - "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n", - "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n", - "\n" - ] - } - ], - "source": [ - "library(dplyr)\n", - "library(tidyverse)" - ] - }, - { - "cell_type": "markdown", - "id": "d786e051", - "metadata": {}, - "source": [ - "## Series" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f659f553", - "metadata": {}, - "outputs": [], - "source": [ - "a<- 1:9" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9acc193d", - "metadata": {}, - "outputs": [], - "source": [ - "b = c(\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f577ec14", - "metadata": {}, - "outputs": [], - "source": [ - "a1 = length(a)\n", - "b1 = length(b)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "31e069a0", - "metadata": {}, - "outputs": [], - "source": [ - "a = data.frame(a,row.names = c(1:a1))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "29ce166e", - "metadata": {}, - "outputs": [], - "source": [ - "b = data.frame(b,row.names = c(1:b1))" - ] - }, - { - "cell_type": "markdown", - "id": "945feffd", - "metadata": {}, - "source": [ - "## DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "88a435ec", - "metadata": {}, - "outputs": [], - "source": [ - "a = data.frame(a,row.names = c(1:a1))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c4e2a6c1", - "metadata": {}, - "outputs": [], - "source": [ - "b = data.frame(b,row.names = c(1:b1))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "2bb5177c", - "metadata": {}, - "outputs": [], - "source": [ - "df<- data.frame(a,b)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "8f45d3a5", - "metadata": {}, - "outputs": [], - "source": [ - "df = \n", - " rename(df,\n", - " A = a,\n", - " B = b,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "0efbf2d4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 9 × 2
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A data.frame: 9 × 1
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A data.frame: 4 × 2
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A data.frame: 1 × 2
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A data.frame: 9 × 3
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A data.frame: 9 × 4
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A data.frame: 5 × 4
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A tibble: 5 × 2
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A data.frame: 6 × 4
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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 420, - "width": 420 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "plot(df$A,type = 'o',xlab = \"no\",ylab = \"A\")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "41b872c9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 420, - "width": 420 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "barplot(df$A, ylab = 'A',xlab = 'no')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11001454", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "670db495", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "4.1.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From ce53eb68b59c5609395e4b2bb00da85e8ffbaa70 Mon Sep 17 00:00:00 2001 From: Keshav Sharma Date: Mon, 11 Oct 2021 14:00:20 -0700 Subject: [PATCH 10/12] Create Notebook.ipynb --- 2-Working-With-Data/R/Notebook.ipynb | 2249 ++++++++++++++++++++++++++ 1 file changed, 2249 insertions(+) create mode 100644 2-Working-With-Data/R/Notebook.ipynb diff --git a/2-Working-With-Data/R/Notebook.ipynb b/2-Working-With-Data/R/Notebook.ipynb new file mode 100644 index 0000000..eec4910 --- /dev/null +++ b/2-Working-With-Data/R/Notebook.ipynb @@ -0,0 +1,2249 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "304296e3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Attaching package: 'dplyr'\n", + "\n", + "\n", + "The following objects are masked from 'package:stats':\n", + "\n", + " filter, lag\n", + "\n", + "\n", + "The following objects are masked from 'package:base':\n", + "\n", + " intersect, setdiff, setequal, union\n", + "\n", + "\n", + "-- \u001b[1mAttaching packages\u001b[22m ------------------------------------------------------------------------------- tidyverse 1.3.1 --\n", + "\n", + "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.5 \u001b[32mv\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.4\n", + "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.1.5 \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n", + "\u001b[32mv\u001b[39m \u001b[34mtidyr \u001b[39m 1.1.4 \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1\n", + "\u001b[32mv\u001b[39m \u001b[34mreadr \u001b[39m 2.0.2 \n", + "\n", + "-- \u001b[1mConflicts\u001b[22m ---------------------------------------------------------------------------------- tidyverse_conflicts() --\n", + "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n", + "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n", + "\n", + "\n", + "Attaching package: 'lubridate'\n", + "\n", + "\n", + "The following objects are masked from 'package:base':\n", + "\n", + " date, intersect, setdiff, union\n", + "\n", + "\n", + "\n", + "Attaching package: 'zoo'\n", + "\n", + "\n", + "The following objects are masked from 'package:base':\n", + "\n", + " as.Date, as.Date.numeric\n", + "\n", + "\n", + "\n", + "Attaching package: 'xts'\n", + "\n", + "\n", + "The following objects are masked from 'package:dplyr':\n", + "\n", + " first, last\n", + "\n", + "\n" + ] + } + ], + "source": [ + "library(dplyr)\n", + "library(tidyverse)\n", + "library('lubridate')\n", + "library('zoo')\n", + "library('xts')" + ] + }, + { + "cell_type": "markdown", + "id": "d786e051", + "metadata": {}, + "source": [ + "## Series" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f659f553", + "metadata": {}, + "outputs": [], + "source": [ + "a<- 1:9" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9acc193d", + "metadata": {}, + "outputs": [], + "source": [ + "b = c(\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f577ec14", + "metadata": {}, + "outputs": [], + "source": [ + "a1 = length(a)\n", + "b1 = length(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "31e069a0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " a\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + "5 5\n", + "6 6\n", + "7 7\n", + "8 8\n", + "9 9\n" + ] + } + ], + "source": [ + "a = data.frame(a,row.names = c(1:a1))\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "29ce166e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " b\n", + "1 I\n", + "2 like\n", + "3 to\n", + "4 use\n", + "5 Python\n", + "6 and\n", + "7 Pandas\n", + "8 very\n", + "9 much\n" + ] + } + ], + "source": [ + "b = data.frame(b,row.names = c(1:b1))\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "eeb683c7", + "metadata": {}, + "outputs": [], + "source": [ + "library('ggplot2')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "e7788ca1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"length of index is 366\"\n" + ] + }, + { + "data": { + "image/png": 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zlzVDpfPeddHDpC1yxxAA+6AW10+koPAbS1c1tAOyC7PKD7d3G0AtoEWz4S0JUZ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\n6KPkVWwT13prruHWSe+7aOB0MAH6fayaJllPNTb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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "start_date <- mdy(\"Jan 1, 2020\")\n", + "end_date <- mdy(\"Dec 31, 2020\")\n", + "idx = seq(start_date,end_date,by ='day')\n", + "print(paste(\"length of index is \",length(idx)))\n", + "size = length(idx)\n", + "sales = runif(366,min=25,max=50)\n", + "sold_items <- data.frame(row.names=idx[0:size],sales)\n", + "ggplot(sold_items,aes(x=idx,y=sales)) + geom_point(color = \"firebrick\", shape = \"diamond\", size = 2) +\n", + " geom_line(color = \"firebrick\", size = .3)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "30747f7c", + "metadata": {}, + "outputs": [], + "source": [ + "library(repr)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "48f3e762", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "366" + ], + "text/latex": [ + "366" + ], + "text/markdown": [ + "366" + ], + "text/plain": [ + "[1] 366" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#changing plot size\n", + "options(repr.plot.width = 12,repr.plot.height=6)\n", + "size" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "abe41544", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 53 × 1
additional_product
<dbl>
2020-01-0110
2020-01-0810
2020-01-1510
2020-01-2210
2020-01-2910
2020-02-0510
2020-02-1210
2020-02-1910
2020-02-2610
2020-03-0410
2020-03-1110
2020-03-1810
2020-03-2510
2020-04-0110
2020-04-0810
2020-04-1510
2020-04-2210
2020-04-2910
2020-05-0610
2020-05-1310
2020-05-2010
2020-05-2710
2020-06-0310
2020-06-1010
2020-06-1710
2020-06-2410
2020-07-0110
2020-07-0810
2020-07-1510
2020-07-2210
2020-07-2910
2020-08-0510
2020-08-1210
2020-08-1910
2020-08-2610
2020-09-0210
2020-09-0910
2020-09-1610
2020-09-2310
2020-09-3010
2020-10-0710
2020-10-1410
2020-10-2110
2020-10-2810
2020-11-0410
2020-11-1110
2020-11-1810
2020-11-2510
2020-12-0210
2020-12-0910
2020-12-1610
2020-12-2310
2020-12-3010
\n" + ], + "text/latex": [ + "A data.frame: 53 × 1\n", + "\\begin{tabular}{r|l}\n", + " & additional\\_product\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t2020-01-01 & 10\\\\\n", + "\t2020-01-08 & 10\\\\\n", + "\t2020-01-15 & 10\\\\\n", + "\t2020-01-22 & 10\\\\\n", + "\t2020-01-29 & 10\\\\\n", + "\t2020-02-05 & 10\\\\\n", + "\t2020-02-12 & 10\\\\\n", + "\t2020-02-19 & 10\\\\\n", + "\t2020-02-26 & 10\\\\\n", + "\t2020-03-04 & 10\\\\\n", + "\t2020-03-11 & 10\\\\\n", + "\t2020-03-18 & 10\\\\\n", + "\t2020-03-25 & 10\\\\\n", + "\t2020-04-01 & 10\\\\\n", + "\t2020-04-08 & 10\\\\\n", + "\t2020-04-15 & 10\\\\\n", + "\t2020-04-22 & 10\\\\\n", + "\t2020-04-29 & 10\\\\\n", + "\t2020-05-06 & 10\\\\\n", + "\t2020-05-13 & 10\\\\\n", + "\t2020-05-20 & 10\\\\\n", + "\t2020-05-27 & 10\\\\\n", + "\t2020-06-03 & 10\\\\\n", + "\t2020-06-10 & 10\\\\\n", + "\t2020-06-17 & 10\\\\\n", + "\t2020-06-24 & 10\\\\\n", + "\t2020-07-01 & 10\\\\\n", + "\t2020-07-08 & 10\\\\\n", + "\t2020-07-15 & 10\\\\\n", + "\t2020-07-22 & 10\\\\\n", + "\t2020-07-29 & 10\\\\\n", + "\t2020-08-05 & 10\\\\\n", + "\t2020-08-12 & 10\\\\\n", + "\t2020-08-19 & 10\\\\\n", + "\t2020-08-26 & 10\\\\\n", + "\t2020-09-02 & 10\\\\\n", + "\t2020-09-09 & 10\\\\\n", + "\t2020-09-16 & 10\\\\\n", + "\t2020-09-23 & 10\\\\\n", + "\t2020-09-30 & 10\\\\\n", + "\t2020-10-07 & 10\\\\\n", + "\t2020-10-14 & 10\\\\\n", + "\t2020-10-21 & 10\\\\\n", + "\t2020-10-28 & 10\\\\\n", + "\t2020-11-04 & 10\\\\\n", + "\t2020-11-11 & 10\\\\\n", + "\t2020-11-18 & 10\\\\\n", + "\t2020-11-25 & 10\\\\\n", + "\t2020-12-02 & 10\\\\\n", + "\t2020-12-09 & 10\\\\\n", + "\t2020-12-16 & 10\\\\\n", + "\t2020-12-23 & 10\\\\\n", + "\t2020-12-30 & 10\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 53 × 1\n", + "\n", + "| | additional_product <dbl> |\n", + "|---|---|\n", + "| 2020-01-01 | 10 |\n", + "| 2020-01-08 | 10 |\n", + "| 2020-01-15 | 10 |\n", + "| 2020-01-22 | 10 |\n", + "| 2020-01-29 | 10 |\n", + "| 2020-02-05 | 10 |\n", + "| 2020-02-12 | 10 |\n", + "| 2020-02-19 | 10 |\n", + "| 2020-02-26 | 10 |\n", + "| 2020-03-04 | 10 |\n", + "| 2020-03-11 | 10 |\n", + "| 2020-03-18 | 10 |\n", + "| 2020-03-25 | 10 |\n", + "| 2020-04-01 | 10 |\n", + "| 2020-04-08 | 10 |\n", + "| 2020-04-15 | 10 |\n", + "| 2020-04-22 | 10 |\n", + "| 2020-04-29 | 10 |\n", + "| 2020-05-06 | 10 |\n", + "| 2020-05-13 | 10 |\n", + "| 2020-05-20 | 10 |\n", + "| 2020-05-27 | 10 |\n", + "| 2020-06-03 | 10 |\n", + "| 2020-06-10 | 10 |\n", + "| 2020-06-17 | 10 |\n", + "| 2020-06-24 | 10 |\n", + "| 2020-07-01 | 10 |\n", + "| 2020-07-08 | 10 |\n", + "| 2020-07-15 | 10 |\n", + "| 2020-07-22 | 10 |\n", + "| 2020-07-29 | 10 |\n", + "| 2020-08-05 | 10 |\n", + "| 2020-08-12 | 10 |\n", + "| 2020-08-19 | 10 |\n", + "| 2020-08-26 | 10 |\n", + "| 2020-09-02 | 10 |\n", + "| 2020-09-09 | 10 |\n", + "| 2020-09-16 | 10 |\n", + "| 2020-09-23 | 10 |\n", + "| 2020-09-30 | 10 |\n", + "| 2020-10-07 | 10 |\n", + "| 2020-10-14 | 10 |\n", + "| 2020-10-21 | 10 |\n", + "| 2020-10-28 | 10 |\n", + "| 2020-11-04 | 10 |\n", + "| 2020-11-11 | 10 |\n", + "| 2020-11-18 | 10 |\n", + "| 2020-11-25 | 10 |\n", + "| 2020-12-02 | 10 |\n", + "| 2020-12-09 | 10 |\n", + "| 2020-12-16 | 10 |\n", + "| 2020-12-23 | 10 |\n", + "| 2020-12-30 | 10 |\n", + "\n" + ], + "text/plain": [ + " additional_product\n", + "2020-01-01 10 \n", + "2020-01-08 10 \n", + "2020-01-15 10 \n", + "2020-01-22 10 \n", + "2020-01-29 10 \n", + "2020-02-05 10 \n", + "2020-02-12 10 \n", + "2020-02-19 10 \n", + "2020-02-26 10 \n", + "2020-03-04 10 \n", + "2020-03-11 10 \n", + "2020-03-18 10 \n", + "2020-03-25 10 \n", + "2020-04-01 10 \n", + "2020-04-08 10 \n", + "2020-04-15 10 \n", + "2020-04-22 10 \n", + "2020-04-29 10 \n", + "2020-05-06 10 \n", + "2020-05-13 10 \n", + "2020-05-20 10 \n", + "2020-05-27 10 \n", + "2020-06-03 10 \n", + "2020-06-10 10 \n", + "2020-06-17 10 \n", + "2020-06-24 10 \n", + "2020-07-01 10 \n", + "2020-07-08 10 \n", + "2020-07-15 10 \n", + "2020-07-22 10 \n", + "2020-07-29 10 \n", + "2020-08-05 10 \n", + "2020-08-12 10 \n", + "2020-08-19 10 \n", + "2020-08-26 10 \n", + "2020-09-02 10 \n", + "2020-09-09 10 \n", + "2020-09-16 10 \n", + "2020-09-23 10 \n", + "2020-09-30 10 \n", + "2020-10-07 10 \n", + "2020-10-14 10 \n", + "2020-10-21 10 \n", + "2020-10-28 10 \n", + "2020-11-04 10 \n", + "2020-11-11 10 \n", + "2020-11-18 10 \n", + "2020-11-25 10 \n", + "2020-12-02 10 \n", + "2020-12-09 10 \n", + "2020-12-16 10 \n", + "2020-12-23 10 \n", + "2020-12-30 10 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + 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A data.frame: 366 × 1
total
<dbl>
2020-01-0154.37099
2020-01-02 NA
2020-01-03 NA
2020-01-04 NA
2020-01-05 NA
2020-01-06 NA
2020-01-07 NA
2020-01-0851.99181
2020-01-09 NA
2020-01-10 NA
2020-01-11 NA
2020-01-12 NA
2020-01-13 NA
2020-01-14 NA
2020-01-1547.57204
2020-01-16 NA
2020-01-17 NA
2020-01-18 NA
2020-01-19 NA
2020-01-20 NA
2020-01-21 NA
2020-01-2250.46082
2020-01-23 NA
2020-01-24 NA
2020-01-25 NA
2020-01-26 NA
2020-01-27 NA
2020-01-28 NA
2020-01-2955.32913
2020-01-30 NA
......
2020-12-0247.63211
2020-12-03 NA
2020-12-04 NA
2020-12-05 NA
2020-12-06 NA
2020-12-07 NA
2020-12-08 NA
2020-12-0949.09786
2020-12-10 NA
2020-12-11 NA
2020-12-12 NA
2020-12-13 NA
2020-12-14 NA
2020-12-15 NA
2020-12-1655.27396
2020-12-17 NA
2020-12-18 NA
2020-12-19 NA
2020-12-20 NA
2020-12-21 NA
2020-12-22 NA
2020-12-2346.30954
2020-12-24 NA
2020-12-25 NA
2020-12-26 NA
2020-12-27 NA
2020-12-28 NA
2020-12-29 NA
2020-12-3043.08600
2020-12-31 NA
\n" + ], + "text/latex": [ + "A data.frame: 366 × 1\n", + "\\begin{tabular}{r|l}\n", + " & total\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t2020-01-01 & 54.37099\\\\\n", + "\t2020-01-02 & NA\\\\\n", + "\t2020-01-03 & NA\\\\\n", + "\t2020-01-04 & NA\\\\\n", + "\t2020-01-05 & NA\\\\\n", + "\t2020-01-06 & NA\\\\\n", + "\t2020-01-07 & NA\\\\\n", + "\t2020-01-08 & 51.99181\\\\\n", + "\t2020-01-09 & NA\\\\\n", + "\t2020-01-10 & NA\\\\\n", + "\t2020-01-11 & NA\\\\\n", + "\t2020-01-12 & NA\\\\\n", + "\t2020-01-13 & NA\\\\\n", + "\t2020-01-14 & NA\\\\\n", + "\t2020-01-15 & 47.57204\\\\\n", + "\t2020-01-16 & NA\\\\\n", + "\t2020-01-17 & NA\\\\\n", + "\t2020-01-18 & NA\\\\\n", + "\t2020-01-19 & NA\\\\\n", + "\t2020-01-20 & NA\\\\\n", + "\t2020-01-21 & NA\\\\\n", + "\t2020-01-22 & 50.46082\\\\\n", + "\t2020-01-23 & NA\\\\\n", + "\t2020-01-24 & NA\\\\\n", + "\t2020-01-25 & NA\\\\\n", + "\t2020-01-26 & NA\\\\\n", + "\t2020-01-27 & NA\\\\\n", + "\t2020-01-28 & NA\\\\\n", + "\t2020-01-29 & 55.32913\\\\\n", + "\t2020-01-30 & NA\\\\\n", + "\t... & ...\\\\\n", + "\t2020-12-02 & 47.63211\\\\\n", + "\t2020-12-03 & NA\\\\\n", + "\t2020-12-04 & NA\\\\\n", + "\t2020-12-05 & NA\\\\\n", + "\t2020-12-06 & NA\\\\\n", + "\t2020-12-07 & NA\\\\\n", + "\t2020-12-08 & NA\\\\\n", + "\t2020-12-09 & 49.09786\\\\\n", + "\t2020-12-10 & NA\\\\\n", + "\t2020-12-11 & NA\\\\\n", + "\t2020-12-12 & NA\\\\\n", + "\t2020-12-13 & NA\\\\\n", + "\t2020-12-14 & NA\\\\\n", + "\t2020-12-15 & NA\\\\\n", + "\t2020-12-16 & 55.27396\\\\\n", + "\t2020-12-17 & NA\\\\\n", + "\t2020-12-18 & NA\\\\\n", + "\t2020-12-19 & NA\\\\\n", + "\t2020-12-20 & NA\\\\\n", + "\t2020-12-21 & NA\\\\\n", + "\t2020-12-22 & NA\\\\\n", + "\t2020-12-23 & 46.30954\\\\\n", + "\t2020-12-24 & NA\\\\\n", + "\t2020-12-25 & NA\\\\\n", + "\t2020-12-26 & NA\\\\\n", + "\t2020-12-27 & NA\\\\\n", + "\t2020-12-28 & NA\\\\\n", + "\t2020-12-29 & NA\\\\\n", + "\t2020-12-30 & 43.08600\\\\\n", + "\t2020-12-31 & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 366 × 1\n", + "\n", + "| | total <dbl> |\n", + "|---|---|\n", + "| 2020-01-01 | 54.37099 |\n", + "| 2020-01-02 | NA |\n", + "| 2020-01-03 | NA |\n", + "| 2020-01-04 | NA |\n", + "| 2020-01-05 | NA |\n", + "| 2020-01-06 | NA |\n", + "| 2020-01-07 | NA |\n", + "| 2020-01-08 | 51.99181 |\n", + "| 2020-01-09 | NA |\n", + "| 2020-01-10 | NA |\n", + "| 2020-01-11 | NA |\n", + "| 2020-01-12 | NA |\n", + "| 2020-01-13 | NA |\n", + "| 2020-01-14 | NA |\n", + "| 2020-01-15 | 47.57204 |\n", + "| 2020-01-16 | NA |\n", + "| 2020-01-17 | NA |\n", + "| 2020-01-18 | NA |\n", + "| 2020-01-19 | NA |\n", + "| 2020-01-20 | NA |\n", + "| 2020-01-21 | NA |\n", + "| 2020-01-22 | 50.46082 |\n", + "| 2020-01-23 | NA |\n", + "| 2020-01-24 | NA |\n", + "| 2020-01-25 | NA |\n", + "| 2020-01-26 | NA |\n", + "| 2020-01-27 | NA |\n", + "| 2020-01-28 | NA |\n", + "| 2020-01-29 | 55.32913 |\n", + "| 2020-01-30 | NA |\n", + "| ... | ... |\n", + "| 2020-12-02 | 47.63211 |\n", + "| 2020-12-03 | NA |\n", + "| 2020-12-04 | NA |\n", + "| 2020-12-05 | NA |\n", + "| 2020-12-06 | NA |\n", + "| 2020-12-07 | NA |\n", + "| 2020-12-08 | NA |\n", + "| 2020-12-09 | 49.09786 |\n", + "| 2020-12-10 | NA |\n", + "| 2020-12-11 | NA |\n", + "| 2020-12-12 | NA |\n", + "| 2020-12-13 | NA |\n", + "| 2020-12-14 | NA |\n", + "| 2020-12-15 | NA |\n", + "| 2020-12-16 | 55.27396 |\n", + "| 2020-12-17 | NA |\n", + "| 2020-12-18 | NA |\n", + "| 2020-12-19 | NA |\n", + "| 2020-12-20 | NA |\n", + "| 2020-12-21 | NA |\n", + "| 2020-12-22 | NA |\n", + "| 2020-12-23 | 46.30954 |\n", + "| 2020-12-24 | NA |\n", + "| 2020-12-25 | NA |\n", + "| 2020-12-26 | NA |\n", + "| 2020-12-27 | NA |\n", + "| 2020-12-28 | NA |\n", + "| 2020-12-29 | NA |\n", + "| 2020-12-30 | 43.08600 |\n", + "| 2020-12-31 | NA |\n", + "\n" + ], + "text/plain": [ + " total \n", + "2020-01-01 54.37099\n", + "2020-01-02 NA\n", + "2020-01-03 NA\n", + "2020-01-04 NA\n", + "2020-01-05 NA\n", + "2020-01-06 NA\n", + "2020-01-07 NA\n", + "2020-01-08 51.99181\n", + "2020-01-09 NA\n", + "2020-01-10 NA\n", + "2020-01-11 NA\n", + "2020-01-12 NA\n", + "2020-01-13 NA\n", + "2020-01-14 NA\n", + "2020-01-15 47.57204\n", + "2020-01-16 NA\n", + "2020-01-17 NA\n", + "2020-01-18 NA\n", + "2020-01-19 NA\n", + "2020-01-20 NA\n", + "2020-01-21 NA\n", + "2020-01-22 50.46082\n", + "2020-01-23 NA\n", + "2020-01-24 NA\n", + "2020-01-25 NA\n", + "2020-01-26 NA\n", + "2020-01-27 NA\n", + "2020-01-28 NA\n", + "2020-01-29 55.32913\n", + "2020-01-30 NA\n", + "... ... \n", + "2020-12-02 47.63211\n", + "2020-12-03 NA\n", + "2020-12-04 NA\n", + "2020-12-05 NA\n", + "2020-12-06 NA\n", + "2020-12-07 NA\n", + "2020-12-08 NA\n", + "2020-12-09 49.09786\n", + "2020-12-10 NA\n", + "2020-12-11 NA\n", + "2020-12-12 NA\n", + "2020-12-13 NA\n", + "2020-12-14 NA\n", + "2020-12-15 NA\n", + "2020-12-16 55.27396\n", + "2020-12-17 NA\n", + "2020-12-18 NA\n", + "2020-12-19 NA\n", + "2020-12-20 NA\n", + "2020-12-21 NA\n", + "2020-12-22 NA\n", + "2020-12-23 46.30954\n", + "2020-12-24 NA\n", + "2020-12-25 NA\n", + "2020-12-26 NA\n", + "2020-12-27 NA\n", + "2020-12-28 NA\n", + "2020-12-29 NA\n", + "2020-12-30 43.08600\n", + "2020-12-31 NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "index = seq(start_date,end_date,by = 'week')\n", + "sz = length(index)\n", + "additional_product <- rep(10,53)\n", + "additional_items <- data.frame(row.names = index[0:sz],additional_product)\n", + "additional_items\n", + "# we are merging two dataframe so that we can add\n", + "additional_item = merge(additional_items,sold_items, by = 0, all = TRUE)[-1] \n", + "total = data.frame(row.names=idx[0:size],additional_item$additional_product + additional_item$sales)\n", + "colnames(total) = c('total')\n", + "total" + ] + }, + { + "cell_type": "markdown", + "id": "cb00ff6e", + "metadata": {}, + "source": [ + "\n", + "### As you can see, we are having problems here, because in the weekly series non-mentioned days are considered to be missing (NaN), if we add NaN to a number gives us NaN. In order todo addition, we need to fill the value 0 of additional_items while adding series: \n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "387cb4c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 366 × 1
total
<dbl>
2020-01-0154.37099
2020-01-0227.85566
2020-01-0338.29037
2020-01-0426.72367
2020-01-0537.93330
2020-01-0638.70961
2020-01-0728.36133
2020-01-0851.99181
2020-01-0944.76469
2020-01-1026.54058
2020-01-1131.70327
2020-01-1237.83133
2020-01-1347.45717
2020-01-1431.78909
2020-01-1547.57204
2020-01-1643.79414
2020-01-1746.35117
2020-01-1840.18666
2020-01-1934.65751
2020-01-2038.09664
2020-01-2136.76733
2020-01-2250.46082
2020-01-2339.62032
2020-01-2444.56143
2020-01-2526.45657
2020-01-2645.48089
2020-01-2749.03699
2020-01-2846.05290
2020-01-2955.32913
2020-01-3044.83293
......
2020-12-0247.63211
2020-12-0349.44946
2020-12-0428.07843
2020-12-0549.80941
2020-12-0637.43425
2020-12-0732.36442
2020-12-0837.96258
2020-12-0949.09786
2020-12-1043.08738
2020-12-1136.93554
2020-12-1233.44147
2020-12-1335.24588
2020-12-1447.67855
2020-12-1541.84728
2020-12-1655.27396
2020-12-1732.23195
2020-12-1830.18391
2020-12-1939.27033
2020-12-2045.13756
2020-12-2143.00961
2020-12-2243.69411
2020-12-2346.30954
2020-12-2437.42397
2020-12-2535.94406
2020-12-2645.00482
2020-12-2731.81550
2020-12-2832.69022
2020-12-2931.52063
2020-12-3043.08600
2020-12-3139.28333
\n" + ], + "text/latex": [ + "A data.frame: 366 × 1\n", + "\\begin{tabular}{r|l}\n", + " & total\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t2020-01-01 & 54.37099\\\\\n", + "\t2020-01-02 & 27.85566\\\\\n", + "\t2020-01-03 & 38.29037\\\\\n", + "\t2020-01-04 & 26.72367\\\\\n", + "\t2020-01-05 & 37.93330\\\\\n", + "\t2020-01-06 & 38.70961\\\\\n", + "\t2020-01-07 & 28.36133\\\\\n", + "\t2020-01-08 & 51.99181\\\\\n", + "\t2020-01-09 & 44.76469\\\\\n", + "\t2020-01-10 & 26.54058\\\\\n", + "\t2020-01-11 & 31.70327\\\\\n", + "\t2020-01-12 & 37.83133\\\\\n", + "\t2020-01-13 & 47.45717\\\\\n", + "\t2020-01-14 & 31.78909\\\\\n", + "\t2020-01-15 & 47.57204\\\\\n", + "\t2020-01-16 & 43.79414\\\\\n", + "\t2020-01-17 & 46.35117\\\\\n", + "\t2020-01-18 & 40.18666\\\\\n", + "\t2020-01-19 & 34.65751\\\\\n", + "\t2020-01-20 & 38.09664\\\\\n", + "\t2020-01-21 & 36.76733\\\\\n", + "\t2020-01-22 & 50.46082\\\\\n", + "\t2020-01-23 & 39.62032\\\\\n", + "\t2020-01-24 & 44.56143\\\\\n", + "\t2020-01-25 & 26.45657\\\\\n", + "\t2020-01-26 & 45.48089\\\\\n", + "\t2020-01-27 & 49.03699\\\\\n", + "\t2020-01-28 & 46.05290\\\\\n", + "\t2020-01-29 & 55.32913\\\\\n", + "\t2020-01-30 & 44.83293\\\\\n", + "\t... & ...\\\\\n", + "\t2020-12-02 & 47.63211\\\\\n", + "\t2020-12-03 & 49.44946\\\\\n", + "\t2020-12-04 & 28.07843\\\\\n", + "\t2020-12-05 & 49.80941\\\\\n", + "\t2020-12-06 & 37.43425\\\\\n", + "\t2020-12-07 & 32.36442\\\\\n", + "\t2020-12-08 & 37.96258\\\\\n", + "\t2020-12-09 & 49.09786\\\\\n", + "\t2020-12-10 & 43.08738\\\\\n", + "\t2020-12-11 & 36.93554\\\\\n", + "\t2020-12-12 & 33.44147\\\\\n", + "\t2020-12-13 & 35.24588\\\\\n", + "\t2020-12-14 & 47.67855\\\\\n", + "\t2020-12-15 & 41.84728\\\\\n", + "\t2020-12-16 & 55.27396\\\\\n", + "\t2020-12-17 & 32.23195\\\\\n", + "\t2020-12-18 & 30.18391\\\\\n", + "\t2020-12-19 & 39.27033\\\\\n", + "\t2020-12-20 & 45.13756\\\\\n", + "\t2020-12-21 & 43.00961\\\\\n", + "\t2020-12-22 & 43.69411\\\\\n", + "\t2020-12-23 & 46.30954\\\\\n", + "\t2020-12-24 & 37.42397\\\\\n", + "\t2020-12-25 & 35.94406\\\\\n", + "\t2020-12-26 & 45.00482\\\\\n", + "\t2020-12-27 & 31.81550\\\\\n", + "\t2020-12-28 & 32.69022\\\\\n", + "\t2020-12-29 & 31.52063\\\\\n", + "\t2020-12-30 & 43.08600\\\\\n", + "\t2020-12-31 & 39.28333\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 366 × 1\n", + "\n", + "| | total <dbl> |\n", + "|---|---|\n", + "| 2020-01-01 | 54.37099 |\n", + "| 2020-01-02 | 27.85566 |\n", + "| 2020-01-03 | 38.29037 |\n", + "| 2020-01-04 | 26.72367 |\n", + "| 2020-01-05 | 37.93330 |\n", + "| 2020-01-06 | 38.70961 |\n", + "| 2020-01-07 | 28.36133 |\n", + "| 2020-01-08 | 51.99181 |\n", + "| 2020-01-09 | 44.76469 |\n", + "| 2020-01-10 | 26.54058 |\n", + "| 2020-01-11 | 31.70327 |\n", + "| 2020-01-12 | 37.83133 |\n", + "| 2020-01-13 | 47.45717 |\n", + "| 2020-01-14 | 31.78909 |\n", + "| 2020-01-15 | 47.57204 |\n", + "| 2020-01-16 | 43.79414 |\n", + "| 2020-01-17 | 46.35117 |\n", + "| 2020-01-18 | 40.18666 |\n", + "| 2020-01-19 | 34.65751 |\n", + "| 2020-01-20 | 38.09664 |\n", + "| 2020-01-21 | 36.76733 |\n", + "| 2020-01-22 | 50.46082 |\n", + "| 2020-01-23 | 39.62032 |\n", + "| 2020-01-24 | 44.56143 |\n", + "| 2020-01-25 | 26.45657 |\n", + "| 2020-01-26 | 45.48089 |\n", + "| 2020-01-27 | 49.03699 |\n", + "| 2020-01-28 | 46.05290 |\n", + "| 2020-01-29 | 55.32913 |\n", + "| 2020-01-30 | 44.83293 |\n", + "| ... | ... |\n", + "| 2020-12-02 | 47.63211 |\n", + "| 2020-12-03 | 49.44946 |\n", + "| 2020-12-04 | 28.07843 |\n", + "| 2020-12-05 | 49.80941 |\n", + "| 2020-12-06 | 37.43425 |\n", + "| 2020-12-07 | 32.36442 |\n", + "| 2020-12-08 | 37.96258 |\n", + "| 2020-12-09 | 49.09786 |\n", + "| 2020-12-10 | 43.08738 |\n", + "| 2020-12-11 | 36.93554 |\n", + "| 2020-12-12 | 33.44147 |\n", + "| 2020-12-13 | 35.24588 |\n", + "| 2020-12-14 | 47.67855 |\n", + "| 2020-12-15 | 41.84728 |\n", + "| 2020-12-16 | 55.27396 |\n", + "| 2020-12-17 | 32.23195 |\n", + "| 2020-12-18 | 30.18391 |\n", + "| 2020-12-19 | 39.27033 |\n", + "| 2020-12-20 | 45.13756 |\n", + "| 2020-12-21 | 43.00961 |\n", + "| 2020-12-22 | 43.69411 |\n", + "| 2020-12-23 | 46.30954 |\n", + "| 2020-12-24 | 37.42397 |\n", + "| 2020-12-25 | 35.94406 |\n", + "| 2020-12-26 | 45.00482 |\n", + "| 2020-12-27 | 31.81550 |\n", + "| 2020-12-28 | 32.69022 |\n", + "| 2020-12-29 | 31.52063 |\n", + "| 2020-12-30 | 43.08600 |\n", + "| 2020-12-31 | 39.28333 |\n", + "\n" + ], + "text/plain": [ + " total \n", + "2020-01-01 54.37099\n", + "2020-01-02 27.85566\n", + "2020-01-03 38.29037\n", + "2020-01-04 26.72367\n", + "2020-01-05 37.93330\n", + "2020-01-06 38.70961\n", + "2020-01-07 28.36133\n", + "2020-01-08 51.99181\n", + "2020-01-09 44.76469\n", + "2020-01-10 26.54058\n", + "2020-01-11 31.70327\n", + "2020-01-12 37.83133\n", + "2020-01-13 47.45717\n", + "2020-01-14 31.78909\n", + "2020-01-15 47.57204\n", + "2020-01-16 43.79414\n", + "2020-01-17 46.35117\n", + "2020-01-18 40.18666\n", + "2020-01-19 34.65751\n", + "2020-01-20 38.09664\n", + "2020-01-21 36.76733\n", + "2020-01-22 50.46082\n", + "2020-01-23 39.62032\n", + "2020-01-24 44.56143\n", + "2020-01-25 26.45657\n", + "2020-01-26 45.48089\n", + "2020-01-27 49.03699\n", + "2020-01-28 46.05290\n", + "2020-01-29 55.32913\n", + "2020-01-30 44.83293\n", + "... ... \n", + "2020-12-02 47.63211\n", + "2020-12-03 49.44946\n", + "2020-12-04 28.07843\n", + "2020-12-05 49.80941\n", + "2020-12-06 37.43425\n", + "2020-12-07 32.36442\n", + "2020-12-08 37.96258\n", + "2020-12-09 49.09786\n", + "2020-12-10 43.08738\n", + "2020-12-11 36.93554\n", + "2020-12-12 33.44147\n", + "2020-12-13 35.24588\n", + "2020-12-14 47.67855\n", + "2020-12-15 41.84728\n", + "2020-12-16 55.27396\n", + "2020-12-17 32.23195\n", + "2020-12-18 30.18391\n", + "2020-12-19 39.27033\n", + "2020-12-20 45.13756\n", + "2020-12-21 43.00961\n", + "2020-12-22 43.69411\n", + "2020-12-23 46.30954\n", + "2020-12-24 37.42397\n", + "2020-12-25 35.94406\n", + "2020-12-26 45.00482\n", + "2020-12-27 31.81550\n", + "2020-12-28 32.69022\n", + "2020-12-29 31.52063\n", + "2020-12-30 43.08600\n", + "2020-12-31 39.28333" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "additional_item[is.na(additional_item)] = 0\n", + "total = data.frame(row.names=idx[0:size],additional_item$additional_product + additional_item$sales)\n", + "colnames(total) = c('total')\n", + "total" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "bdb60236", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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tOg7Wr5SBp0+aNOUoN+VJB+LgA4gc8PjihYgo6QgGQSgClieA0af/IeKl8g8GvQ\nNl/pxBeWOHyiLJGLN+QdLD00aDrEIoa91uVNBeK3gYx712mJY9nBXITdKcaAVYN2lVrclCq8\nv2PRoMVWDJc3fkBE0G0IJfFTNWgt/cwETV9W9DdtPllkEH68HaVBV7wF7OnG0aA/vr+vUD9U\ngfk5KsDnZF5YOFug8tXdwLvKSL0MGrTOk+EkrWjQnnL0ZQi2khr0oQT9gGs38UhiCNKEkX4Y\nCTriklE0aIp1KQ36TVOiwNG6CZL+YiwrcTRBncamTQOuQTdFss58xCkOvu8io2NomUwN+niC\n3sLSNobBK04fvGqtw9E+2wbRoAl+ljRoemfuU7mYMjiiYAk6QgISEmnqlbKySUZ7/WUcBndL\nvlD5AkFq0J0I2jk76S3p80NNGaxRcaQfpkFTvKV1eWOB6G05I8nP9DE7D5vYsmhU+Lhg06CJ\nUUZqVJpb/M3dN1yDlvl5IT61I9/F4bN8m5ug6cvK2JPozBRdgBF03FDiLBnHxxgadMHP71vn\nvIuj6HeuT5nrwoJfFiKmojYI9fYY2rKJc+AsDVooqMcKjO9fUoM+lKAfcO0mjPrcEBq0A2No\n0PT6pry0gb+FRppkii1B8zpKW59W1YWmMUDiT1jOQTfoG86c9zoz33eR0THQ+alBn0HQW1hj\nKUti7tZxGrQdI2nQ5TXrkHCqXKQv7M9geILGtSt2c9bQjNusQREBNvzNPqcGXSaGpaSZCdq2\nhjHfiiumI8X83QM1aPOO6nwN+hVUEIwoShzmEgzJeXpeL7Rp0DQ/B9Gz8V0cvAKiCz0upAZd\nZoGbeWaCpi+L+luwQMQiNWii4PITscs9R4O+7RdmJusJGjRxg3POYoodSa9VpNw7NKNJg+b+\n+o4CyPdHomgNWu9Zg5J6PYJ+AJ5LnXCmBq11/Uka9POVcEqiUzRoLTkvcdgsO/gZJvGId3HI\nB5NgMwRaNGicy1wC0iK0HR43qwn3dWe2U5TJixL0FrZBZUmNShxR5WEGtQHt1qAt6k+dGZln\nZ2nQZKIyjmzSoFtTaTlDIgL0EJPZ5bd3jm7An9ubnh9tcbQGzRwaov2fmaBtaxjzjb6i35Hv\nnqdB6wP6FA167xadc1EkDgvMmfVtaes5aPASiCJnuAYdGjhoEgcPJqinmtJyAmuHwzVoukq0\n/6cSdBcs4o36Lpv+5550s9GdMEu76+8BfYRDBuzcenuw8YRyqrqmeb4I30xgskIdgOcvU8kD\nV01lMMXbeHbRer/zWFnHqTRcmeG87NIQA7+toWyw9uxS11yfuA0YTuLgVtIe23IS52nQQATd\nYNyfs3j8xB6eDlYAACAASURBVMAsI+iphtGglXLO16DrTJ7Xu1Kz717nXT7xeqsG/UyBRtp1\nihPOQVNy+YARtL9s2fV9Td0atMp1qUHj6KBBL6lBr+gREbDDX6l2nYvUoDnFu977i8VJb9PC\nQLcdJPW4NOji8/dp0FXHuzTo955D6IMxNGhgdd7hpHPQ+po5pQbNjaYpNejdqqQOf8YCkYt7\nBxZmX263j52e2104TmvYsH3JKQ6OWOX5RN5xDdDCt7gIGeLn6Cg1EGVESkUUTeegm2aKnpUP\nCLD8ylZeeHQHpCo6VXZVa5rnptxH0GQ2csht0jn66pMFDMR5K8efg8bMPHA9gpaIVQodGFnI\n4dgt38XhNWCSEdSQHCpSTf6+8m0aNDWNkHiXi6Crfew7nb2MukQmJ7Sb8GnQMKg64qavSNAe\nDXohR4vCz6jE4bPRC+do0BDs0T3oBR5OEV+J6B4yi/kWo3L00qDVgqFszDloMIrSd04LmJCG\nS4N2hP7MTki2MTNBC7GVxCXUOos+sEDvnqlBawZHeRcHdXVcDbr+YwJy1EY4gF0CQcWoqElk\nv/Hzwbl/fMezn9yNLwKELulmuEyjaNBfInFYl7znSGzQmyl00KBZQ9bxMYoGTfn9TRo0vLVH\nqhWvQVMJLTNkO5+a3sUhZ2Xz175zAVh3Dbpln3c9gn4AnCKfG7H8PLAGTf9ZKTx3a+kyUoOW\nL7KV6XIOujKCzZHlnXYRmo7vPD9bSztgboYvQtvBYbPunHGX+8FFCXoLqC3wB8qbXkYlDtEc\nnDIKqUFjico48us06Kq8N8cBEfh+T3p/ZfJ0A5pNTiftkc84B31DWOQHUxO0r+M4lUPP+biy\n6WWUoIM6Gd1iKwYn0aDbFgBzbq0RL6RBQ4EhPfLxfaYWQRsQ0m6C76lBHxpBW+U3mw4GjtGz\nNGhkdqYGbbKz4us1aJsOmBq0cAEy88D1CPoBeC4ZgUYR42rQbb41LzdadG/JsFhTIeBX8NSg\nMX5eWfeT9n6TSAxdqNQiZXGE3SOnBn08QW9h63BtW7/lZ1TiaCivA1KDxhKVceTXa9Arx4ER\n+J6gkYxMIrSJtLnLXD9Fg5bEpC2mJmhfxzEDAIpgiigCDGiCOhndFmhRanOB6H1jxn0w07YA\nmHNrjZgadAMG0KAFnKlBq4VMTdAkRBoT+hpmeyThgRq0cXjMo0Hbt4VRM4XO2luDbuphUIMW\nYhEqd9TgHUGD5q2cqEHrm9LrEfQD8FxywEbQ4yE1aAn8Bjs1aPeW3qRByzbJ+IiVI/S1bRHa\nDg6bRSxEajz3RQl6i+1QaNv/4WmH06Ctv7vVY5Iejp+rQSsioVmDNgRxLmxzHhoRiC4vBF/e\nNzdV4xLNyk/ond2RGnQvgoZVife/zDl7ft/nnYGjadC7M0/NBaL3jTn3wUzbAmDOresPPTRo\nPwpj36FBr2eoDvcODqZtnumppyZoEjyN0SfkzDqYTeIIG0oQPTOJNjWXNOjQX7vX1osPlOPX\n0KAZA68O2LQDtidBqjW6Bi1KE/A+aHPKVW0UfPtynga9b5QveWH/A2SPBb0RyUbQI6D+3S2f\nTLHU7Ip8+/oa9LuNsUkMr1HjaNAL8eleDy2288RYlZnB2KYF2LYJuQ336pR0P3b+RcXIBL3F\nJx5A+Nkykvm0g2nQ0O9ud6Nfj0l6OH59DboegzE60Sga9FaAcEoT0obkQhr02iiyjakJGm2e\ndSfFaNDkIrfLqRgmfMOtNGsrQJgFaNDb/SPoWQy3bC8Oo0ETAS6tItjCuaKNG+pYZB1Eg2YC\n3KbNayED0PuPFpykQVeNQmadmqBJSDQmPCG2bVk0DKZBb2rOa9BBChDrQfmJcnxoDZozKYV8\n5VVpl9LSw2No0KwEIUoTpnLILPp+T7IWrkGDPVs1CpXsegT9QE/hwEbQ4+GrNWiHxPW+MrMG\nHTUflL3gPij8JPZp0IYXM3HO6WvbIk0ItUgE1Pb85XBq0D+wDU5Laj7tYBr0rhzoFIcek/Rw\nvLsGLU8JpoJUdA+VTjOCJqbA2OYcQ4NmCdoiTdR9hTaRM7o4S4N+jQXZxtQE7ew4OkiCIhj0\n9qkatMKu0t9yxApE7xtz7oOZtgUA3FSyGYg2BTVoNpojcjTUscgapEEvSCLJNN2+npm+9hW1\nbIZGB5EadEs0SOadmqBJwDS2v2zbsmgYToPepDhJfqnmPuV4Vw0aflZFJwjRoC3fyRtMojE0\n6N0mbAPHuzi4viKz6Ps9yYGACaEs78Z93hbXI+gHegoHNoIeD9/7Lg6EoHm2nfldHFHzwRSe\nbyUOqSZ0HGl4ZM2rEUD0IkQsaNgsg9qe47kvStBbtOw6vGmH06A3xXA7D/WBMrAha4Rdgwbd\neE9hedIzFaSie6hwn5gCY5tzDA26uL+ms7yLY9dZlmzO7uihQZs6XrYxNUGjY1udEtQixyTF\nbh+qQUPr8yacoWu61Gfm4a2wGVxj9z4HzfOzVnVGg+YCbqELsMBfQ5F1EA2agW+mk38egG9e\nN8I1aGPkT35925iaoElwbaNMTNuWRcN0GnTQGWgoPKViEypewFSAZ7ELZxF3UMv6hRp01OjV\nRARDMeW+hjWAB+vRGrR4pnCbTLCy2rgeQT/QY/9tsT2bBg3/1EvZDLQepPZp0KLrzbuC95XU\noI3h+WbTJlaXJTPjDlljaW7ZDNagqR8IEv0oi22rjYsS9Ba2welZzGtMp0GX/CyrJEyK5jDc\np0GbHv3hk67Mkxo0VbB0f03n0qC5aNntF3c7VoPmY506zOb2M5vzRlMTtHGFZb7e6EWOSYrd\n/kYNGj7FxlneD0e8WahyrbSnNuEm0NrOKy7gFrrAESLyxt64ogb9ArVsHu4duq6v8YJiThmz\nHSPoX1ucEUFzTcNcr1Z7FTaJI2woQfVCZid7isPqEWFCGJpVOEQ57tKg19HcRNFq1ndAACiM\nUOiM6xeLnig1aDahaq2HBq36Qs2VkqEbnRuUoB8IXWEdtkN2nBFP7QgEhjPV3S/QoKMep+oF\nExfBLZtemhOW8HyTVtag2RvGHbKqKjCZw89B1wdWiX5URtH77tQSBwbb4PQs5jUiNOgYIqiL\n0X7WVX8hr9GE0uaw410cz/GPvwMJn3Ql1dxfBW4Y2rUaGVcMzPxQGnTFmOKGkuduqNNwv94b\noPL6We/igDbvUxO0cYVlvr6uQBEMejtAg65FKjRIVNh12Hdx3OwaNLuGWV1Tm3DZR9BvvZ4L\nuAUicoSIBIqsqUGj1uoRc967OKQyXt+6EPS/UoM2pSZLKR8jQPVCZmfXYAsgfzI0WZ4XjBp0\nsYYZm4IFnXWrQSslYYsGsq5WN5hEX6NB0xMW7QJyxPR/F4dnC9CRoP+VGnREnweJnRU6atDN\n920a9GcNg4JCxAFhqm9OcQT0CjSH8Yk+iQZNxRt8Bxl3yMKu5fWFOjxBRCxI0Aa3KLkk4v3R\ng6B///q/P3/9568/f/2/0wh6A+PYNCQXkn6rBt0IqwaNr2HqRqS+V1IIsStSioYC6Ybd+ubz\nHBo01lt18xqZmr9JnW6r2m7ZvuVULtrUeca9/RM9CPrvyPm/f/37/tevPzsTtLPf6CCJNOaa\ngbegc9D6cS7mhsyuo2rQP9cm0KDfNxemHO46FZc1LHJF1ik0aN+eMDQuoFwo5SHKSXRdb3GP\nrGgngv73r/95/HtCBG1cp5DtDFbAFhOeg44BQP5kaEINR2SW8E9R48jvhTs3VjiSVq+i6yHA\nACYNGo87gkYvcQBmZ99SzLL5r7QjXrmtThSp7lUC8zKi9SRojqhTD4L+56///c+vP+7/LzXo\nA0qyo6sG3Rh1+85BQ+sCVL40t455F4d8bgfcsumlOWEJzzdpX2vblvrYRcCxweBpnmC8+tch\n+6hgQ9Bo2Kw7Zx2FH/Qg6B9m/vPnGeF/nUbQG7Sthc6kMe/iOJagtfgTjfca0O190FX8Xk/U\nylZJIeRLphy+ie1oCd62CVODVtOBi1vx3NnTw8bEopEux+z+/cf9/l+/fv1L4eezNGg6MAG3\npdjdLu/igHuYpJ7NbIH20oBnsSrCzyXPuzhQ+0UC+Yw5MUTiNWgis/2p5xPna9CS23ckEWu4\n/hYbHBAatOIEf6/FMzI+mPqHKiSM61Q9bTQgKVOD5u+QoQk1HPENPnVfWRUlJqSvN2vQ2iqw\n0IcMIAY4XYMWF5ZxzkHfiNasT3EY3CFLM49cKhm/bwMxKEE/0GP/bTEeRIJdqpEa9I1jQspS\ntU+PAVWO/uMkcMuml+YET/x1c26+ie/i4MnNuEPWepNOdso5aBydTnE88Pv3aQS9ga1tPGs5\ngdE06K2l1KBvTATNEQwfyyhhOnJ13+ymEwQ7CjwQNduuK8suQifWNqRuxPrkiUDhm8e/i4Nd\nyWqEE/TvA99mZ1xh2e/CKgf0DzWhRtOgb7vZomyA1fLQ+7asP5e+XYPGHxIWqQwUiIUExuaX\nV5bod3HEBgff9S6O/9nw8/+cEUGjLLa/bGjYksWogdkhoIHqpVdjUA2ailLxDT51X00MuLrD\nvaoCm1wea9x3drkHGCA1aCYZb2D9zEocdqeY7+imiKhTT4lDhb9szfUe+2/aOB063Mm0bSVF\nITVoCfyqcMw5aHuiB2Tnwl4ZIPVGGdtuPhI/XmUXAahXySzK0sZGBt01aEme13DRh4Qb2Mam\ndS1nnjalBu3Ckeeg1airTHnIOWjvXk5sOM8PrMFyxQTE2ob4AfWVnNN0s7cGXZysthnpQtB/\n/euPX7/++NdfvQnauMKy32/l4i8kLW8jBA3vKBWgPSzXJDVoIgkwRIpdkdwZwgKn0I6PiyQK\nLEYoFhK0b/M3+GoNmj8xJJbx+taDoP/zelD4+z9nRNDGdQpaKCVDPSUOoVj6OjIKUoPWQGel\nNGiGLuSxxn2H1k0mkaBB7wkCITgfTXsIul60VNtkFrgLbkRrVuegFWcUm9V356+P+H0bCJag\n/+vXn39T83/+PPGn3rFLrGy840PCLtVo8601qpbvf5UGLZ8jkZ0pIU4G09k9EaoVuk9aNWjF\nfT7Ioli8Ku/rNOj3Q8ITX5a0gW1kekIGIk9q0C4MpUGXeaI1aEaZ9O3lLqlBr3acv38Xbm4N\nkm2Hxfbo6BP3LaKRqQkaHXXqHODbD94M7nGsBg1x52a2oPFma7Rsy/pzaTYNml3H+AVux8/G\ntz7wyWQKxP605e6evfmFHG0atFmiAW2+v0DegXMmPKa6nsRhXKf47REHJOnXatBSwfsP5KIy\nmwbNJJfH2vNzTdDQ+sskQs9BQwTno2kPQSNxatFSZBbLOrdsFseF8s4xchq2QTu/aiPXe0j4\nROiWTrFNlBVFgj2q0VODXlSP5ftODRpKBBRPJbAFWjAqfiYjX/XCG2e/i4NIsPno1KDfVzSJ\nQwiyiLWt+NOEVMQCRG1N215Dd0x9zA6CbWx6QoYmgg4ZBKZSUoPmcnI1JKJ7qHT67lbjIBL5\n9nInvYuDIc6S4Hwa9Lrae3//zt7cr479NWg5sWhk6h+qeFcxOjCRhhpoeMVlNWhYzVTALABR\nGrSFB8gMxBAxadB8eVt2U93iUSQ7510cu9BWyNF6Dro8xd0cHIyoQdOTeGqCJmFcp4DdDFjA\nFrUG3fwcHaqXXkibBq3FMsgwpluciFLxDT51P3b1uJk0aCV2NhVb3GASnaJB0+Kw7l1dkmP6\nKZ2ttcDmYMUgGnSVjXTOAJagR3jdaPMaa7BNlFUfrfSddOpRjYZmR07UNt2eTYP29w+0+uBL\n1F0OAYLGUUEg8oPOzUefBm3cWer7kaX6/42OWICozeicM3MHgj7ydaMQbGPTklpY/qtFmWW2\nkEGgYWfIr0HvJmSXJTAgeNFSCVtyjiqI6P51w7OfUJYM317uHn7YGSiXOylYzYzGc9BgPiWV\ntLjxqZU9hOYRklg0Ek7QR75u1LuK0YGJmT+F24UGTYQaeBlAQiRM3ESCyvaPt1PUooEQmFl9\noAYtV5UYIrUGTfYpO5aAsAy5Tye7r11j3yOQ9yA30F+QzP8uDnTOxE6JW1+JQ4W/bMF14zoF\nThvd/g6lBi0SdFO5CqUSyVOD1sBNlSKkfneqEEIiV/XbQL2Yw8KMLdgRtcU3zwiFtONp0Nv/\nD6lBL4xzBrAEDcNftup6z90e0BukBh3Q8RFo/FmXmqLl9kwatLIvcpdjTfTAXXYlaBzpZhby\nY2rQzszXJegPbGPTklpY/ok+Z2ZPyCDQsDMUcw66zwrYbQ0jpr0adRUp6UXX/mhOWTIcweTt\nJA36plXx/a9NuSIpGqqbb3cnadBN4QaYWDQyNUG7VzEyMtFXYPw2cepJ23MBQAMkuSbf8C4O\nEw1QGYgRMrYGvW5umrY45vAVy9CkqhHfYleiITVoYm2z4nyCJmFcp8Bpo9vf4cc37JFNa7nG\nApa+PzpDhjE9x4goleY+tPTY1eNGnoNmH8vZd0vSbaBe9/0dpR9gR0IixNSgfWZI5wwYlKCf\n6LnbA3rjfjNuOZtPeOCpe76LQ08g355Jg24DNKXxJeqYd3HoZuhFQlzC2B4y7iz1FtwGY5vF\nNjXoEwj6gw4Ut09LZbnXJ59FBpaPkzZjZ2h0DVpZ18rbmCPEtFejriJl2HtC5IZ0BJO3097F\noWwViH06UD2aopF2EdPYFjdsZx1CLqKRqQnavYqRkQkWw2C37/sn/Kz5113wNAC2B2YSbiJB\nbfunFwjdNub9ubLZeXCjGdqYmJMAI4R6FwdnV+gAzTWwUYtkd80trABr+KpS9ROja9Atc08N\nbFDQA3JqgiYhThz2Mt6wUMo1goZSQ+e1kOUXGmcfoomPgoFhzDT4O0qVfqu43OSlrM9UeePO\n0pcai2MuYesv5xzIltLiV+c2NSKfOFKDJgcQ2gNl9sf/7+0HYBq2QUK2V8jixaAE/UQ89/C2\nibKMGrTvl+BOvNut08nspgjxXq1VBRUuJIELNm17AmZz9fRNy2oCNKV5Z0p8tQYtJKdofL9G\nVfvXBYra8Lndkvm6BP2BrZtbl/IX1lMcquUX1xyvQYsiuT6q+qyAi3a4uL5r3YuXoTxvqkhp\nGa7iDJcb0tCwm6RDa9Da3yVgze+GJLDBEI2z+y5yyAEcrd9GPRDyTE3Q7lWMjEw8ESx3+76/\ny5r/3EXqgu2BmYSbSPBZoDAs1QKh28a8rw3d2ytuQRtcg36GZ8Jy27TDYJPdUfNoXGhd96Tx\n3aQgrCS/GRWx0QH0BEievkAy3Q3y29QETcK4TJl7HEpavw+6GaL/cGkLH0FHABjGDJF8otTd\no9U6jeBzn6nyBq5Bcy3rXe+AeoVq0NIuA8lVXBYGWhFqC7YrZUtplG1jUEk3tgoN2jNwkAjJ\nbuYVsngxKEE/IW922ngJ6I1OR7JC0KZBN0eA4v17lUJIDoV7tj0Btyr8AO5T5JEvNqV5Z0rI\nzkWNIlN4vgl9ieZgO4+8IT5Gl/YOy3acb//ZrEH1KQ5gNwI3qWOl3uKqBP0BsbhKGqfBAaEb\na6JhLIuxSp95xZ3iWJjP9LUOKwdo1Uq5RSqhxTnLn+geKurDJkqfk274NnMDa9B7agWqV/fV\nIhK07M2aUVrc2LHw8QAu0OaeZmVqgvatYpz2isUw2u2n5fv+tjg+2hZj8obIr0OcgyYGfLGh\na1pDTTRAZKC8gzVohkyAsEy/zaW7L/R1YwnQpoS2JQyqAA16v+RZFjGd2gEegmO65oqW344j\n6Mffv/r9e/t3sPxlO1wvdlz7Ae0LW6i7T8sTaNBQcisA6l+jKur+bufBURxWethUeeNqGjQk\n5ltakfdfrRkwcEmhgi/6M96K3thm3ww6yZb+vAaJkAAQc+Iwgn4Q8++VqbsRtBqngLslAQJx\nvE2HadDNJFpXtPGNcWoegIbYHqikoeZwz7YnkKaaoU+B4QVNaXyJsmvQnjlgCc+VXmQ7j10F\nNZbnWpCh9s/aUEschUWKMuC2E+uu4yiC/n0/iKAr1INc4mdHxLBdnd89SfhGW16k2wERLjG0\nkNMvujctvvFrJGLVSrlFKqHFOctkoGUrFSlGzKQYMjvXEqWooXoZGEe8i8OWc3d1EW7eEA2a\nbSxbA3pI5yCC/n3vQdDOVYxo7KVKROZUb28j6I1N1rxaBvK+pfpOsb/bqHe3ITRocsAXGzot\nYJILsCZRK246By3dCWrTIp1Vg6bXSJV5BS86sQzZT8aBpyRPDZog6H/8QM3mwALcWJbyMpvL\nUMDL9LJLYDCsmGMsiaUtHyDJ28FbXHYfymbadw9nTPM3qnJc1oVJgRalpEOGLtTEaqLlPTRU\nS5ZWdLS4MrilPEoXKI0pTHv6ojLt4eItZsLmp0rQv+9dImgBAeoAbnsX5T6+3HffXEaf2dvU\n8tuGoddrnTVo7AAwk+oiGjQASNgQR9ruuv1v+c6mQatl6i24ly3Wi6oGrRo2wpL3kAh65eUh\nNOiw1HQ3rgS97CaBsiMW+DmGoT+X+mrQmr92JlHSWPfiS31Js/z8MLAG7Rol/nGlChvlqD5X\ng9Z0EeQctK1AJgU7Z4T8xxD0E6No0MT3hbhIpgRvv97F8Z44rHnRCPWeLczEdgbtnHj65rED\npP4UJ2SRn8/OoEHLU1dt2+YpX6fbrsEtq7kjeqUdKjG9Bo3Ovbi4mljbrMAJeg2jjztmB994\nUJcl/oBSPrfqMQGwXC4QZBQc3/UcNFvlZXOfzPn4/zXOQdvs6reVehG7JMHWZ61GOEd2GW0G\nfqZjq5aeBR8rS/V//Rw06lObETKQvBxBv2DqcSONyr3x+FJE0B6jb99wv5BSii53WFemrFhl\ndcn6Gg0am9HgvFcDASYbn8Fkh05h70XJAlCm3oK7hJ8w4F6mQwL1lkXZ0vDX/yWhPiW9O0R6\nYKwEvVgC4NZ+5FHuhb0aNMh0usJhZxKXI1yu+oNqmQy0TIVCxYiZREOtGzUjFvIjkeD9b61c\nORZeoIJiEvZmVw0aSS1Z+cJ3cVTfpbhOK4K5X5ziWPiUQBlAQoS03pJ253PQ2jNC8LUIbc1l\nnsxqxUsN2j7ZYqd8NYTR/M17EsZWJ5Yh+8nIi0pyzDvWCNoItgKItc2K8wmagnXiPLjTEoBA\nCTu8XOw9vyUqUbkRfejohU6r7JuB150HT2Wm1bKhcnTW3cLmKsrLzUBh5TZd7giN5nxDhE87\nlQbtgXmoYmaWCxL0C9Y1Vp370raunkDd3sXRtJd9R9B6qZKR5gQCUoN2pPqB533Q9o5y0vVS\ne8f3nX1xkEieWtv2y1OpQS91EtWwMR2c98IEvcK4V2PjO+b9mHWWlaCVJX5/GexHRotBZd0D\nNOg2QFahhmUzCV3NVdEdaEHEyDOUwfxZ74OmO6Mc1Se/i0PJf7QGXc1giXSmJmi4eaA5IFw0\na9TEyR1kQ6mAOk/1vq5bfDixDPEuDtbY9Bq0/g7PoDaVFxNnAdboFd3rXOAcNG8EbYQ1DbQL\nXoj9rhHnEzQFrurW67s05nMevTTomp+X7UWk389+H7SYYHYNuumMCkQBrHP7W3JHaCPZStRa\n2h4atNIqSmNu/38n7luBb4O4EwllEKceSVUxJkG/EMs9+6ZSBsPPtwM1aC6sZrHzzd5OTSGa\nhngNms2D3affYcLmLh7CGtYrbH/GGhxEg6aXiKE16Kd39IbEv9iy6fDZetEIukTMbmgUDZqc\n9HKPVze+SoOuV1WBAOoqPhuWCrSosorJB8piePAlGBpEgy4vvj6MoEFL8T0/3L2bHj41MVsl\n0pmaoOHmgSIRcaixp8Poy500aO46rEH/IDVoIgUTQW2VLUCDrviZ+1tLsF9oOmL9cRRgjV5R\nZ1OD3qah122iQy95ioMlMdNlAVCOfuegyZm5YJP/mSA1aMiVKiI2atDEXtbLzUC9TtegFylt\natC7e1hHP3E5gn4hnHukwV8v8t00aCEliuE1aNQYQiOWiVPf31Es9tKzPVnjf1pN3PToSA0a\nqONuNfjsOe7ENdUD507HlPcHVyXoFVG7IeIGOTA+izK1N2PNtvYji9rI6Bq0cQUAHSnnHpWz\nrqJJg67MhWvQfM6xNOhyWE+iQe8e8EKF2mYBHoR9MDVBV+EOlxSKV1wxDHP/WA0aY89PJNhm\nB75tw8PYYBr0JiL2vIuD/DvQQW3KOmzMyNzD3KAD5hpzaNDK6Qqoxw1eyeY/s9WN8wl6h2Ub\n7dRQIlgcUI6DNWjyC2dkR4LRrzM139inGEqDLjD8uzgwvvwEC6//i7slSytKMaqSx1OM3CpK\nY27///Ku6eCxYRtksHK7EEEXrRvLOzd58NeL/CwatGdAOjcVECppCIr3DGGP2fnPhQHexeGh\nwCJfuYs3jQBLeL79OIMGveNndd2A24xKaJkhVyFo9ucaUbshNs4gI9r65/2OCD5kkamNbAXB\nTZNpvIjVpxGQVZcnBJtrY6XIonAgxLBiMVIeuszPR3QulVPEFTQuzOf66utfrwZtlQ8kZ6TF\nbV2qqggvNBrBg7APpiboXfdJQw2ibXyG6fcJDVrkfrCf0eVDrMqn3epFzcZ9oST9MDacBr25\nImrQZezFlxjUpvt0nxhVzl/1d/HTGpsbMH2erEEr6bcatMOIcREBzX9mqxvnE/QOZ2vQu86d\nQoNe5FXNgRZW3RA0R2Wm1TJurrwgadBYO3q5GagXpEGv28xPA9MjwEjUauJQDZrMg4+Vpfr/\nZ0IsVlfY4mKs3K5E0PtTHKHBXWGQHAx7rWAmDTo2HGnzbzwN+gOhT+3PlzBCwb0HNejaTZPf\nliVo+9GgQTvmsBRVU4OpSO96F0dTk1lmyIUImoaRLrAopr6xjvx1UZYi3eoyND1dqI2U2j2V\nkCgaq08jzDtrNA/F5hpJrt1ZBFqUdYbnkH71B1+blA4NelmvwAXW5Sq8+Pp3Cg36Ro2N0GjE\nTi6TE/Qzcl0vGNY7eF3TgrPPKN9NUasGDQI1IVblLgx7IZxpDQU0PGydpUGrJLncLqFBU/1I\n8iHUltYX4AAAIABJREFUyCp9fn4lHwWtkaVcHMDlg7tnXERA85/Z6sb5BH271cI+0oxAch7M\nKr0Pofpp0CKVqLX5SQDP5V1GKNBqYdUNQXPhiy2ciZssT8jnoNuaR74N9DF+DnpzFyhQClCq\nIVH11ztFgwbNb0vkVlHqtv3/0e/iMJiZnqDro4svRMZ2lUWqN47ToFvr5hmP7Hk8Kq3DpzeG\n06A3369xDhoxBtlRni7eNk9OJ9CgafpoCTiEhJaWn5uggYczQhvDoiF9o+jE3ToxjQbNJCyd\nr9s5fgWErWIty2WSokNmkdfCQN4N5KI/+IpZPXxEvYhz7x2yvBKcqkGrZJsadC+CLo43Gda7\n9wgTE8k2q/vbpPNo0JgdZi5GsvTD1rQaNB50Ka7Zwsb1m0u3qnJIi1eVbT8kqGBnTRG3fEjR\nMmykwO5Yk91KUNjCTL2pCfp2K5SFG99E1RQ0n4xiDPG+hYJZhWSeIY3Mq0HbyC1usjzBP1z1\nkSp+W48jZtegbWORyIIPlaX6/72+bcbL3rL/7jSzxewEvSoLSAy5vW8maGH018TgqBq6sjTH\nrb5nIvjY87n3CbUAOsJLVENjPP10GrTSZY5+Kga6ODSXT4pv0aApPqF2FqxVAtMT9LO6PNWy\nvEfzMxbFbK8Us2BdlLEVQxoJjUTMGXFo0MSlEOc25ranyLW0ew8wTwiy0pa+IotHgyavanGn\nXA6dknAODT/aiFqu9utfVESQrZtyVhelxa1Ogg6u7X3/8zAh09QE/ZlBbNs8L1I8fGUNWuSF\nuPdBx7H0u/8ADTqIdBgu5vIv+4CAz6GWrCSwhY3rt1q3gjeI5BA2t7KYIViDNg87OUOQBu0W\nTUnrn9nqxvkEvVZm0zTFwBUOhy3Sqzt4QDkup0Fj4FlVLmvtwAtr0Ppgw9YkOlWlQauHLLAC\nTY3IJv4ODXrX4j47da75CfrTNNubrw5oWdIKCNu6mjSvp0HjOYj7ai9sI2iAjopbUCItJZ1g\n830yDXoz9kWWtsDSfNvumUeD3jcZ7wHdXcQ4p2YDa5XANQiaYQBl00Fdx6KY7ZVl/31dlDFq\nkNbbNiJmJ+fxGjSwTjo0aJsnS/VBXfqKLKlBU1mUatdNpxfHWQccFZpbWtzWJNVG3BaNKC1u\nIJeNc24MQNCbybaQE+7V4Ey8Asx0Swfx82Upk5rKAFLWk5w/BFUvHqwhR7BMJILEufLAbFtr\nmViASE8NmJ0GzUVKessqroGDoexbSrey87O0eMFZCcyiQdsDupuhFWTws9WLAQh6A3r8q5qQ\nvUWhHKdr0MIZlcM1aMPTk1k06H110JK83AzEkeQ5aM0gUKCpEdnEk2jQWCgh+QSUbzNzAYKW\n91hxb6MXJmRNml01aCQ+FEZabw2aGKzwqJ9Eg25/toFNaDWwXyEOOHmKICCfwMs2t93j0qCN\nyx5QyV3K9c5Gg/7MmqXOzdsFvHPl/cFVCXqFcRrZaXLZf18XZaxgaXSxvgDcUIy0LU44Bw3T\nGZTK5clSfSAysvuRn/8pgRbOgor/eMvSq4cVQHllbdXdQ9Ha19CghXf3tKn9Uu65CRqZIuSt\nF3UB+Q38z8+XpS6MLUPqrc8yL1m4vYcaneZoDfqREEw5iQbN87PaskFNWvYtqVuZm4pZvLjt\nGFzWLBq0GPxwUcayuWX27GOYn61eDEDQG1BLHnUfuywAyuGvmjAM1vubNDzPsOv9KeegQQPy\nj0Fsu6WG6pFZu2vQ2NilE/XUoGFplk0wiQYtl8o3AXULr9A2d51rfoJm2qzNtmxRibcWV7O+\nhkzV16Vx/4OMF6gTJhb456mO4TToDfa/Z2gcYBihqIH9iq4a9DriDM23my5jadA7dr6R7+Io\nktzEwEickVyYhOW+LkGvUPecWHK+nZf99/eiDDJDMRB2vSUt18Do5Yz4NGicK1qAWfVEykv1\nQa3ksu9j1zlofYI2tOwmZWrQbBJtuwC+i4Oedgt/C/NOyz03QcPxEROwADPdwP/8fFlg72T6\n/QwHxcuFufbyDW0obdCFs/RxGrScnhovckCg0oCaALtPJ+urQd8YaRYuax4NWjLCzMzl1kOD\nNjpHZj2foDeg9h7UfeyyAChHmwYtTYZlP1tMIUYRzsQHwcbGr3GYBi1bIu9Wf6QYdQtOBzUf\nnaiXBm0aJI4wUJu2Qh65VZS6bf+PTlZRhYbLF3PXH+YnaGWPFQZh0NarfMM5aOZ4gHLJgFYN\numdcPZoGvf365MAW9V9yBNq+8UV3PgdN7jhFm7vpcp4GTUb5+8os9yoh854EVX5Uyucv8bgq\nQa9Q95xYcr6d94ve8vYNZAaiu+tV1AduJFk1aNpO/AqIWzVEynUqiQE4w49Pj8XD9DSIuawM\nDbxlNyn7atBCk2uNsTYdXhxnHXCUSqJtFwgNmj5WQT3OY+e14InpztwEDcdHXMCiN7YhXOTn\ny1LcNpahJyQnOTPzjRq0FDKGs/RhGrSVJJdnu/GPCFQaUBNg9+lkvTVoyAkek2nQrNrMPyck\nUuLuUQZY53AMQNAbLMW/JfAARwGU4x73E/OiWIlZsMlv06BNR/qMjV9j9HPQ+BSFzQL3gU4+\nW4OWy+uvQVuDnO3/S+/4ZZifC1t74kYLcWuL+Qla22OJMLSiMGhr0nSsoEv1gS5sd8mzDpg0\naGqsBgWBFFKDFi/yRacGzYxRaoNQsGmlQa9jvu4laEHZMrw4fQFclaBXiHvOeroZYsByzG76\nw6BV1Xc5pqZyC3TBTimLBv0ZajhXtOCORfUOT4iJX2fkBsPjw2seG3cJ+gT1t+wmZctcQgpk\nm1wj/03TwaUxJG3KWV0SFreK4mkNGvn+yW/aaTlXXhkDEDQcH9FtVTQjFMRI92ta25WGewcm\n5J8qkwW+vps0aHmzFs7SQRo0sLRZvj6vYG9P1Eq0jCc82apBWxmNHsD2VpbTT6ZBC8uwbPyz\nojsljvrr3AS9gTZDqLWVf+QjABq6DrtgsdvhQK4Dqg27Bo15yFvEDYytQbfWz0vOAOuerUHL\naY/XoJWFcmeikYfI8pzTv850AYJ2Bb7PRBYiFQZtPTiO0aB964DxHLT9mVgDMw6vQUcBalRD\ny6cGDSye+9Vg/Uacg1ZdQFvCZpXA/AQtV1eeu/gDMJYmF/JrdYpDtktzkt6R3TVo6gr47nbJ\nGIvxNWjUK+GyMjRcG5bUoLkkeoxea9C8QX9niYmdK6+MAQjatq8hLuwIzhWKUyv0ceeguafK\nZIHvxQN3hTKzXRQwOjUE+eNr0AoNqz2kuGYMG9evIRq0yru8D3L66TRop3GzY2zW9evcBL2B\ntlDC8Y0GKEfodnhXrEQl2GJifRdHwdmSrELGIBYZ5nIatOMZgXKdTmTToPntVVWGpRXZtMJ0\neHniiETF3laqthtgIZO1KbQWcl2AoF2BrwPCRKtGR8O7OACeaatc07s4kAerez4xCeVX06DZ\nykNrj2GBGkOD5qJxQYN+riQLsRVE4wfZNSrM3+foo0EL6UwNPz1BB20ZtfQsTS7kV3hqEaNr\nqT64wI7bNg2aOxfNeWEkaCihJ1ImJj5fSTKlQ4Oma68sGni3b1IOq0E/PioadNlIDEnTkZim\nUtbMX2IADdq1+dAwAEHD8RFBmOr01GzW9z+fj34XB9kSzGA6VoO2nTS5mga9IWiBBcSSkXQL\nvZ20rnYq7/I+yOnlmS4u4srAxoZXi3cqjK2mW9l9nZugN9AWyrZw1GqomwYtMgk2+ds06Juk\nF5IxiOMhoXUfQt6Pmy1PCAub0CIWERoyTyciNWhtlUMWwZAQUdGgrcdFV++XD7ejsdqu5ovm\nndUnxAOTlSsQtCvwdUCYZ9XokKqmhgo66zbVrkmDRnJUUSRuejQNegvfcGWYh9ndKBd57wbW\noBfKu/0mzK9Br9yOLG77MtZvqUH3JGj3ltE2aonrr6G1T/H6f1W1zbgjRDOak9oWGXZKtZ6D\n5lJEoJ8GTTSrWsk9a+Q5aOqbOo1+PqrnoLkX1+iLdWrQLAYgaDg+gmIRRyhODo+/r5VV+6zx\nwFMjoTj0DsnOr6+NGjTmjxNX06DrFO6AQkpHa9CYLZrerK0sp+95Dtr/11rfaOMhe4urZrZf\n5yZoAoYQ2Iddh1DimSioGU81VMWKTIJN/lYN2pzUYOByGrQpHWaec44gWGWVQxbBkBBRPQft\nKUZsFazmi+ad1SfEAzo71yMXIGgyzAuP7aiN18q2NWkrDH2rV35mX4fH+hreBR6tQVtQatCS\nqcWUSE9KJNh96/ouDmz3xrvfW4PGspDEvlDesZ3HTQOtUGRx229z1m8DaNA8Y0xP0O4to3GF\nIy9tyfj1/4WOkTfj7s3Pn2cbTODXtsiU43b1yadBi8nD4NGgrVt5gdg5w48PTg1apVl/7LVJ\n2FmDXtjhoZL/z8d8F4ecWIjp5iZoeKZCk2AB5mx5tz5FxfLzJu4uusQzI+U7dVTxKdCqQe+c\nR/1xIjVo+TaTrkGDZjJYW1lOH6tB2wXCbt7dHC1OmKg5Y/08N0F/IIRF8nUziqlBvI1T05gX\nskuwYikC5b5WTj0L7KhBNxtIDVq/wTm3u1Wv0IRBpLyQEDFUg37PG99U2NV80bwzeKVekDLz\nhwUvQNBkS4THdtzGixor9SmO2hi/Zsojj71GLhZlgT+f4jToskD7ZCsRrUGXt1TXhLb/Zg0a\n7FmS2BfKO7bzuGnwuUxPHGTt3G9z1m/3OqXsgnhHTUdO6NSgsTtYDENe2vW6cA76feHZJzW/\nqyOUdbGWW3bWPho0ORrkKI264DuOIiI1aMGQZLn3OWh2eOjkXxA0UBpj/jlXxL0nP66FYu8C\nxZs7T4IYPzE35iZouLWgSdAeiq+J+XPQa7LNIPPMyOrObuBSQ249xSERKxVBiqFMLEMPr0Fr\nxKAOQXdAIaUbWYNeyAgaRR1ge4adnPp0DVqyOjdBf6DOkLC2gwwhO86Py4az9hSBvo2AI3dN\nBzaJzM/qXzwwNX1q0PoNzrndLXFpVVcLI1NLiR8jRNegDeWQei2jtnHl7ep/vgYtZLoAQeub\nrBiUo18qDKnaOjRLutSHGnnNxs/2IyRl8WyBDU1fkqBkCtmA1AGYAqHtU4O2LJDvHA/EnoMu\nBt3yLkZzqajGOgEG0KB5TE/Q7i0jNiWEGwv19XVR16DX9CXPASOUcVHWoPfplD8Fq1PDIs0K\nN1KDFgxJlk/ToMV5RG3rgNKYAJ7vOGFTCMQhI2jQLOYmaLi11LnIZvf1gKxB7z7C0oTozCuM\n2Cekk0dp0FUoE4DUoOXbTLpxNWgugkaB7TTUOSRWJzXonhH0C+oMCWs7yJBhxwnyMzm/Xcv5\neooDbBK85TT20rHnGXNsKe8CcJBZU4PWQOpd4Rp0lZWaQ0rNd/V/tV0jQ5CD1Wq0Tn4Bgqbb\nIDq2q0e/VBhUte0+kDdPVaSpclR0b0NQFEhgDA2a1uen1aADJggU/tNKHX2Kg+08YZYJvrVr\n0KQBZ0j+SWUzSmF6go4lC8vGmgxfXxeBc9Cff7i1v4mG+SnleheHOZj1AdOggSVMyCMQ+6sP\n3x2yT+nVoDUy9jftJuEAGvQ2ii1M0ptdpCwoKr7d+E0hEKH/DDr4gbfWeYVTyCknEXMTNNxa\n0DqGRTES1sSKBu2mNyMJkMld7+IwuuPFURq0xJnkXH2HgYxlIJqHglBT2Lj9dr4GLenAYRp0\n+Lz5wf0mOm8wXfSK3yjPJAYMQNAfqDMkikswO45TT1i5Ypxr883HBY6UhvoOoEGz0+ryGjT5\nyylD7/NsxE8HLuaGCkTuiZHL85+77Uk96s0itQhq5AoETbdAeHAnaQ/VBYsGXX9T52NT5VKD\nlo0KBB0HyDHDAhWgQUtkom2lagO7tKwGXbe0MMsk1wSfiFL3GZe3I4b2xpxLDTooKHElp3Zf\n72saCQpxg3mE0uALADRoYFTFL4A/GEaDXuqUvTRo50Zon7Bdg7YcVGPGCsfPrAbNrIVMrAK0\nSz3PqucJNe5vhgYMkrNe8MczUnc4laCbsYhfxTtEWjI7b9OSeNnf/BkNFrtYAfV3LjnaULIV\nY+PYANf0TjSl6hfdXB+LdSGyZeX25pbiGtqiVD+V1yFbq+cvGPI29/5SFKqXYSxyNQ/kc87I\n3bT2WVCsBiA16BJy1Tx6FxUR+0IvowZt8JNJarDg0KC3TQnLIwrIrBfXoJlDGHSTiw5y3lGG\n6t0KDlX/oqTlXcMsoneN3njGX51neonDLFa4IWgP1QVREqQfSXCTAtp4WXAtDVpe7DZ9htGA\nUHpq0IuekDZ/lgZdzrOF+selNDc4Z5se0xO0kyyQY+3KDUqNel+TSfAzbkSzTTTMh1CX0qDR\nx++bNMJmixMYHx/CNGhNx4SbdpMw4hy02Iplk1NO0K7dpHPQZNcxsQrQLlVTwho0aNCoQWvW\ndMxN0PDQpjpOTSSbFBNr7+KwSxyKN3RLkMnvuB1tbIezNDAkyhlCtSUZHUo/GUAia6nd9GCM\nUhUEQ4A/u2/HnYP+LIlQ8gfOOweNCChh7+IInA4skxgwAEF/IIRFxHWPCKzY30PbcdpLJ+a3\nM6o9WoO21HT1jZ5W5I4R0KDROFvzDRpcArzszNXrg2o7KfOStlpIAbKjKe3rLgIs4Cdv7Orf\nSYOOqdIFCNpOC875yi8C1QW1akVIfasnF2+8cam+lgZ9E3fmnzRV0CdnIAy7hyvyE2Js98a7\nf8i7ON60hkhKu2/Cuzj4G/EBRLEu6due5r0jGVFgWV+YnqDl6jJ3WYLGYhjy0i5kQUlQNKvx\nIMBeVJJLadAoHBp0mcWrQdcjTdm3w7XbJDz0XRzAe+MEgtaL20wBvSShWLDU1KC7ETQ8tKuE\nFD9jUYwIiaAjKG0bZgOiKrsbRhtK3ijH0/TabuJMUyM4at0TI3MyQyGeCO0mcH4ZvAftPspN\nVrAGbc2qpPfOdOmok82QdDM16J4R9AvSFKGuuwVJLJt9x4mVu40t1MiMwRQaNJ3zdZndY4sk\nvL2FHfvYl+PToHd2vOys16ubBh3COk4NWhEigR0kd2NX/9Sg+xK0jxaaWk8NwLCqcfNOjxqW\nmj9gzK5Bv2uOFMNsLPiWK1fAPUF7QPcTtPfD9ngPHKlB4xne8GnQ2Bi31GOh/nHpIg3RjW16\nTE/QcnWtXGEJkYjwdR1M/TVoLLqgEng06JDwAACgQVvWpm2qZXORY/j94vAp5PFPatDkF4T+\nnRp00c+Any5GNUXirk2rL/UDcxM0PLTtY9CU8512HU6DaNAsQaMNpUVO0TSNaNAAPy+bpKwB\nlWUKcuDbjTnnt78t9IjmiJZuobeT5kFv4kJhz1chNWi31bkJ+gN5igS2nbQSf6Z9fw16Swu1\nZYm/vkaD5pgcjsCrMJDjZ1Rq8rIzG76uEDRo4bQSUmDEzEFmurC2WXJo93YjS5+sOEI2mXWW\nCxB0Oy2A4AOMR4C8JYTuGrRiWKKMwzRoh0KOnYNeuJ/Jl6lKJhYja6HAt29COYozWjHY7g3v\n1E0exjmTx9pOSrH+reeg2xt+foKWq2ulCC49QJKLFEEb7JpHKGFidYQyQTe7zIue8MBOW8Hn\noPf8vN1jcQwvdhOn3fOSNme2GjmYF5Jddi4BywdUHhs0PIMTObn7HDRyGUsiZDZF4krfyXDM\n6LkJGh7ajjFYXbS8kede3kB90EpAbmwiR46g0YbSIidp2DsCS/ActAo1UjZz4sK327aiOg1g\nuw+15cr1lNOgNcWeWZeNvKO5GyMi3PxzSMyXGnTPCPqFpfqwR3vbCZS3SfP6cC8uS8/zwOLr\nvOzq/uFnKYKO30LSq4SlvpAGjTnwiZSDcKAGLT7DJI2wGjTNz9pqgQzXvbfNFGjvKVPku7+x\nG19By4d5tCI2rkDQWIzUCnq7zBW2Xzy4AczNO3Y7KV4rSvz8I/mm27KX/iIHZwStkxHoBZFG\nzyKkkGRewBW5mH2tOdGEL+dEDZoM0vffBtOgi+p4dmtYqe0NPz9By9W1zhwuPfHASZz826pJ\nu0ziorYXgLDyM1Hs12jQTB6hgdnu//mf9xx0fUtZNqjBppkVnFPtQK3J8u9yMxK0e5UE/HQx\nqikKMC/5/tQPzE3QaiSr3lATrcusKSDcVG2B5xsAQwXZYkUNmpyIzkDCd4pDM+wInNUEEGcI\nP/VGDC1QUZvNh9y2tXPVdXNL2aLXTeoIiQPCrnqG4dXRO3bnG2N1boL+QIs729sO0aBXSBKH\nzxWCKrXlSYmg47eQzBYYNxCtQYcCIWgFCDs/PsRp0GJJyHoPcXXAQ0LzZlcsVK7Zvvq9NGjP\ncCHDKSeGIWhUPGiEwLFKs7JjiZuE3IRkRXAavTRolGhcETRMso5Q2r27/sE98JmjunOmgwG+\n+JPfxaF46tGgtTG2mCKmvc2F+CY6Y7gDO4FheoKWq2telvHrIklTx+ysdokV2coQ4ikOwROA\nGRz0CKCjBk3MR5gAH9fvyk4ev6UsG3DtNgmb5pK9L9UIWyBo9yq5C2FoGcjFqKYowLLkm3eA\nBOYm6JD+kdP4lkqyas0bZIEhDLYP06AdOEiDtlPickOUYYAGgjYf5Xp6qgatpu+gQdse64gJ\nU4PuGUG/oO2MwtoOM9ThXRzVQz/v+pwatAsRj3pBdq5S6vU6V4PW8J0atGesVFmuQNCBka8I\nPsDwNisXhNATckMQLbVLDdpnNOwoDlUMNob50k/WoKv0gsQhm9IiraoMSz32A1Mfpk37OGa0\nGEfQ9ATt3nKa0hPXRZJGq1bblbaZMQRxAQ3atUDV8xEmwMf1u9L8eDynLBuejftYGnTZUjEa\ndNP66om2yXvC3mZ/h1jPHcN2boLGh7abT3yM2EWDljZNBtt3MT01/tqa1YKRNeg78jYW7Rbq\nmaUGyxKjQSusC2a9Edw09Ls4GoMeptWifvwwN0F/oO2MwpgEM9RBg9avgIZn0KCfmcw1lLcB\njQhgGcUlliHFej1oYCQNuuam0d7Fsa1+mG5VRssufq465goEHRj5iuD3xlVhXTRo5RqKyrfY\nqbG57ZA4DCTraIOWPXJcGEgVg41hTk5YFumn3qbLUmI0CxBB60sDXBiYbpt2Ux2RR5v2calB\nP/4v19c6iQ09JZruo0HHIEaDRuAg6NiwnqyU0MJi96t9Ck9n+SvebD8Jex8uoV+kqkTY82jQ\nWvNpG1ebduLopbkJGh/aUNO0LHj7dJ00aLR4ESEatLZHdgLQoBGYpBCotKV5n44FoWi7blaZ\nHcOw+zDVDs26YHhZNSgwHTzwDguBGFqXty4R1bUkjh9oO6OwxsMMpQbtMqBp0Dfp75aUGbot\nHn4oLrGUJ9ZrI3FUFKuQq7pMBx0t/EYNOsjIFQiabJkO4aphb+zQoNltY0tcT+E4DdpuOViD\nNkfKUoJBNegHxYivGzVdLgyz4bkJuAZtj0ctTu0r8qZpz77bPwGNOWcnaENMggDZztGXfHs6\nKS7upYiEaNBuyUjG0OegG5xahG8OwaVICeovmhnqzpOflzIRF0wwOEyD1vcEBGxtJ3alKS+E\nuQkaH9puQvHR5OU1aHuxEHQNOmRlsFMiyIFqS4EBhSnU//lMOYc0FaOqfPZAPg26wMAa9E30\nzrQkBE6Gj6m5CfoDbVcd1niYoa/UoAOaXtegcQ+6LR5+GNznq0IaeWnQJVto5AooDRHNOLIG\nHbd8RLRUZeMKBE02TI9wlV8FygseiYPbNsbF9U+kBu2z2U2DrgQE1hvevb7noLHwn0dHDdrk\n035den/j265tH+fMKpxQtGEIgraEVAAM6tcifb2YBm0OZQ2JdriDeTxtUy8bMAE+rvfSoBve\n2BCyE3Z1pm2YTqtBq/PeNhCR1HG/kh+AoPEIyU8oLp4ENeiwv9Bh8dKuQbe2K44v1KDJV8ia\nQ/07mE+yw23hQBcETKtBm0yHzIXyhzNzE/QH2q76YCbEqmY5ggkEsaCteTRocxVhecSDPhr0\ndkIybIlsb4hz0KxysrkOxKoRXA01XdPigt7bNfTrn4E06PJg4zUIOi7wVYDvjaFdU9EV3KSI\nrl5q0D6bXTToekZK3vDupQZtSbxfD4fSoD3vmWIwBEHL1Q1blFUtqvgKVI2ZmsE7JgKHadCO\nCrw1aG1n4WmbetmAq7UJUn1OLew3Ygx49iypQStJhLypQXcjaDxCcoxBS84SkAYd9Dta0raA\nCTRo1wvG8ERW5eSZoqMGXeUvekFcSbarx0In4cFkMK6/IRIHAu9wE/MFadBhU3kJWnkHIOgP\ntF11GJFghrBFeQwN2v93ULGUlqZ/H+d9tYy1irA84kGvc9D0X5nkhwlppCLohU+7uQ4sgxHN\n2EeDxrcsxfXiDX0xy0dMsFWZuAJBxwW+CvhVoLyAvosDCWCiq1f45hlYKE+aDd/fHnEMjTtB\nJVGzSAnOehcH8xRxj7Zfw2lQN1IKxtKg3835zhqiQSPbYXVIE5idoKOiPyUDcX2RvvqrZh6h\nZux9ew8saU0gRp5xD4z7tsjPzWTTYon1sgHsSjaXT3oXB/YU0TzgmsQQ6zAthpytKENGbEtQ\nNmeABo2MWTAUKubi3ASNR0iOMWjJWWKad3FUA4sYf9LIC65TIXEYC1yKvSubzmJ0TYA8+FVv\ngZ7t6JNqjc8i8/zk1KC5ZdmYV0t+uga9XVAqKg3RoDF+RjerDSvvBwMQ9Ap1Ux1GJJih+B1n\nQ/RQYK9B68EqEBuI5VsqXDwktDFp4Wb0enjauzgkDfp9r58GHdGOQ2nQlYB2jAaNzaPq9hUI\nOi7wVcAvA+UFT9W4ScFUz11BowZN7d7QUWb2sTxmJ64c5fd6AuA7LCDBWRq01Bprlc0RgdLn\nZOKoISeY4rZyLEw+bWKSTV5HNEVd1xXoR7nGRpydoOXqmkeUoUPoMfzCPBr0e2DxxEau/Ob9\nM+oblonqDy1CqZeNRxQlMvr2cjcN2r6OvNN96hx6DppsRq4KiLNDadC38pnKUeegoX3oJrLy\naPkyAAAcwElEQVR/YG6CxrfAUEPGheKHa9AG49WvggU7T+4+WoOWIBBwGRkhNp5ZIMo4UIPG\n5vGtjqCt0gS5OoGS1kJ+JDGSBl1/i3oXh54UTvtJODdBr1A31WFEghkK16C1iMZg2f4uDvDZ\nhhR+gijlF5Mtmw6zZkGnzWkatLa9+fk3UIMutyIBU2coDbr6EqldNaIei1cgaCDwjfmVDz9m\nywsHaNBuBLyLA1WWzJbvAhfpPvBnHbjvpSwiFdhTg6bLRVLtT3EARuTLT5OMlu8edHjT2aQT\nPBmZWCfoDvGelJWIFmYnaCWkWj+ADG3oELqzt7658BIVBF9a4XgXhy+5owKoBt20qGw+lFwk\n9X+LBi23F0bXotXzNGjE20qDBhd48Hpj1mHexVEeL/nB3AQtR7LrOEM3snGxapsGjW+7Hcbt\nGrRWrMNbDi0aNJ5myy5za9Drp9hz0FCHGjh6NA16j8M0aM3SQjD03AS9gtpUf2qrPd+3ALPS\npEFjpyaQKySIuSx6oyZQns1Z2r1Jg3ZFN/iwGFKDfiP4HLRang2pQUOgJv4VCJoOfLfrURA/\nU8sAfaFFg4Ye0jTUJliDXr0lPDZbbtOg1SRQpMxhTA36BZ8Gbfxhm3vQ4UOuXGFUmHyio54B\nNejrRNDSIvqhuZk06GUpGToYsRr06iwS9QO+9ZiXRZ79+gFVHJI4xKknJTSvI3VC14BTtj5c\nWcVXIPdg56ALHKdB6+LbylfvK3MTtBzJbiij4ZyYiyebNGgXPePpgzXoDT+3rymHa9DPLxBl\njKlB76NAViQhbYBPSDUX9JypQdtMbvplboJeQW+qewShGOW3nYMmqK7OhVwh0UODlnR+Syc0\natDWfYAJx2rQ/BfSiEODRjdqAc2YGrQFu365AkHHBb4KNstAMbLL0hrPQes/uGioXvg56JcC\n/WkSernEfMMpttVta4LLadD4pgcO7BmkBm3IulyKoOXqmtsR7ZB6bO9TzPQuDqI0wxZ7/TKg\nBs34s18/oIpDEofkk1iKgYi5hKlB+7O2aNDGQY619HUkDvMWWAYcq6q7w0u9iwMsNkJQSg0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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "ggplot(total,aes(x=idx,y=total)) + geom_point(color = \"firebrick\", shape = \"diamond\", size = 2) +\n", + " geom_line(color = \"firebrick\", linetype = \"dotted\", size = .3)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "294dde87", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 2020-01-01\n", + "\\item 2020-02-01\n", + "\\item 2020-03-01\n", + "\\item 2020-04-01\n", + "\\item 2020-05-01\n", + "\\item 2020-06-01\n", + "\\item 2020-07-01\n", + "\\item 2020-08-01\n", + "\\item 2020-09-01\n", + "\\item 2020-10-01\n", + "\\item 2020-11-01\n", + "\\item 2020-12-01\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 2020-01-01\n", + "2. 2020-02-01\n", + "3. 2020-03-01\n", + "4. 2020-04-01\n", + "5. 2020-05-01\n", + "6. 2020-06-01\n", + "7. 2020-07-01\n", + "8. 2020-08-01\n", + "9. 2020-09-01\n", + "10. 2020-10-01\n", + "11. 2020-11-01\n", + "12. 2020-12-01\n", + "\n", + "\n" + ], + "text/plain": [ + " [1] \"2020-01-01\" \"2020-02-01\" \"2020-03-01\" \"2020-04-01\" \"2020-05-01\"\n", + " [6] \"2020-06-01\" \"2020-07-01\" \"2020-08-01\" \"2020-09-01\" \"2020-10-01\"\n", + "[11] \"2020-11-01\" \"2020-12-01\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "index = seq(start_date,end_date,by ='month')\n", + "index" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "7542d95e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " total\n", + "2020-01-31 40.12725\n", + "2020-02-29 36.38809\n", + "2020-03-31 36.63476\n", + "2020-04-30 36.69513\n", + "2020-05-31 40.12267\n", + "2020-06-30 38.04770\n", + "2020-07-31 39.38036\n", + "2020-08-31 37.60016\n", + "2020-09-30 37.76963\n", + "2020-10-31 38.57457\n", + "2020-11-30 38.38480\n", + "2020-12-31 39.73959" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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B8ooJdFQE81QJeLgA4X2wcK6GUR0FMN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\nFo1Pe09AL+eQPQ9Z85Q9D1nTovFp7wno5Ryy5yFrnrLnIWtaND7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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "x<- as.xts(total, dateFormat =\"Date\")\n", + "(monthly<-apply.monthly(x,mean))\n", + "ggplot(monthly, aes(x=index, y=total)) + \n", + " geom_bar(stat = \"identity\", width=5) " + ] + }, + { + "cell_type": "markdown", + "id": "945feffd", + "metadata": {}, + "source": [ + "## DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "id": "d38f1754", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in seq_len(head.end.idx):\n", + "\"first element used of 'length.out' argument\"\n", + "ERROR while rich displaying an object: Error in seq_len(head.end.idx): argument must be coercible to non-negative integer\n", + "\n", + "Traceback:\n", + "1. FUN(X[[i]], ...)\n", + "2. tryCatch(withCallingHandlers({\n", + " . if (!mime %in% names(repr::mime2repr)) \n", + " . stop(\"No repr_* for mimetype \", mime, \" in repr::mime2repr\")\n", + " . rpr <- repr::mime2repr[[mime]](obj)\n", + " . if (is.null(rpr)) \n", + " . return(NULL)\n", + " . prepare_content(is.raw(rpr), rpr)\n", + " . }, error = error_handler), error = outer_handler)\n", + "3. tryCatchList(expr, classes, parentenv, handlers)\n", + "4. tryCatchOne(expr, names, parentenv, handlers[[1L]])\n", + "5. doTryCatch(return(expr), name, parentenv, handler)\n", + "6. withCallingHandlers({\n", + " . if (!mime %in% names(repr::mime2repr)) \n", + " . stop(\"No repr_* for mimetype \", mime, \" in repr::mime2repr\")\n", + " . rpr <- repr::mime2repr[[mime]](obj)\n", + " . if (is.null(rpr)) \n", + " . return(NULL)\n", + " . prepare_content(is.raw(rpr), rpr)\n", + " . }, error = error_handler)\n", + "7. repr::mime2repr[[mime]](obj)\n", + "8. repr_html.help_files_with_topic(obj)\n", + "9. repr_help_files_with_topic_generic(obj, Rd2HTML)\n" + ] + } + ], + "source": [ + "?vector" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "88a435ec", + "metadata": {}, + "outputs": [], + "source": [ + "a = data.frame(a,row.names = c(1:a1))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c4e2a6c1", + "metadata": {}, + "outputs": [], + "source": [ + "b = data.frame(b,row.names = c(1:b1))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2bb5177c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 2
ab
<int><chr>
11I
22like
33to
44use
55Python
66and
77Pandas
88very
99much
\n" + ], + "text/latex": [ + "A data.frame: 9 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & a & b\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I \\\\\n", + "\t2 & 2 & like \\\\\n", + "\t3 & 3 & to \\\\\n", + "\t4 & 4 & use \\\\\n", + "\t5 & 5 & Python\\\\\n", + "\t6 & 6 & and \\\\\n", + "\t7 & 7 & Pandas\\\\\n", + "\t8 & 8 & very \\\\\n", + "\t9 & 9 & much \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 2\n", + "\n", + "| | a <int> | b <chr> |\n", + "|---|---|---|\n", + "| 1 | 1 | I |\n", + "| 2 | 2 | like |\n", + "| 3 | 3 | to |\n", + "| 4 | 4 | use |\n", + "| 5 | 5 | Python |\n", + "| 6 | 6 | and |\n", + "| 7 | 7 | Pandas |\n", + "| 8 | 8 | very |\n", + "| 9 | 9 | much |\n", + "\n" + ], + "text/plain": [ + " a b \n", + "1 1 I \n", + "2 2 like \n", + "3 3 to \n", + "4 4 use \n", + "5 5 Python\n", + "6 6 and \n", + "7 7 Pandas\n", + "8 8 very \n", + "9 9 much " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df<- data.frame(a,b)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8f45d3a5", + "metadata": {}, + "outputs": [], + "source": [ + "df = \n", + " rename(df,\n", + " A = a,\n", + " B = b,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0efbf2d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 2
AB
<int><chr>
11I
22like
33to
44use
55Python
66and
77Pandas
88very
99much
\n" + ], + "text/latex": [ + "A data.frame: 9 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & A & B\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I \\\\\n", + "\t2 & 2 & like \\\\\n", + "\t3 & 3 & to \\\\\n", + "\t4 & 4 & use \\\\\n", + "\t5 & 5 & Python\\\\\n", + "\t6 & 6 & and \\\\\n", + "\t7 & 7 & Pandas\\\\\n", + "\t8 & 8 & very \\\\\n", + "\t9 & 9 & much \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 2\n", + "\n", + "| | A <int> | B <chr> |\n", + "|---|---|---|\n", + "| 1 | 1 | I |\n", + "| 2 | 2 | like |\n", + "| 3 | 3 | to |\n", + "| 4 | 4 | use |\n", + "| 5 | 5 | Python |\n", + "| 6 | 6 | and |\n", + "| 7 | 7 | Pandas |\n", + "| 8 | 8 | very |\n", + "| 9 | 9 | much |\n", + "\n" + ], + "text/plain": [ + " A B \n", + "1 1 I \n", + "2 2 like \n", + "3 3 to \n", + "4 4 use \n", + "5 5 Python\n", + "6 6 and \n", + "7 7 Pandas\n", + "8 8 very \n", + "9 9 much " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "88b51fdc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Column A (series):\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 1
A
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A data.frame: 4 × 2
AB
<int><chr>
11I
22like
33to
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A data.frame: 1 × 2
AB
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A data.frame: 9 × 3
ABDivA
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55Python 0
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A data.frame: 9 × 4
ABDivALenB
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33to -22
44use -13
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77Pandas 26
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A data.frame: 5 × 4
ABDivALenB
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11I -41
22like -34
33to -22
44use -13
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\n" + ], + "text/latex": [ + "A data.frame: 5 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & A & B & DivA & LenB\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4 & 1\\\\\n", + "\t2 & 2 & like & -3 & 4\\\\\n", + "\t3 & 3 & to & -2 & 2\\\\\n", + "\t4 & 4 & use & -1 & 3\\\\\n", + "\t5 & 5 & Python & 0 & 6\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 5 × 4\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | I | -4 | 1 |\n", + "| 2 | 2 | like | -3 | 4 |\n", + "| 3 | 3 | to | -2 | 2 |\n", + "| 4 | 4 | use | -1 | 3 |\n", + "| 5 | 5 | Python | 0 | 6 |\n", + "\n" + ], + "text/plain": [ + " A B DivA LenB\n", + "1 1 I -4 1 \n", + "2 2 like -3 4 \n", + "3 3 to -2 2 \n", + "4 4 use -1 3 \n", + "5 5 Python 0 6 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[0:5,]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f944a949", + "metadata": {}, + "outputs": [], + "source": [ + "df1 = df %>% group_by(LenB) %>% summarise(a = mean(A))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "8ffd39cd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 5 × 2
LenBa
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35.000000
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A data.frame: 6 × 4
ABDivALenB
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22like -34
33to -22
44use -13
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66and 13
\n" + ], + "text/latex": [ + "A data.frame: 6 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & A & B & DivA & LenB\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4 & 1\\\\\n", + "\t2 & 2 & like & -3 & 4\\\\\n", + "\t3 & 3 & to & -2 & 2\\\\\n", + "\t4 & 4 & use & -1 & 3\\\\\n", + "\t5 & 5 & Python & 0 & 6\\\\\n", + "\t6 & 6 & and & 1 & 3\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 4\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | I | -4 | 1 |\n", + "| 2 | 2 | like | -3 | 4 |\n", + "| 3 | 3 | to | -2 | 2 |\n", + "| 4 | 4 | use | -1 | 3 |\n", + "| 5 | 5 | Python | 0 | 6 |\n", + "| 6 | 6 | and | 1 | 3 |\n", + "\n" + ], + "text/plain": [ + " A B DivA LenB\n", + "1 1 I -4 1 \n", + "2 2 like -3 4 \n", + "3 3 to -2 2 \n", + "4 4 use -1 3 \n", + "5 5 Python 0 6 \n", + "6 6 and 1 3 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "515c95b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(df$A,type = 'o',xlab = \"no\",ylab = \"A\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "41b872c9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "barplot(df$A, ylab = 'A',xlab = 'no')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11001454", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "670db495", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.1.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 36a21b89a39a82e4a4aa2bff2c95e7802ed0d3e7 Mon Sep 17 00:00:00 2001 From: Keshav Sharma Date: Tue, 12 Oct 2021 06:58:20 -0700 Subject: [PATCH 11/12] added --- .../{R => 07-python/R(Bonus Lesson)}/Notebook.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename 2-Working-With-Data/{R => 07-python/R(Bonus Lesson)}/Notebook.ipynb (100%) diff --git a/2-Working-With-Data/R/Notebook.ipynb b/2-Working-With-Data/07-python/R(Bonus Lesson)/Notebook.ipynb similarity index 100% rename from 2-Working-With-Data/R/Notebook.ipynb rename to 2-Working-With-Data/07-python/R(Bonus Lesson)/Notebook.ipynb From b3aabac0fe1a461e012c5be6b3bc1056cdea3891 Mon Sep 17 00:00:00 2001 From: Keshav Sharma Date: Wed, 13 Oct 2021 04:58:59 -0700 Subject: [PATCH 12/12] Add Pandas in R --- .../07-python/R(Bonus Lesson)/Notebook.ipynb | 2249 ----------------- .../07-python/R/notebook.ipynb | 2131 ++++++++++++++++ 2 files changed, 2131 insertions(+), 2249 deletions(-) delete mode 100644 2-Working-With-Data/07-python/R(Bonus Lesson)/Notebook.ipynb create mode 100644 2-Working-With-Data/07-python/R/notebook.ipynb diff --git a/2-Working-With-Data/07-python/R(Bonus Lesson)/Notebook.ipynb b/2-Working-With-Data/07-python/R(Bonus Lesson)/Notebook.ipynb deleted file mode 100644 index eec4910..0000000 --- a/2-Working-With-Data/07-python/R(Bonus Lesson)/Notebook.ipynb +++ /dev/null @@ -1,2249 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "304296e3", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Attaching package: 'dplyr'\n", - "\n", - "\n", - "The following objects are masked from 'package:stats':\n", - "\n", - " filter, lag\n", - "\n", - "\n", - "The following objects are masked from 'package:base':\n", - "\n", - " intersect, setdiff, setequal, union\n", - "\n", - "\n", - "-- \u001b[1mAttaching packages\u001b[22m ------------------------------------------------------------------------------- tidyverse 1.3.1 --\n", - "\n", - "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.5 \u001b[32mv\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.4\n", - "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.1.5 \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n", - "\u001b[32mv\u001b[39m \u001b[34mtidyr \u001b[39m 1.1.4 \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1\n", - "\u001b[32mv\u001b[39m \u001b[34mreadr \u001b[39m 2.0.2 \n", - "\n", - "-- \u001b[1mConflicts\u001b[22m ---------------------------------------------------------------------------------- tidyverse_conflicts() --\n", - "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n", - "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n", - "\n", - "\n", - "Attaching package: 'lubridate'\n", - "\n", - "\n", - "The following objects are masked from 'package:base':\n", - "\n", - " date, intersect, setdiff, union\n", - "\n", - "\n", - "\n", - "Attaching package: 'zoo'\n", - "\n", - "\n", - "The following objects are masked from 'package:base':\n", - "\n", - " as.Date, as.Date.numeric\n", - "\n", - "\n", - "\n", - "Attaching package: 'xts'\n", - "\n", - "\n", - "The following objects are masked from 'package:dplyr':\n", - "\n", - " first, last\n", - "\n", - "\n" - ] - } - ], - "source": [ - "library(dplyr)\n", - "library(tidyverse)\n", - "library('lubridate')\n", - "library('zoo')\n", - "library('xts')" - ] - }, - { - "cell_type": "markdown", - "id": "d786e051", - "metadata": {}, - "source": [ - "## Series" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f659f553", - "metadata": {}, - "outputs": [], - "source": [ - "a<- 1:9" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9acc193d", - "metadata": {}, - "outputs": [], - "source": [ - "b = c(\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f577ec14", - "metadata": {}, - "outputs": [], - "source": [ - "a1 = length(a)\n", - "b1 = length(b)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "31e069a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " a\n", - "1 1\n", - "2 2\n", - "3 3\n", - "4 4\n", - "5 5\n", - "6 6\n", - "7 7\n", - "8 8\n", - "9 9\n" - ] - } - ], - "source": [ - "a = data.frame(a,row.names = c(1:a1))\n", - "print(a)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "29ce166e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " b\n", - "1 I\n", - "2 like\n", - "3 to\n", - "4 use\n", - "5 Python\n", - "6 and\n", - "7 Pandas\n", - "8 very\n", - "9 much\n" - ] - } - ], - "source": [ - "b = data.frame(b,row.names = c(1:b1))\n", - "print(b)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "eeb683c7", - "metadata": {}, - "outputs": [], - "source": [ - "library('ggplot2')" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "e7788ca1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] \"length of index is 366\"\n" - ] - }, - { - "data": { - "image/png": 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zlzVDpfPeddHDpC1yxxAA+6AW10+koPAbS1c1tAOyC7PKD7d3G0AtoEWz4S0JUZ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\n6KPkVWwT13prruHWSe+7aOB0MAH6fayaJllPNTb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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 360, - "width": 720 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "start_date <- mdy(\"Jan 1, 2020\")\n", - "end_date <- mdy(\"Dec 31, 2020\")\n", - "idx = seq(start_date,end_date,by ='day')\n", - "print(paste(\"length of index is \",length(idx)))\n", - "size = length(idx)\n", - "sales = runif(366,min=25,max=50)\n", - "sold_items <- data.frame(row.names=idx[0:size],sales)\n", - "ggplot(sold_items,aes(x=idx,y=sales)) + geom_point(color = \"firebrick\", shape = \"diamond\", size = 2) +\n", - " geom_line(color = \"firebrick\", size = .3)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "30747f7c", - "metadata": {}, - "outputs": [], - "source": [ - "library(repr)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "48f3e762", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "366" - ], - "text/latex": [ - "366" - ], - "text/markdown": [ - "366" - ], - "text/plain": [ - "[1] 366" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#changing plot size\n", - "options(repr.plot.width = 12,repr.plot.height=6)\n", - "size" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "abe41544", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 53 × 1
additional_product
<dbl>
2020-01-0110
2020-01-0810
2020-01-1510
2020-01-2210
2020-01-2910
2020-02-0510
2020-02-1210
2020-02-1910
2020-02-2610
2020-03-0410
2020-03-1110
2020-03-1810
2020-03-2510
2020-04-0110
2020-04-0810
2020-04-1510
2020-04-2210
2020-04-2910
2020-05-0610
2020-05-1310
2020-05-2010
2020-05-2710
2020-06-0310
2020-06-1010
2020-06-1710
2020-06-2410
2020-07-0110
2020-07-0810
2020-07-1510
2020-07-2210
2020-07-2910
2020-08-0510
2020-08-1210
2020-08-1910
2020-08-2610
2020-09-0210
2020-09-0910
2020-09-1610
2020-09-2310
2020-09-3010
2020-10-0710
2020-10-1410
2020-10-2110
2020-10-2810
2020-11-0410
2020-11-1110
2020-11-1810
2020-11-2510
2020-12-0210
2020-12-0910
2020-12-1610
2020-12-2310
2020-12-3010
\n" - ], - "text/latex": [ - "A data.frame: 53 × 1\n", - "\\begin{tabular}{r|l}\n", - " & additional\\_product\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t2020-01-01 & 10\\\\\n", - "\t2020-01-08 & 10\\\\\n", - "\t2020-01-15 & 10\\\\\n", - "\t2020-01-22 & 10\\\\\n", - "\t2020-01-29 & 10\\\\\n", - "\t2020-02-05 & 10\\\\\n", - "\t2020-02-12 & 10\\\\\n", - "\t2020-02-19 & 10\\\\\n", - "\t2020-02-26 & 10\\\\\n", - "\t2020-03-04 & 10\\\\\n", - "\t2020-03-11 & 10\\\\\n", - "\t2020-03-18 & 10\\\\\n", - "\t2020-03-25 & 10\\\\\n", - "\t2020-04-01 & 10\\\\\n", - "\t2020-04-08 & 10\\\\\n", - "\t2020-04-15 & 10\\\\\n", - "\t2020-04-22 & 10\\\\\n", - "\t2020-04-29 & 10\\\\\n", - "\t2020-05-06 & 10\\\\\n", - "\t2020-05-13 & 10\\\\\n", - "\t2020-05-20 & 10\\\\\n", - "\t2020-05-27 & 10\\\\\n", - "\t2020-06-03 & 10\\\\\n", - "\t2020-06-10 & 10\\\\\n", - "\t2020-06-17 & 10\\\\\n", - "\t2020-06-24 & 10\\\\\n", - "\t2020-07-01 & 10\\\\\n", - "\t2020-07-08 & 10\\\\\n", - "\t2020-07-15 & 10\\\\\n", - "\t2020-07-22 & 10\\\\\n", - "\t2020-07-29 & 10\\\\\n", - "\t2020-08-05 & 10\\\\\n", - "\t2020-08-12 & 10\\\\\n", - "\t2020-08-19 & 10\\\\\n", - "\t2020-08-26 & 10\\\\\n", - "\t2020-09-02 & 10\\\\\n", - "\t2020-09-09 & 10\\\\\n", - "\t2020-09-16 & 10\\\\\n", - "\t2020-09-23 & 10\\\\\n", - "\t2020-09-30 & 10\\\\\n", - "\t2020-10-07 & 10\\\\\n", - "\t2020-10-14 & 10\\\\\n", - "\t2020-10-21 & 10\\\\\n", - "\t2020-10-28 & 10\\\\\n", - "\t2020-11-04 & 10\\\\\n", - "\t2020-11-11 & 10\\\\\n", - "\t2020-11-18 & 10\\\\\n", - "\t2020-11-25 & 10\\\\\n", - "\t2020-12-02 & 10\\\\\n", - "\t2020-12-09 & 10\\\\\n", - "\t2020-12-16 & 10\\\\\n", - "\t2020-12-23 & 10\\\\\n", - "\t2020-12-30 & 10\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 53 × 1\n", - "\n", - "| | additional_product <dbl> |\n", - "|---|---|\n", - "| 2020-01-01 | 10 |\n", - "| 2020-01-08 | 10 |\n", - "| 2020-01-15 | 10 |\n", - "| 2020-01-22 | 10 |\n", - "| 2020-01-29 | 10 |\n", - "| 2020-02-05 | 10 |\n", - "| 2020-02-12 | 10 |\n", - "| 2020-02-19 | 10 |\n", - "| 2020-02-26 | 10 |\n", - "| 2020-03-04 | 10 |\n", - "| 2020-03-11 | 10 |\n", - "| 2020-03-18 | 10 |\n", - "| 2020-03-25 | 10 |\n", - "| 2020-04-01 | 10 |\n", - "| 2020-04-08 | 10 |\n", - "| 2020-04-15 | 10 |\n", - "| 2020-04-22 | 10 |\n", - "| 2020-04-29 | 10 |\n", - "| 2020-05-06 | 10 |\n", - "| 2020-05-13 | 10 |\n", - "| 2020-05-20 | 10 |\n", - "| 2020-05-27 | 10 |\n", - "| 2020-06-03 | 10 |\n", - "| 2020-06-10 | 10 |\n", - "| 2020-06-17 | 10 |\n", - "| 2020-06-24 | 10 |\n", - "| 2020-07-01 | 10 |\n", - "| 2020-07-08 | 10 |\n", - "| 2020-07-15 | 10 |\n", - "| 2020-07-22 | 10 |\n", - "| 2020-07-29 | 10 |\n", - "| 2020-08-05 | 10 |\n", - "| 2020-08-12 | 10 |\n", - "| 2020-08-19 | 10 |\n", - "| 2020-08-26 | 10 |\n", - "| 2020-09-02 | 10 |\n", - "| 2020-09-09 | 10 |\n", - "| 2020-09-16 | 10 |\n", - "| 2020-09-23 | 10 |\n", - "| 2020-09-30 | 10 |\n", - "| 2020-10-07 | 10 |\n", - "| 2020-10-14 | 10 |\n", - "| 2020-10-21 | 10 |\n", - "| 2020-10-28 | 10 |\n", - "| 2020-11-04 | 10 |\n", - "| 2020-11-11 | 10 |\n", - "| 2020-11-18 | 10 |\n", - "| 2020-11-25 | 10 |\n", - "| 2020-12-02 | 10 |\n", - "| 2020-12-09 | 10 |\n", - "| 2020-12-16 | 10 |\n", - "| 2020-12-23 | 10 |\n", - "| 2020-12-30 | 10 |\n", - "\n" - ], - "text/plain": [ - " additional_product\n", - "2020-01-01 10 \n", - "2020-01-08 10 \n", - "2020-01-15 10 \n", - "2020-01-22 10 \n", - "2020-01-29 10 \n", - "2020-02-05 10 \n", - "2020-02-12 10 \n", - "2020-02-19 10 \n", - "2020-02-26 10 \n", - "2020-03-04 10 \n", - "2020-03-11 10 \n", - "2020-03-18 10 \n", - "2020-03-25 10 \n", - "2020-04-01 10 \n", - "2020-04-08 10 \n", - "2020-04-15 10 \n", - "2020-04-22 10 \n", - "2020-04-29 10 \n", - "2020-05-06 10 \n", - "2020-05-13 10 \n", - "2020-05-20 10 \n", - "2020-05-27 10 \n", - "2020-06-03 10 \n", - "2020-06-10 10 \n", - "2020-06-17 10 \n", - "2020-06-24 10 \n", - "2020-07-01 10 \n", - "2020-07-08 10 \n", - "2020-07-15 10 \n", - "2020-07-22 10 \n", - "2020-07-29 10 \n", - "2020-08-05 10 \n", - "2020-08-12 10 \n", - "2020-08-19 10 \n", - "2020-08-26 10 \n", - "2020-09-02 10 \n", - "2020-09-09 10 \n", - "2020-09-16 10 \n", - "2020-09-23 10 \n", - "2020-09-30 10 \n", - "2020-10-07 10 \n", - "2020-10-14 10 \n", - "2020-10-21 10 \n", - "2020-10-28 10 \n", - "2020-11-04 10 \n", - "2020-11-11 10 \n", - "2020-11-18 10 \n", - "2020-11-25 10 \n", - "2020-12-02 10 \n", - "2020-12-09 10 \n", - "2020-12-16 10 \n", - "2020-12-23 10 \n", - "2020-12-30 10 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - 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A data.frame: 366 × 1
total
<dbl>
2020-01-0154.37099
2020-01-02 NA
2020-01-03 NA
2020-01-04 NA
2020-01-05 NA
2020-01-06 NA
2020-01-07 NA
2020-01-0851.99181
2020-01-09 NA
2020-01-10 NA
2020-01-11 NA
2020-01-12 NA
2020-01-13 NA
2020-01-14 NA
2020-01-1547.57204
2020-01-16 NA
2020-01-17 NA
2020-01-18 NA
2020-01-19 NA
2020-01-20 NA
2020-01-21 NA
2020-01-2250.46082
2020-01-23 NA
2020-01-24 NA
2020-01-25 NA
2020-01-26 NA
2020-01-27 NA
2020-01-28 NA
2020-01-2955.32913
2020-01-30 NA
......
2020-12-0247.63211
2020-12-03 NA
2020-12-04 NA
2020-12-05 NA
2020-12-06 NA
2020-12-07 NA
2020-12-08 NA
2020-12-0949.09786
2020-12-10 NA
2020-12-11 NA
2020-12-12 NA
2020-12-13 NA
2020-12-14 NA
2020-12-15 NA
2020-12-1655.27396
2020-12-17 NA
2020-12-18 NA
2020-12-19 NA
2020-12-20 NA
2020-12-21 NA
2020-12-22 NA
2020-12-2346.30954
2020-12-24 NA
2020-12-25 NA
2020-12-26 NA
2020-12-27 NA
2020-12-28 NA
2020-12-29 NA
2020-12-3043.08600
2020-12-31 NA
\n" - ], - "text/latex": [ - "A data.frame: 366 × 1\n", - "\\begin{tabular}{r|l}\n", - " & total\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t2020-01-01 & 54.37099\\\\\n", - "\t2020-01-02 & NA\\\\\n", - "\t2020-01-03 & NA\\\\\n", - "\t2020-01-04 & NA\\\\\n", - "\t2020-01-05 & NA\\\\\n", - "\t2020-01-06 & NA\\\\\n", - "\t2020-01-07 & NA\\\\\n", - "\t2020-01-08 & 51.99181\\\\\n", - "\t2020-01-09 & NA\\\\\n", - "\t2020-01-10 & NA\\\\\n", - "\t2020-01-11 & NA\\\\\n", - "\t2020-01-12 & NA\\\\\n", - "\t2020-01-13 & NA\\\\\n", - "\t2020-01-14 & NA\\\\\n", - "\t2020-01-15 & 47.57204\\\\\n", - "\t2020-01-16 & NA\\\\\n", - "\t2020-01-17 & NA\\\\\n", - "\t2020-01-18 & NA\\\\\n", - "\t2020-01-19 & NA\\\\\n", - "\t2020-01-20 & NA\\\\\n", - "\t2020-01-21 & NA\\\\\n", - "\t2020-01-22 & 50.46082\\\\\n", - "\t2020-01-23 & NA\\\\\n", - "\t2020-01-24 & NA\\\\\n", - "\t2020-01-25 & NA\\\\\n", - "\t2020-01-26 & NA\\\\\n", - "\t2020-01-27 & NA\\\\\n", - "\t2020-01-28 & NA\\\\\n", - "\t2020-01-29 & 55.32913\\\\\n", - "\t2020-01-30 & NA\\\\\n", - "\t... & ...\\\\\n", - "\t2020-12-02 & 47.63211\\\\\n", - "\t2020-12-03 & NA\\\\\n", - "\t2020-12-04 & NA\\\\\n", - "\t2020-12-05 & NA\\\\\n", - "\t2020-12-06 & NA\\\\\n", - "\t2020-12-07 & NA\\\\\n", - "\t2020-12-08 & NA\\\\\n", - "\t2020-12-09 & 49.09786\\\\\n", - "\t2020-12-10 & NA\\\\\n", - "\t2020-12-11 & NA\\\\\n", - "\t2020-12-12 & NA\\\\\n", - "\t2020-12-13 & NA\\\\\n", - "\t2020-12-14 & NA\\\\\n", - "\t2020-12-15 & NA\\\\\n", - "\t2020-12-16 & 55.27396\\\\\n", - "\t2020-12-17 & NA\\\\\n", - "\t2020-12-18 & NA\\\\\n", - "\t2020-12-19 & NA\\\\\n", - "\t2020-12-20 & NA\\\\\n", - "\t2020-12-21 & NA\\\\\n", - "\t2020-12-22 & NA\\\\\n", - "\t2020-12-23 & 46.30954\\\\\n", - "\t2020-12-24 & NA\\\\\n", - "\t2020-12-25 & NA\\\\\n", - "\t2020-12-26 & NA\\\\\n", - "\t2020-12-27 & NA\\\\\n", - "\t2020-12-28 & NA\\\\\n", - "\t2020-12-29 & NA\\\\\n", - "\t2020-12-30 & 43.08600\\\\\n", - "\t2020-12-31 & NA\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 366 × 1\n", - "\n", - "| | total <dbl> |\n", - "|---|---|\n", - "| 2020-01-01 | 54.37099 |\n", - "| 2020-01-02 | NA |\n", - "| 2020-01-03 | NA |\n", - "| 2020-01-04 | NA |\n", - "| 2020-01-05 | NA |\n", - "| 2020-01-06 | NA |\n", - "| 2020-01-07 | NA |\n", - "| 2020-01-08 | 51.99181 |\n", - "| 2020-01-09 | NA |\n", - "| 2020-01-10 | NA |\n", - "| 2020-01-11 | NA |\n", - "| 2020-01-12 | NA |\n", - "| 2020-01-13 | NA |\n", - "| 2020-01-14 | NA |\n", - "| 2020-01-15 | 47.57204 |\n", - "| 2020-01-16 | NA |\n", - "| 2020-01-17 | NA |\n", - "| 2020-01-18 | NA |\n", - "| 2020-01-19 | NA |\n", - "| 2020-01-20 | NA |\n", - "| 2020-01-21 | NA |\n", - "| 2020-01-22 | 50.46082 |\n", - "| 2020-01-23 | NA |\n", - "| 2020-01-24 | NA |\n", - "| 2020-01-25 | NA |\n", - "| 2020-01-26 | NA |\n", - "| 2020-01-27 | NA |\n", - "| 2020-01-28 | NA |\n", - "| 2020-01-29 | 55.32913 |\n", - "| 2020-01-30 | NA |\n", - "| ... | ... |\n", - "| 2020-12-02 | 47.63211 |\n", - "| 2020-12-03 | NA |\n", - "| 2020-12-04 | NA |\n", - "| 2020-12-05 | NA |\n", - "| 2020-12-06 | NA |\n", - "| 2020-12-07 | NA |\n", - "| 2020-12-08 | NA |\n", - "| 2020-12-09 | 49.09786 |\n", - "| 2020-12-10 | NA |\n", - "| 2020-12-11 | NA |\n", - "| 2020-12-12 | NA |\n", - "| 2020-12-13 | NA |\n", - "| 2020-12-14 | NA |\n", - "| 2020-12-15 | NA |\n", - "| 2020-12-16 | 55.27396 |\n", - "| 2020-12-17 | NA |\n", - "| 2020-12-18 | NA |\n", - "| 2020-12-19 | NA |\n", - "| 2020-12-20 | NA |\n", - "| 2020-12-21 | NA |\n", - "| 2020-12-22 | NA |\n", - "| 2020-12-23 | 46.30954 |\n", - "| 2020-12-24 | NA |\n", - "| 2020-12-25 | NA |\n", - "| 2020-12-26 | NA |\n", - "| 2020-12-27 | NA |\n", - "| 2020-12-28 | NA |\n", - "| 2020-12-29 | NA |\n", - "| 2020-12-30 | 43.08600 |\n", - "| 2020-12-31 | NA |\n", - "\n" - ], - "text/plain": [ - " total \n", - "2020-01-01 54.37099\n", - "2020-01-02 NA\n", - "2020-01-03 NA\n", - "2020-01-04 NA\n", - "2020-01-05 NA\n", - "2020-01-06 NA\n", - "2020-01-07 NA\n", - "2020-01-08 51.99181\n", - "2020-01-09 NA\n", - "2020-01-10 NA\n", - "2020-01-11 NA\n", - "2020-01-12 NA\n", - "2020-01-13 NA\n", - "2020-01-14 NA\n", - "2020-01-15 47.57204\n", - "2020-01-16 NA\n", - "2020-01-17 NA\n", - "2020-01-18 NA\n", - "2020-01-19 NA\n", - "2020-01-20 NA\n", - "2020-01-21 NA\n", - "2020-01-22 50.46082\n", - "2020-01-23 NA\n", - "2020-01-24 NA\n", - "2020-01-25 NA\n", - "2020-01-26 NA\n", - "2020-01-27 NA\n", - "2020-01-28 NA\n", - "2020-01-29 55.32913\n", - "2020-01-30 NA\n", - "... ... \n", - "2020-12-02 47.63211\n", - "2020-12-03 NA\n", - "2020-12-04 NA\n", - "2020-12-05 NA\n", - "2020-12-06 NA\n", - "2020-12-07 NA\n", - "2020-12-08 NA\n", - "2020-12-09 49.09786\n", - "2020-12-10 NA\n", - "2020-12-11 NA\n", - "2020-12-12 NA\n", - "2020-12-13 NA\n", - "2020-12-14 NA\n", - "2020-12-15 NA\n", - "2020-12-16 55.27396\n", - "2020-12-17 NA\n", - "2020-12-18 NA\n", - "2020-12-19 NA\n", - "2020-12-20 NA\n", - "2020-12-21 NA\n", - "2020-12-22 NA\n", - "2020-12-23 46.30954\n", - "2020-12-24 NA\n", - "2020-12-25 NA\n", - "2020-12-26 NA\n", - "2020-12-27 NA\n", - "2020-12-28 NA\n", - "2020-12-29 NA\n", - "2020-12-30 43.08600\n", - "2020-12-31 NA" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "index = seq(start_date,end_date,by = 'week')\n", - "sz = length(index)\n", - "additional_product <- rep(10,53)\n", - "additional_items <- data.frame(row.names = index[0:sz],additional_product)\n", - "additional_items\n", - "# we are merging two dataframe so that we can add\n", - "additional_item = merge(additional_items,sold_items, by = 0, all = TRUE)[-1] \n", - "total = data.frame(row.names=idx[0:size],additional_item$additional_product + additional_item$sales)\n", - "colnames(total) = c('total')\n", - "total" - ] - }, - { - "cell_type": "markdown", - "id": "cb00ff6e", - "metadata": {}, - "source": [ - "\n", - "### As you can see, we are having problems here, because in the weekly series non-mentioned days are considered to be missing (NaN), if we add NaN to a number gives us NaN. In order todo addition, we need to fill the value 0 of additional_items while adding series: \n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "387cb4c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 366 × 1
total
<dbl>
2020-01-0154.37099
2020-01-0227.85566
2020-01-0338.29037
2020-01-0426.72367
2020-01-0537.93330
2020-01-0638.70961
2020-01-0728.36133
2020-01-0851.99181
2020-01-0944.76469
2020-01-1026.54058
2020-01-1131.70327
2020-01-1237.83133
2020-01-1347.45717
2020-01-1431.78909
2020-01-1547.57204
2020-01-1643.79414
2020-01-1746.35117
2020-01-1840.18666
2020-01-1934.65751
2020-01-2038.09664
2020-01-2136.76733
2020-01-2250.46082
2020-01-2339.62032
2020-01-2444.56143
2020-01-2526.45657
2020-01-2645.48089
2020-01-2749.03699
2020-01-2846.05290
2020-01-2955.32913
2020-01-3044.83293
......
2020-12-0247.63211
2020-12-0349.44946
2020-12-0428.07843
2020-12-0549.80941
2020-12-0637.43425
2020-12-0732.36442
2020-12-0837.96258
2020-12-0949.09786
2020-12-1043.08738
2020-12-1136.93554
2020-12-1233.44147
2020-12-1335.24588
2020-12-1447.67855
2020-12-1541.84728
2020-12-1655.27396
2020-12-1732.23195
2020-12-1830.18391
2020-12-1939.27033
2020-12-2045.13756
2020-12-2143.00961
2020-12-2243.69411
2020-12-2346.30954
2020-12-2437.42397
2020-12-2535.94406
2020-12-2645.00482
2020-12-2731.81550
2020-12-2832.69022
2020-12-2931.52063
2020-12-3043.08600
2020-12-3139.28333
\n" - ], - "text/latex": [ - "A data.frame: 366 × 1\n", - "\\begin{tabular}{r|l}\n", - " & total\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t2020-01-01 & 54.37099\\\\\n", - "\t2020-01-02 & 27.85566\\\\\n", - "\t2020-01-03 & 38.29037\\\\\n", - "\t2020-01-04 & 26.72367\\\\\n", - "\t2020-01-05 & 37.93330\\\\\n", - "\t2020-01-06 & 38.70961\\\\\n", - "\t2020-01-07 & 28.36133\\\\\n", - "\t2020-01-08 & 51.99181\\\\\n", - "\t2020-01-09 & 44.76469\\\\\n", - "\t2020-01-10 & 26.54058\\\\\n", - "\t2020-01-11 & 31.70327\\\\\n", - "\t2020-01-12 & 37.83133\\\\\n", - "\t2020-01-13 & 47.45717\\\\\n", - "\t2020-01-14 & 31.78909\\\\\n", - "\t2020-01-15 & 47.57204\\\\\n", - "\t2020-01-16 & 43.79414\\\\\n", - "\t2020-01-17 & 46.35117\\\\\n", - "\t2020-01-18 & 40.18666\\\\\n", - "\t2020-01-19 & 34.65751\\\\\n", - "\t2020-01-20 & 38.09664\\\\\n", - "\t2020-01-21 & 36.76733\\\\\n", - "\t2020-01-22 & 50.46082\\\\\n", - "\t2020-01-23 & 39.62032\\\\\n", - "\t2020-01-24 & 44.56143\\\\\n", - "\t2020-01-25 & 26.45657\\\\\n", - "\t2020-01-26 & 45.48089\\\\\n", - "\t2020-01-27 & 49.03699\\\\\n", - "\t2020-01-28 & 46.05290\\\\\n", - "\t2020-01-29 & 55.32913\\\\\n", - "\t2020-01-30 & 44.83293\\\\\n", - "\t... & ...\\\\\n", - "\t2020-12-02 & 47.63211\\\\\n", - "\t2020-12-03 & 49.44946\\\\\n", - "\t2020-12-04 & 28.07843\\\\\n", - "\t2020-12-05 & 49.80941\\\\\n", - "\t2020-12-06 & 37.43425\\\\\n", - "\t2020-12-07 & 32.36442\\\\\n", - "\t2020-12-08 & 37.96258\\\\\n", - "\t2020-12-09 & 49.09786\\\\\n", - "\t2020-12-10 & 43.08738\\\\\n", - "\t2020-12-11 & 36.93554\\\\\n", - "\t2020-12-12 & 33.44147\\\\\n", - "\t2020-12-13 & 35.24588\\\\\n", - "\t2020-12-14 & 47.67855\\\\\n", - "\t2020-12-15 & 41.84728\\\\\n", - "\t2020-12-16 & 55.27396\\\\\n", - "\t2020-12-17 & 32.23195\\\\\n", - "\t2020-12-18 & 30.18391\\\\\n", - "\t2020-12-19 & 39.27033\\\\\n", - "\t2020-12-20 & 45.13756\\\\\n", - "\t2020-12-21 & 43.00961\\\\\n", - "\t2020-12-22 & 43.69411\\\\\n", - "\t2020-12-23 & 46.30954\\\\\n", - "\t2020-12-24 & 37.42397\\\\\n", - "\t2020-12-25 & 35.94406\\\\\n", - "\t2020-12-26 & 45.00482\\\\\n", - "\t2020-12-27 & 31.81550\\\\\n", - "\t2020-12-28 & 32.69022\\\\\n", - "\t2020-12-29 & 31.52063\\\\\n", - "\t2020-12-30 & 43.08600\\\\\n", - "\t2020-12-31 & 39.28333\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 366 × 1\n", - "\n", - "| | total <dbl> |\n", - "|---|---|\n", - "| 2020-01-01 | 54.37099 |\n", - "| 2020-01-02 | 27.85566 |\n", - "| 2020-01-03 | 38.29037 |\n", - "| 2020-01-04 | 26.72367 |\n", - "| 2020-01-05 | 37.93330 |\n", - "| 2020-01-06 | 38.70961 |\n", - "| 2020-01-07 | 28.36133 |\n", - "| 2020-01-08 | 51.99181 |\n", - "| 2020-01-09 | 44.76469 |\n", - "| 2020-01-10 | 26.54058 |\n", - "| 2020-01-11 | 31.70327 |\n", - "| 2020-01-12 | 37.83133 |\n", - "| 2020-01-13 | 47.45717 |\n", - "| 2020-01-14 | 31.78909 |\n", - "| 2020-01-15 | 47.57204 |\n", - "| 2020-01-16 | 43.79414 |\n", - "| 2020-01-17 | 46.35117 |\n", - "| 2020-01-18 | 40.18666 |\n", - "| 2020-01-19 | 34.65751 |\n", - "| 2020-01-20 | 38.09664 |\n", - "| 2020-01-21 | 36.76733 |\n", - "| 2020-01-22 | 50.46082 |\n", - "| 2020-01-23 | 39.62032 |\n", - "| 2020-01-24 | 44.56143 |\n", - "| 2020-01-25 | 26.45657 |\n", - "| 2020-01-26 | 45.48089 |\n", - "| 2020-01-27 | 49.03699 |\n", - "| 2020-01-28 | 46.05290 |\n", - "| 2020-01-29 | 55.32913 |\n", - "| 2020-01-30 | 44.83293 |\n", - "| ... | ... |\n", - "| 2020-12-02 | 47.63211 |\n", - "| 2020-12-03 | 49.44946 |\n", - "| 2020-12-04 | 28.07843 |\n", - "| 2020-12-05 | 49.80941 |\n", - "| 2020-12-06 | 37.43425 |\n", - "| 2020-12-07 | 32.36442 |\n", - "| 2020-12-08 | 37.96258 |\n", - "| 2020-12-09 | 49.09786 |\n", - "| 2020-12-10 | 43.08738 |\n", - "| 2020-12-11 | 36.93554 |\n", - "| 2020-12-12 | 33.44147 |\n", - "| 2020-12-13 | 35.24588 |\n", - "| 2020-12-14 | 47.67855 |\n", - "| 2020-12-15 | 41.84728 |\n", - "| 2020-12-16 | 55.27396 |\n", - "| 2020-12-17 | 32.23195 |\n", - "| 2020-12-18 | 30.18391 |\n", - "| 2020-12-19 | 39.27033 |\n", - "| 2020-12-20 | 45.13756 |\n", - "| 2020-12-21 | 43.00961 |\n", - "| 2020-12-22 | 43.69411 |\n", - "| 2020-12-23 | 46.30954 |\n", - "| 2020-12-24 | 37.42397 |\n", - "| 2020-12-25 | 35.94406 |\n", - "| 2020-12-26 | 45.00482 |\n", - "| 2020-12-27 | 31.81550 |\n", - "| 2020-12-28 | 32.69022 |\n", - "| 2020-12-29 | 31.52063 |\n", - "| 2020-12-30 | 43.08600 |\n", - "| 2020-12-31 | 39.28333 |\n", - "\n" - ], - "text/plain": [ - " total \n", - "2020-01-01 54.37099\n", - "2020-01-02 27.85566\n", - "2020-01-03 38.29037\n", - "2020-01-04 26.72367\n", - "2020-01-05 37.93330\n", - "2020-01-06 38.70961\n", - "2020-01-07 28.36133\n", - "2020-01-08 51.99181\n", - "2020-01-09 44.76469\n", - "2020-01-10 26.54058\n", - "2020-01-11 31.70327\n", - "2020-01-12 37.83133\n", - "2020-01-13 47.45717\n", - "2020-01-14 31.78909\n", - "2020-01-15 47.57204\n", - "2020-01-16 43.79414\n", - "2020-01-17 46.35117\n", - "2020-01-18 40.18666\n", - "2020-01-19 34.65751\n", - "2020-01-20 38.09664\n", - "2020-01-21 36.76733\n", - "2020-01-22 50.46082\n", - "2020-01-23 39.62032\n", - "2020-01-24 44.56143\n", - "2020-01-25 26.45657\n", - "2020-01-26 45.48089\n", - "2020-01-27 49.03699\n", - "2020-01-28 46.05290\n", - "2020-01-29 55.32913\n", - "2020-01-30 44.83293\n", - "... ... \n", - "2020-12-02 47.63211\n", - "2020-12-03 49.44946\n", - "2020-12-04 28.07843\n", - "2020-12-05 49.80941\n", - "2020-12-06 37.43425\n", - "2020-12-07 32.36442\n", - "2020-12-08 37.96258\n", - "2020-12-09 49.09786\n", - "2020-12-10 43.08738\n", - "2020-12-11 36.93554\n", - "2020-12-12 33.44147\n", - "2020-12-13 35.24588\n", - "2020-12-14 47.67855\n", - "2020-12-15 41.84728\n", - "2020-12-16 55.27396\n", - "2020-12-17 32.23195\n", - "2020-12-18 30.18391\n", - "2020-12-19 39.27033\n", - "2020-12-20 45.13756\n", - "2020-12-21 43.00961\n", - "2020-12-22 43.69411\n", - "2020-12-23 46.30954\n", - "2020-12-24 37.42397\n", - "2020-12-25 35.94406\n", - "2020-12-26 45.00482\n", - "2020-12-27 31.81550\n", - "2020-12-28 32.69022\n", - "2020-12-29 31.52063\n", - "2020-12-30 43.08600\n", - "2020-12-31 39.28333" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "additional_item[is.na(additional_item)] = 0\n", - "total = data.frame(row.names=idx[0:size],additional_item$additional_product + additional_item$sales)\n", - "colnames(total) = c('total')\n", - "total" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "bdb60236", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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tOg7Wr5SBp0+aNOUoN+VJB+LgA4gc8PjihYgo6QgGQSgClieA0af/IeKl8g8GvQ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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 360, - "width": 720 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "ggplot(total,aes(x=idx,y=total)) + geom_point(color = \"firebrick\", shape = \"diamond\", size = 2) +\n", - " geom_line(color = \"firebrick\", linetype = \"dotted\", size = .3)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "294dde87", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n" - ], - "text/latex": [ - "\\begin{enumerate*}\n", - "\\item 2020-01-01\n", - "\\item 2020-02-01\n", - "\\item 2020-03-01\n", - "\\item 2020-04-01\n", - "\\item 2020-05-01\n", - "\\item 2020-06-01\n", - "\\item 2020-07-01\n", - "\\item 2020-08-01\n", - "\\item 2020-09-01\n", - "\\item 2020-10-01\n", - "\\item 2020-11-01\n", - "\\item 2020-12-01\n", - "\\end{enumerate*}\n" - ], - "text/markdown": [ - "1. 2020-01-01\n", - "2. 2020-02-01\n", - "3. 2020-03-01\n", - "4. 2020-04-01\n", - "5. 2020-05-01\n", - "6. 2020-06-01\n", - "7. 2020-07-01\n", - "8. 2020-08-01\n", - "9. 2020-09-01\n", - "10. 2020-10-01\n", - "11. 2020-11-01\n", - "12. 2020-12-01\n", - "\n", - "\n" - ], - "text/plain": [ - " [1] \"2020-01-01\" \"2020-02-01\" \"2020-03-01\" \"2020-04-01\" \"2020-05-01\"\n", - " [6] \"2020-06-01\" \"2020-07-01\" \"2020-08-01\" \"2020-09-01\" \"2020-10-01\"\n", - "[11] \"2020-11-01\" \"2020-12-01\"" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "index = seq(start_date,end_date,by ='month')\n", - "index" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "7542d95e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " total\n", - "2020-01-31 40.12725\n", - "2020-02-29 36.38809\n", - "2020-03-31 36.63476\n", - "2020-04-30 36.69513\n", - "2020-05-31 40.12267\n", - "2020-06-30 38.04770\n", - "2020-07-31 39.38036\n", - "2020-08-31 37.60016\n", - "2020-09-30 37.76963\n", - "2020-10-31 38.57457\n", - "2020-11-30 38.38480\n", - "2020-12-31 39.73959" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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B8ooJdFQE81QJeLgA4X2wcK6GUR0FMN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\nFo1Pe09AL+eQPQ9Z85Q9D1nTovFp7wno5Ryy5yFrnrLnIWtaND7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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 360, - "width": 720 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "x<- as.xts(total, dateFormat =\"Date\")\n", - "(monthly<-apply.monthly(x,mean))\n", - "ggplot(monthly, aes(x=index, y=total)) + \n", - " geom_bar(stat = \"identity\", width=5) " - ] - }, - { - "cell_type": "markdown", - "id": "945feffd", - "metadata": {}, - "source": [ - "## DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "id": "d38f1754", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message in seq_len(head.end.idx):\n", - "\"first element used of 'length.out' argument\"\n", - "ERROR while rich displaying an object: Error in seq_len(head.end.idx): argument must be coercible to non-negative integer\n", - "\n", - "Traceback:\n", - "1. FUN(X[[i]], ...)\n", - "2. tryCatch(withCallingHandlers({\n", - " . if (!mime %in% names(repr::mime2repr)) \n", - " . stop(\"No repr_* for mimetype \", mime, \" in repr::mime2repr\")\n", - " . rpr <- repr::mime2repr[[mime]](obj)\n", - " . if (is.null(rpr)) \n", - " . return(NULL)\n", - " . prepare_content(is.raw(rpr), rpr)\n", - " . }, error = error_handler), error = outer_handler)\n", - "3. tryCatchList(expr, classes, parentenv, handlers)\n", - "4. tryCatchOne(expr, names, parentenv, handlers[[1L]])\n", - "5. doTryCatch(return(expr), name, parentenv, handler)\n", - "6. withCallingHandlers({\n", - " . if (!mime %in% names(repr::mime2repr)) \n", - " . stop(\"No repr_* for mimetype \", mime, \" in repr::mime2repr\")\n", - " . rpr <- repr::mime2repr[[mime]](obj)\n", - " . if (is.null(rpr)) \n", - " . return(NULL)\n", - " . prepare_content(is.raw(rpr), rpr)\n", - " . }, error = error_handler)\n", - "7. repr::mime2repr[[mime]](obj)\n", - "8. repr_html.help_files_with_topic(obj)\n", - "9. repr_help_files_with_topic_generic(obj, Rd2HTML)\n" - ] - } - ], - "source": [ - "?vector" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "88a435ec", - "metadata": {}, - "outputs": [], - "source": [ - "a = data.frame(a,row.names = c(1:a1))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c4e2a6c1", - "metadata": {}, - "outputs": [], - "source": [ - "b = data.frame(b,row.names = c(1:b1))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "2bb5177c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 9 × 2
ab
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A data.frame: 9 × 2
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A data.frame: 9 × 1
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A data.frame: 4 × 2
AB
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A data.frame: 1 × 2
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A data.frame: 9 × 3
ABDivA
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A data.frame: 9 × 4
ABDivALenB
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A data.frame: 5 × 4
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A tibble: 5 × 2
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A data.frame: 6 × 4
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}, - { - "cell_type": "code", - "execution_count": 25, - "id": "515c95b2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 420, - "width": 420 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "plot(df$A,type = 'o',xlab = \"no\",ylab = \"A\")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "41b872c9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 420, - "width": 420 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "barplot(df$A, ylab = 'A',xlab = 'no')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11001454", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "670db495", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "4.1.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/2-Working-With-Data/07-python/R/notebook.ipynb b/2-Working-With-Data/07-python/R/notebook.ipynb new file mode 100644 index 0000000..605b211 --- /dev/null +++ b/2-Working-With-Data/07-python/R/notebook.ipynb @@ -0,0 +1,2131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9f9b980c", + "metadata": {}, + "source": [ + "## Pandas Usecase in R\n", + " We have to use dplyr library to solve pandas usecase in R. We will start importing typical data science library" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "625abf4a", + "metadata": {}, + "outputs": [], + "source": [ + "options(warn=-1)\n", + "library(dplyr)\n", + "library(tidyverse)\n", + "library(lubridate)\n", + "library(zoo)\n", + "library(xts)\n", + "library('ggplot2')" + ] + }, + { + "cell_type": "markdown", + "id": "d786e051", + "metadata": {}, + "source": [ + "## Series" + ] + }, + { + "cell_type": "markdown", + "id": "0f47587a", + "metadata": {}, + "source": [ + " Series is like a list or 1D-array, but with index. All operations are index-aligned. Indexing of row in R we have to use row.names.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f659f553", + "metadata": {}, + "outputs": [], + "source": [ + "a<- 1:9" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "9acc193d", + "metadata": {}, + "outputs": [], + "source": [ + "b = c(\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f577ec14", + "metadata": {}, + "outputs": [], + "source": [ + "a1 = length(a)\n", + "b1 = length(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "31e069a0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " a\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + "5 5\n", + "6 6\n", + "7 7\n", + "8 8\n", + "9 9\n" + ] + } + ], + "source": [ + "a = data.frame(a,row.names = c(1:a1))\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "29ce166e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " b\n", + "1 I\n", + "2 like\n", + "3 to\n", + "4 use\n", + "5 Python\n", + "6 and\n", + "7 Pandas\n", + "8 very\n", + "9 much\n" + ] + } + ], + "source": [ + "b = data.frame(b,row.names = c(1:b1))\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "id": "a83abe74", + "metadata": {}, + "source": [ + " One of the frequent usages of series is time series. In time series, the index has a special structure - typically a range of dates or datetimes. The easiest way to create time series using the ts function. But we will try another way to implement time series. We have to use the lubridate library to create an index of dates using the seq function.\n", + " \n", + " Suppose we have a series that shows the amount of product bought every day, and we know that every Sunday we also need to take one additional item for ourselves. Here is how to model using series:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "eeb683c7", + "metadata": {}, + "outputs": [], + "source": [ + "# We will use ggplot2 for visualizing the data\n", + "# If you want to change the plot size repr library will be used\n", + "library(repr)\n", + "options(repr.plot.width = 12,repr.plot.height=6)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "e7788ca1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"length of index is 366\"\n" + ] + }, + { + "data": { + "image/png": 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HcDOwCdjGuSywP6eTPoaQC9bxeHGddPBvRpuzjGAFqPt3XLtUk26gF6fQGgZwX0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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "start_date <- mdy(\"Jan 1, 2020\")\n", + "end_date <- mdy(\"Dec 31, 2020\")\n", + "idx = seq(start_date,end_date,by ='day')\n", + "print(paste(\"length of index is \",length(idx)))\n", + "size = length(idx)\n", + "sales = runif(366,min=25,max=50)\n", + "sold_items <- data.frame(row.names=idx[0:size],sales)\n", + "ggplot(sold_items,aes(x=idx,y=sales)) + geom_point(color = \"firebrick\", shape = \"diamond\", size = 2) +\n", + " geom_line(color = \"firebrick\", size = .3)" + ] + }, + { + "cell_type": "markdown", + "id": "3f199e43", + "metadata": {}, + "source": [ + "We are merging additional_items and sold_items so that we can find the total no of products.\n", + "As you can see, we are having problems here to find the total, we are getting NaN value as in the weekly series non-mentioned days are considered to be missing (NaN) if we add NaN to a number that gives us NaN.\n", + "In order to do addition, we need to replace NAN with 0." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "abe41544", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 53 × 1
additional_product
<dbl>
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2020-10-2810
2020-11-0410
2020-11-1110
2020-11-1810
2020-11-2510
2020-12-0210
2020-12-0910
2020-12-1610
2020-12-2310
2020-12-3010
\n" + ], + "text/latex": [ + "A data.frame: 53 × 1\n", + "\\begin{tabular}{r|l}\n", + " & additional\\_product\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t2020-01-01 & 10\\\\\n", + "\t2020-01-08 & 10\\\\\n", + "\t2020-01-15 & 10\\\\\n", + "\t2020-01-22 & 10\\\\\n", + "\t2020-01-29 & 10\\\\\n", + "\t2020-02-05 & 10\\\\\n", + "\t2020-02-12 & 10\\\\\n", + "\t2020-02-19 & 10\\\\\n", + "\t2020-02-26 & 10\\\\\n", + "\t2020-03-04 & 10\\\\\n", + "\t2020-03-11 & 10\\\\\n", + "\t2020-03-18 & 10\\\\\n", + "\t2020-03-25 & 10\\\\\n", + "\t2020-04-01 & 10\\\\\n", + "\t2020-04-08 & 10\\\\\n", + "\t2020-04-15 & 10\\\\\n", + "\t2020-04-22 & 10\\\\\n", + "\t2020-04-29 & 10\\\\\n", + "\t2020-05-06 & 10\\\\\n", + "\t2020-05-13 & 10\\\\\n", + "\t2020-05-20 & 10\\\\\n", + "\t2020-05-27 & 10\\\\\n", + "\t2020-06-03 & 10\\\\\n", + "\t2020-06-10 & 10\\\\\n", + "\t2020-06-17 & 10\\\\\n", + "\t2020-06-24 & 10\\\\\n", + "\t2020-07-01 & 10\\\\\n", + "\t2020-07-08 & 10\\\\\n", + "\t2020-07-15 & 10\\\\\n", + "\t2020-07-22 & 10\\\\\n", + "\t2020-07-29 & 10\\\\\n", + "\t2020-08-05 & 10\\\\\n", + "\t2020-08-12 & 10\\\\\n", + "\t2020-08-19 & 10\\\\\n", + "\t2020-08-26 & 10\\\\\n", + "\t2020-09-02 & 10\\\\\n", + "\t2020-09-09 & 10\\\\\n", + "\t2020-09-16 & 10\\\\\n", + "\t2020-09-23 & 10\\\\\n", + "\t2020-09-30 & 10\\\\\n", + "\t2020-10-07 & 10\\\\\n", + "\t2020-10-14 & 10\\\\\n", + "\t2020-10-21 & 10\\\\\n", + "\t2020-10-28 & 10\\\\\n", + "\t2020-11-04 & 10\\\\\n", + "\t2020-11-11 & 10\\\\\n", + "\t2020-11-18 & 10\\\\\n", + "\t2020-11-25 & 10\\\\\n", + "\t2020-12-02 & 10\\\\\n", + "\t2020-12-09 & 10\\\\\n", + "\t2020-12-16 & 10\\\\\n", + "\t2020-12-23 & 10\\\\\n", + "\t2020-12-30 & 10\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 53 × 1\n", + "\n", + "| | additional_product <dbl> |\n", + "|---|---|\n", + "| 2020-01-01 | 10 |\n", + "| 2020-01-08 | 10 |\n", + "| 2020-01-15 | 10 |\n", + "| 2020-01-22 | 10 |\n", + "| 2020-01-29 | 10 |\n", + "| 2020-02-05 | 10 |\n", + "| 2020-02-12 | 10 |\n", + "| 2020-02-19 | 10 |\n", + "| 2020-02-26 | 10 |\n", + "| 2020-03-04 | 10 |\n", + "| 2020-03-11 | 10 |\n", + "| 2020-03-18 | 10 |\n", + "| 2020-03-25 | 10 |\n", + "| 2020-04-01 | 10 |\n", + "| 2020-04-08 | 10 |\n", + "| 2020-04-15 | 10 |\n", + "| 2020-04-22 | 10 |\n", + "| 2020-04-29 | 10 |\n", + "| 2020-05-06 | 10 |\n", + "| 2020-05-13 | 10 |\n", + "| 2020-05-20 | 10 |\n", + "| 2020-05-27 | 10 |\n", + "| 2020-06-03 | 10 |\n", + "| 2020-06-10 | 10 |\n", + "| 2020-06-17 | 10 |\n", + "| 2020-06-24 | 10 |\n", + "| 2020-07-01 | 10 |\n", + "| 2020-07-08 | 10 |\n", + "| 2020-07-15 | 10 |\n", + "| 2020-07-22 | 10 |\n", + "| 2020-07-29 | 10 |\n", + "| 2020-08-05 | 10 |\n", + "| 2020-08-12 | 10 |\n", + "| 2020-08-19 | 10 |\n", + "| 2020-08-26 | 10 |\n", + "| 2020-09-02 | 10 |\n", + "| 2020-09-09 | 10 |\n", + "| 2020-09-16 | 10 |\n", + "| 2020-09-23 | 10 |\n", + "| 2020-09-30 | 10 |\n", + "| 2020-10-07 | 10 |\n", + "| 2020-10-14 | 10 |\n", + "| 2020-10-21 | 10 |\n", + "| 2020-10-28 | 10 |\n", + "| 2020-11-04 | 10 |\n", + "| 2020-11-11 | 10 |\n", + "| 2020-11-18 | 10 |\n", + "| 2020-11-25 | 10 |\n", + "| 2020-12-02 | 10 |\n", + "| 2020-12-09 | 10 |\n", + "| 2020-12-16 | 10 |\n", + "| 2020-12-23 | 10 |\n", + "| 2020-12-30 | 10 |\n", + "\n" + ], + "text/plain": [ + " additional_product\n", + "2020-01-01 10 \n", + "2020-01-08 10 \n", + "2020-01-15 10 \n", + "2020-01-22 10 \n", + "2020-01-29 10 \n", + "2020-02-05 10 \n", + "2020-02-12 10 \n", + "2020-02-19 10 \n", + "2020-02-26 10 \n", + "2020-03-04 10 \n", + "2020-03-11 10 \n", + "2020-03-18 10 \n", + "2020-03-25 10 \n", + "2020-04-01 10 \n", + "2020-04-08 10 \n", + "2020-04-15 10 \n", + "2020-04-22 10 \n", + "2020-04-29 10 \n", + "2020-05-06 10 \n", + "2020-05-13 10 \n", + "2020-05-20 10 \n", + "2020-05-27 10 \n", + "2020-06-03 10 \n", + "2020-06-10 10 \n", + "2020-06-17 10 \n", + "2020-06-24 10 \n", + "2020-07-01 10 \n", + "2020-07-08 10 \n", + "2020-07-15 10 \n", + "2020-07-22 10 \n", + "2020-07-29 10 \n", + "2020-08-05 10 \n", + "2020-08-12 10 \n", + "2020-08-19 10 \n", + "2020-08-26 10 \n", + "2020-09-02 10 \n", + "2020-09-09 10 \n", + "2020-09-16 10 \n", + "2020-09-23 10 \n", + "2020-09-30 10 \n", + "2020-10-07 10 \n", + "2020-10-14 10 \n", + "2020-10-21 10 \n", + "2020-10-28 10 \n", + "2020-11-04 10 \n", + "2020-11-11 10 \n", + "2020-11-18 10 \n", + "2020-11-25 10 \n", + "2020-12-02 10 \n", + "2020-12-09 10 \n", + "2020-12-16 10 \n", + "2020-12-23 10 \n", + "2020-12-30 10 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + 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A data.frame: 366 × 1
total
<dbl>
2020-01-0153.59979
2020-01-02 NA
2020-01-03 NA
2020-01-04 NA
2020-01-05 NA
2020-01-06 NA
2020-01-07 NA
2020-01-0840.93455
2020-01-09 NA
2020-01-10 NA
2020-01-11 NA
2020-01-12 NA
2020-01-13 NA
2020-01-14 NA
2020-01-1559.24704
2020-01-16 NA
2020-01-17 NA
2020-01-18 NA
2020-01-19 NA
2020-01-20 NA
2020-01-21 NA
2020-01-2238.26416
2020-01-23 NA
2020-01-24 NA
2020-01-25 NA
2020-01-26 NA
2020-01-27 NA
2020-01-28 NA
2020-01-2944.58327
2020-01-30 NA
......
2020-12-0241.74811
2020-12-03 NA
2020-12-04 NA
2020-12-05 NA
2020-12-06 NA
2020-12-07 NA
2020-12-08 NA
2020-12-0937.85650
2020-12-10 NA
2020-12-11 NA
2020-12-12 NA
2020-12-13 NA
2020-12-14 NA
2020-12-15 NA
2020-12-1646.73560
2020-12-17 NA
2020-12-18 NA
2020-12-19 NA
2020-12-20 NA
2020-12-21 NA
2020-12-22 NA
2020-12-2340.42143
2020-12-24 NA
2020-12-25 NA
2020-12-26 NA
2020-12-27 NA
2020-12-28 NA
2020-12-29 NA
2020-12-3041.20298
2020-12-31 NA
\n" + ], + "text/latex": [ + "A data.frame: 366 × 1\n", + "\\begin{tabular}{r|l}\n", + " & total\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t2020-01-01 & 53.59979\\\\\n", + "\t2020-01-02 & NA\\\\\n", + "\t2020-01-03 & NA\\\\\n", + "\t2020-01-04 & NA\\\\\n", + "\t2020-01-05 & NA\\\\\n", + "\t2020-01-06 & NA\\\\\n", + "\t2020-01-07 & NA\\\\\n", + "\t2020-01-08 & 40.93455\\\\\n", + "\t2020-01-09 & NA\\\\\n", + "\t2020-01-10 & NA\\\\\n", + "\t2020-01-11 & NA\\\\\n", + "\t2020-01-12 & NA\\\\\n", + "\t2020-01-13 & NA\\\\\n", + "\t2020-01-14 & NA\\\\\n", + "\t2020-01-15 & 59.24704\\\\\n", + "\t2020-01-16 & NA\\\\\n", + "\t2020-01-17 & NA\\\\\n", + "\t2020-01-18 & NA\\\\\n", + "\t2020-01-19 & NA\\\\\n", + "\t2020-01-20 & NA\\\\\n", + "\t2020-01-21 & NA\\\\\n", + "\t2020-01-22 & 38.26416\\\\\n", + "\t2020-01-23 & NA\\\\\n", + "\t2020-01-24 & NA\\\\\n", + "\t2020-01-25 & NA\\\\\n", + "\t2020-01-26 & NA\\\\\n", + "\t2020-01-27 & NA\\\\\n", + "\t2020-01-28 & NA\\\\\n", + "\t2020-01-29 & 44.58327\\\\\n", + "\t2020-01-30 & NA\\\\\n", + "\t... & ...\\\\\n", + "\t2020-12-02 & 41.74811\\\\\n", + "\t2020-12-03 & NA\\\\\n", + "\t2020-12-04 & NA\\\\\n", + "\t2020-12-05 & NA\\\\\n", + "\t2020-12-06 & NA\\\\\n", + "\t2020-12-07 & NA\\\\\n", + "\t2020-12-08 & NA\\\\\n", + "\t2020-12-09 & 37.85650\\\\\n", + "\t2020-12-10 & NA\\\\\n", + "\t2020-12-11 & NA\\\\\n", + "\t2020-12-12 & NA\\\\\n", + "\t2020-12-13 & NA\\\\\n", + "\t2020-12-14 & NA\\\\\n", + "\t2020-12-15 & NA\\\\\n", + "\t2020-12-16 & 46.73560\\\\\n", + "\t2020-12-17 & NA\\\\\n", + "\t2020-12-18 & NA\\\\\n", + "\t2020-12-19 & NA\\\\\n", + "\t2020-12-20 & NA\\\\\n", + "\t2020-12-21 & NA\\\\\n", + "\t2020-12-22 & NA\\\\\n", + "\t2020-12-23 & 40.42143\\\\\n", + "\t2020-12-24 & NA\\\\\n", + "\t2020-12-25 & NA\\\\\n", + "\t2020-12-26 & NA\\\\\n", + "\t2020-12-27 & NA\\\\\n", + "\t2020-12-28 & NA\\\\\n", + "\t2020-12-29 & NA\\\\\n", + "\t2020-12-30 & 41.20298\\\\\n", + "\t2020-12-31 & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 366 × 1\n", + "\n", + "| | total <dbl> |\n", + "|---|---|\n", + "| 2020-01-01 | 53.59979 |\n", + "| 2020-01-02 | NA |\n", + "| 2020-01-03 | NA |\n", + "| 2020-01-04 | NA |\n", + "| 2020-01-05 | NA |\n", + "| 2020-01-06 | NA |\n", + "| 2020-01-07 | NA |\n", + "| 2020-01-08 | 40.93455 |\n", + "| 2020-01-09 | NA |\n", + "| 2020-01-10 | NA |\n", + "| 2020-01-11 | NA |\n", + "| 2020-01-12 | NA |\n", + "| 2020-01-13 | NA |\n", + "| 2020-01-14 | NA |\n", + "| 2020-01-15 | 59.24704 |\n", + "| 2020-01-16 | NA |\n", + "| 2020-01-17 | NA |\n", + "| 2020-01-18 | NA |\n", + "| 2020-01-19 | NA |\n", + "| 2020-01-20 | NA |\n", + "| 2020-01-21 | NA |\n", + "| 2020-01-22 | 38.26416 |\n", + "| 2020-01-23 | NA |\n", + "| 2020-01-24 | NA |\n", + "| 2020-01-25 | NA |\n", + "| 2020-01-26 | NA |\n", + "| 2020-01-27 | NA |\n", + "| 2020-01-28 | NA |\n", + "| 2020-01-29 | 44.58327 |\n", + "| 2020-01-30 | NA |\n", + "| ... | ... |\n", + "| 2020-12-02 | 41.74811 |\n", + "| 2020-12-03 | NA |\n", + "| 2020-12-04 | NA |\n", + "| 2020-12-05 | NA |\n", + "| 2020-12-06 | NA |\n", + "| 2020-12-07 | NA |\n", + "| 2020-12-08 | NA |\n", + "| 2020-12-09 | 37.85650 |\n", + "| 2020-12-10 | NA |\n", + "| 2020-12-11 | NA |\n", + "| 2020-12-12 | NA |\n", + "| 2020-12-13 | NA |\n", + "| 2020-12-14 | NA |\n", + "| 2020-12-15 | NA |\n", + "| 2020-12-16 | 46.73560 |\n", + "| 2020-12-17 | NA |\n", + "| 2020-12-18 | NA |\n", + "| 2020-12-19 | NA |\n", + "| 2020-12-20 | NA |\n", + "| 2020-12-21 | NA |\n", + "| 2020-12-22 | NA |\n", + "| 2020-12-23 | 40.42143 |\n", + "| 2020-12-24 | NA |\n", + "| 2020-12-25 | NA |\n", + "| 2020-12-26 | NA |\n", + "| 2020-12-27 | NA |\n", + "| 2020-12-28 | NA |\n", + "| 2020-12-29 | NA |\n", + "| 2020-12-30 | 41.20298 |\n", + "| 2020-12-31 | NA |\n", + "\n" + ], + "text/plain": [ + " total \n", + "2020-01-01 53.59979\n", + "2020-01-02 NA\n", + "2020-01-03 NA\n", + "2020-01-04 NA\n", + "2020-01-05 NA\n", + "2020-01-06 NA\n", + "2020-01-07 NA\n", + "2020-01-08 40.93455\n", + "2020-01-09 NA\n", + "2020-01-10 NA\n", + "2020-01-11 NA\n", + "2020-01-12 NA\n", + "2020-01-13 NA\n", + "2020-01-14 NA\n", + "2020-01-15 59.24704\n", + "2020-01-16 NA\n", + "2020-01-17 NA\n", + "2020-01-18 NA\n", + "2020-01-19 NA\n", + "2020-01-20 NA\n", + "2020-01-21 NA\n", + "2020-01-22 38.26416\n", + "2020-01-23 NA\n", + "2020-01-24 NA\n", + "2020-01-25 NA\n", + "2020-01-26 NA\n", + "2020-01-27 NA\n", + "2020-01-28 NA\n", + "2020-01-29 44.58327\n", + "2020-01-30 NA\n", + "... ... \n", + "2020-12-02 41.74811\n", + "2020-12-03 NA\n", + "2020-12-04 NA\n", + "2020-12-05 NA\n", + "2020-12-06 NA\n", + "2020-12-07 NA\n", + "2020-12-08 NA\n", + "2020-12-09 37.85650\n", + "2020-12-10 NA\n", + "2020-12-11 NA\n", + "2020-12-12 NA\n", + "2020-12-13 NA\n", + "2020-12-14 NA\n", + "2020-12-15 NA\n", + "2020-12-16 46.73560\n", + "2020-12-17 NA\n", + "2020-12-18 NA\n", + "2020-12-19 NA\n", + "2020-12-20 NA\n", + "2020-12-21 NA\n", + "2020-12-22 NA\n", + "2020-12-23 40.42143\n", + "2020-12-24 NA\n", + "2020-12-25 NA\n", + "2020-12-26 NA\n", + "2020-12-27 NA\n", + "2020-12-28 NA\n", + "2020-12-29 NA\n", + "2020-12-30 41.20298\n", + "2020-12-31 NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "index = seq(start_date,end_date,by = 'week')\n", + "sz = length(index)\n", + "additional_product <- rep(10,53)\n", + "additional_items <- data.frame(row.names = index[0:sz],additional_product)\n", + "additional_items\n", + "# we are merging two dataframe so that we can add\n", + "additional_item = merge(additional_items,sold_items, by = 0, all = TRUE)[-1] \n", + "total = data.frame(row.names=idx[0:size],additional_item$additional_product + additional_item$sales)\n", + "colnames(total) = c('total')\n", + "total" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "387cb4c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + 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A data.frame: 366 × 1
total
<dbl>
2020-01-0153.59979
2020-01-0230.41127
2020-01-0348.54839
2020-01-0439.20897
2020-01-0539.09894
2020-01-0647.53019
2020-01-0744.94766
2020-01-0840.93455
2020-01-0937.66561
2020-01-1031.68825
2020-01-1145.30576
2020-01-1226.45509
2020-01-1345.81249
2020-01-1446.84547
2020-01-1559.24704
2020-01-1629.28688
2020-01-1732.41731
2020-01-1845.23295
2020-01-1948.54330
2020-01-2036.69353
2020-01-2143.09588
2020-01-2238.26416
2020-01-2345.56863
2020-01-2425.70944
2020-01-2537.38721
2020-01-2644.53955
2020-01-2746.88427
2020-01-2848.05540
2020-01-2944.58327
2020-01-3026.19490
......
2020-12-0241.74811
2020-12-0335.03915
2020-12-0425.84637
2020-12-0527.73147
2020-12-0639.00993
2020-12-0741.03187
2020-12-0826.33862
2020-12-0937.85650
2020-12-1041.98943
2020-12-1136.68901
2020-12-1246.96883
2020-12-1339.70374
2020-12-1446.59464
2020-12-1541.24742
2020-12-1646.73560
2020-12-1732.68275
2020-12-1846.64238
2020-12-1925.22163
2020-12-2039.79997
2020-12-2134.45013
2020-12-2248.71183
2020-12-2340.42143
2020-12-2432.41991
2020-12-2539.12296
2020-12-2629.43616
2020-12-2739.09337
2020-12-2838.09288
2020-12-2941.00681
2020-12-3041.20298
2020-12-3143.25232
\n" + ], + "text/latex": [ + "A data.frame: 366 × 1\n", + "\\begin{tabular}{r|l}\n", + " & total\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t2020-01-01 & 53.59979\\\\\n", + "\t2020-01-02 & 30.41127\\\\\n", + "\t2020-01-03 & 48.54839\\\\\n", + "\t2020-01-04 & 39.20897\\\\\n", + "\t2020-01-05 & 39.09894\\\\\n", + "\t2020-01-06 & 47.53019\\\\\n", + "\t2020-01-07 & 44.94766\\\\\n", + "\t2020-01-08 & 40.93455\\\\\n", + "\t2020-01-09 & 37.66561\\\\\n", + "\t2020-01-10 & 31.68825\\\\\n", + "\t2020-01-11 & 45.30576\\\\\n", + "\t2020-01-12 & 26.45509\\\\\n", + "\t2020-01-13 & 45.81249\\\\\n", + "\t2020-01-14 & 46.84547\\\\\n", + "\t2020-01-15 & 59.24704\\\\\n", + "\t2020-01-16 & 29.28688\\\\\n", + "\t2020-01-17 & 32.41731\\\\\n", + "\t2020-01-18 & 45.23295\\\\\n", + "\t2020-01-19 & 48.54330\\\\\n", + "\t2020-01-20 & 36.69353\\\\\n", + "\t2020-01-21 & 43.09588\\\\\n", + "\t2020-01-22 & 38.26416\\\\\n", + "\t2020-01-23 & 45.56863\\\\\n", + "\t2020-01-24 & 25.70944\\\\\n", + "\t2020-01-25 & 37.38721\\\\\n", + "\t2020-01-26 & 44.53955\\\\\n", + "\t2020-01-27 & 46.88427\\\\\n", + "\t2020-01-28 & 48.05540\\\\\n", + "\t2020-01-29 & 44.58327\\\\\n", + "\t2020-01-30 & 26.19490\\\\\n", + "\t... & ...\\\\\n", + "\t2020-12-02 & 41.74811\\\\\n", + "\t2020-12-03 & 35.03915\\\\\n", + "\t2020-12-04 & 25.84637\\\\\n", + "\t2020-12-05 & 27.73147\\\\\n", + "\t2020-12-06 & 39.00993\\\\\n", + "\t2020-12-07 & 41.03187\\\\\n", + "\t2020-12-08 & 26.33862\\\\\n", + "\t2020-12-09 & 37.85650\\\\\n", + "\t2020-12-10 & 41.98943\\\\\n", + "\t2020-12-11 & 36.68901\\\\\n", + "\t2020-12-12 & 46.96883\\\\\n", + "\t2020-12-13 & 39.70374\\\\\n", + "\t2020-12-14 & 46.59464\\\\\n", + "\t2020-12-15 & 41.24742\\\\\n", + "\t2020-12-16 & 46.73560\\\\\n", + "\t2020-12-17 & 32.68275\\\\\n", + "\t2020-12-18 & 46.64238\\\\\n", + "\t2020-12-19 & 25.22163\\\\\n", + "\t2020-12-20 & 39.79997\\\\\n", + "\t2020-12-21 & 34.45013\\\\\n", + "\t2020-12-22 & 48.71183\\\\\n", + "\t2020-12-23 & 40.42143\\\\\n", + "\t2020-12-24 & 32.41991\\\\\n", + "\t2020-12-25 & 39.12296\\\\\n", + "\t2020-12-26 & 29.43616\\\\\n", + "\t2020-12-27 & 39.09337\\\\\n", + "\t2020-12-28 & 38.09288\\\\\n", + "\t2020-12-29 & 41.00681\\\\\n", + "\t2020-12-30 & 41.20298\\\\\n", + "\t2020-12-31 & 43.25232\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 366 × 1\n", + "\n", + "| | total <dbl> |\n", + "|---|---|\n", + "| 2020-01-01 | 53.59979 |\n", + "| 2020-01-02 | 30.41127 |\n", + "| 2020-01-03 | 48.54839 |\n", + "| 2020-01-04 | 39.20897 |\n", + "| 2020-01-05 | 39.09894 |\n", + "| 2020-01-06 | 47.53019 |\n", + "| 2020-01-07 | 44.94766 |\n", + "| 2020-01-08 | 40.93455 |\n", + "| 2020-01-09 | 37.66561 |\n", + "| 2020-01-10 | 31.68825 |\n", + "| 2020-01-11 | 45.30576 |\n", + "| 2020-01-12 | 26.45509 |\n", + "| 2020-01-13 | 45.81249 |\n", + "| 2020-01-14 | 46.84547 |\n", + "| 2020-01-15 | 59.24704 |\n", + "| 2020-01-16 | 29.28688 |\n", + "| 2020-01-17 | 32.41731 |\n", + "| 2020-01-18 | 45.23295 |\n", + "| 2020-01-19 | 48.54330 |\n", + "| 2020-01-20 | 36.69353 |\n", + "| 2020-01-21 | 43.09588 |\n", + "| 2020-01-22 | 38.26416 |\n", + "| 2020-01-23 | 45.56863 |\n", + "| 2020-01-24 | 25.70944 |\n", + "| 2020-01-25 | 37.38721 |\n", + "| 2020-01-26 | 44.53955 |\n", + "| 2020-01-27 | 46.88427 |\n", + "| 2020-01-28 | 48.05540 |\n", + "| 2020-01-29 | 44.58327 |\n", + "| 2020-01-30 | 26.19490 |\n", + "| ... | ... |\n", + "| 2020-12-02 | 41.74811 |\n", + "| 2020-12-03 | 35.03915 |\n", + "| 2020-12-04 | 25.84637 |\n", + "| 2020-12-05 | 27.73147 |\n", + "| 2020-12-06 | 39.00993 |\n", + "| 2020-12-07 | 41.03187 |\n", + "| 2020-12-08 | 26.33862 |\n", + "| 2020-12-09 | 37.85650 |\n", + "| 2020-12-10 | 41.98943 |\n", + "| 2020-12-11 | 36.68901 |\n", + "| 2020-12-12 | 46.96883 |\n", + "| 2020-12-13 | 39.70374 |\n", + "| 2020-12-14 | 46.59464 |\n", + "| 2020-12-15 | 41.24742 |\n", + "| 2020-12-16 | 46.73560 |\n", + "| 2020-12-17 | 32.68275 |\n", + "| 2020-12-18 | 46.64238 |\n", + "| 2020-12-19 | 25.22163 |\n", + "| 2020-12-20 | 39.79997 |\n", + "| 2020-12-21 | 34.45013 |\n", + "| 2020-12-22 | 48.71183 |\n", + "| 2020-12-23 | 40.42143 |\n", + "| 2020-12-24 | 32.41991 |\n", + "| 2020-12-25 | 39.12296 |\n", + "| 2020-12-26 | 29.43616 |\n", + "| 2020-12-27 | 39.09337 |\n", + "| 2020-12-28 | 38.09288 |\n", + "| 2020-12-29 | 41.00681 |\n", + "| 2020-12-30 | 41.20298 |\n", + "| 2020-12-31 | 43.25232 |\n", + "\n" + ], + "text/plain": [ + " total \n", + "2020-01-01 53.59979\n", + "2020-01-02 30.41127\n", + "2020-01-03 48.54839\n", + "2020-01-04 39.20897\n", + "2020-01-05 39.09894\n", + "2020-01-06 47.53019\n", + "2020-01-07 44.94766\n", + "2020-01-08 40.93455\n", + "2020-01-09 37.66561\n", + "2020-01-10 31.68825\n", + "2020-01-11 45.30576\n", + "2020-01-12 26.45509\n", + "2020-01-13 45.81249\n", + "2020-01-14 46.84547\n", + "2020-01-15 59.24704\n", + "2020-01-16 29.28688\n", + "2020-01-17 32.41731\n", + "2020-01-18 45.23295\n", + "2020-01-19 48.54330\n", + "2020-01-20 36.69353\n", + "2020-01-21 43.09588\n", + "2020-01-22 38.26416\n", + "2020-01-23 45.56863\n", + "2020-01-24 25.70944\n", + "2020-01-25 37.38721\n", + "2020-01-26 44.53955\n", + "2020-01-27 46.88427\n", + "2020-01-28 48.05540\n", + "2020-01-29 44.58327\n", + "2020-01-30 26.19490\n", + "... ... \n", + "2020-12-02 41.74811\n", + "2020-12-03 35.03915\n", + "2020-12-04 25.84637\n", + "2020-12-05 27.73147\n", + "2020-12-06 39.00993\n", + "2020-12-07 41.03187\n", + "2020-12-08 26.33862\n", + "2020-12-09 37.85650\n", + "2020-12-10 41.98943\n", + "2020-12-11 36.68901\n", + "2020-12-12 46.96883\n", + "2020-12-13 39.70374\n", + "2020-12-14 46.59464\n", + "2020-12-15 41.24742\n", + "2020-12-16 46.73560\n", + "2020-12-17 32.68275\n", + "2020-12-18 46.64238\n", + "2020-12-19 25.22163\n", + "2020-12-20 39.79997\n", + "2020-12-21 34.45013\n", + "2020-12-22 48.71183\n", + "2020-12-23 40.42143\n", + "2020-12-24 32.41991\n", + "2020-12-25 39.12296\n", + "2020-12-26 29.43616\n", + "2020-12-27 39.09337\n", + "2020-12-28 38.09288\n", + "2020-12-29 41.00681\n", + "2020-12-30 41.20298\n", + "2020-12-31 43.25232" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "additional_item[is.na(additional_item)] = 0\n", + "total = data.frame(row.names=idx[0:size],additional_item$additional_product + additional_item$sales)\n", + "colnames(total) = c('total')\n", + "total" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "bdb60236", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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RVP1qDfRLZPvumCUeh3cXSqvKtq0K/qW37WvYtyZYJ2G0I6CDVo2DDPbU2pQWNl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\nAS5sdYLm0dKiPKkFtXCxH4XSHmuVE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Vgh40yLvmj3gXh5fxKXGcDp1tc3ccXafd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\nEomgSIJOJBKJoEiCTiQSiaD4/87RXKHCQFeFAAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "ggplot(total,aes(x=idx,y=total)) + geom_point(color = \"firebrick\", shape = \"diamond\", size = 2) +\n", + " geom_line(color = \"firebrick\", linetype = \"dotted\", size = .3)" + ] + }, + { + "cell_type": "markdown", + "id": "38e65fd5", + "metadata": {}, + "source": [ + "We want to analyse total no of product in monthly basis.Thus, we find the mean of total no of product in a month and draw a bargraph" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "294dde87", + "metadata": {}, + "outputs": [], + "source": [ + "index = seq(start_date,end_date,by ='month')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "7542d95e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " total\n", + "2020-01-31 41.03847\n", + "2020-02-29 40.91568\n", + "2020-03-31 39.27424\n", + "2020-04-30 37.63589\n", + "2020-05-31 38.75129\n", + "2020-06-30 38.75744\n", + "2020-07-31 38.35212\n", + "2020-08-31 40.43712\n", + "2020-09-30 38.90043\n", + "2020-10-31 37.99855\n", + "2020-11-30 41.20759\n", + "2020-12-31 38.46355" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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8X0gQK6WQR0VQN0uAjo4WL6QBtA+10c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\nZM8la27Zc8maFh2f9J6Abs6SPZesuWXPJWtadHzSewK6OUv2XLL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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 360, + "width": 720 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "x<- as.xts(total, dateFormat =\"Date\")\n", + "(monthly<-apply.monthly(x,mean))\n", + "ggplot(monthly, aes(x=index, y=total)) + \n", + " geom_bar(stat = \"identity\", width=5) " + ] + }, + { + "cell_type": "markdown", + "id": "945feffd", + "metadata": {}, + "source": [ + "## DataFrame\n", + "Dataframe is essentially a collection of series with the same index. We can combine several series together into a dataframe. \n", + "For example we are making dataframe of a and b series" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "88a435ec", + "metadata": {}, + "outputs": [], + "source": [ + "a = data.frame(a,row.names = c(1:a1))" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "c4e2a6c1", + "metadata": {}, + "outputs": [], + "source": [ + "b = data.frame(b,row.names = c(1:b1))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "2bb5177c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 2
ab
<int><chr>
11I
22like
33to
44use
55Python
66and
77Pandas
88very
99much
\n" + ], + "text/latex": [ + "A data.frame: 9 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & a & b\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I \\\\\n", + "\t2 & 2 & like \\\\\n", + "\t3 & 3 & to \\\\\n", + "\t4 & 4 & use \\\\\n", + "\t5 & 5 & Python\\\\\n", + "\t6 & 6 & and \\\\\n", + "\t7 & 7 & Pandas\\\\\n", + "\t8 & 8 & very \\\\\n", + "\t9 & 9 & much \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 2\n", + "\n", + "| | a <int> | b <chr> |\n", + "|---|---|---|\n", + "| 1 | 1 | I |\n", + "| 2 | 2 | like |\n", + "| 3 | 3 | to |\n", + "| 4 | 4 | use |\n", + "| 5 | 5 | Python |\n", + "| 6 | 6 | and |\n", + "| 7 | 7 | Pandas |\n", + "| 8 | 8 | very |\n", + "| 9 | 9 | much |\n", + "\n" + ], + "text/plain": [ + " a b \n", + "1 1 I \n", + "2 2 like \n", + "3 3 to \n", + "4 4 use \n", + "5 5 Python\n", + "6 6 and \n", + "7 7 Pandas\n", + "8 8 very \n", + "9 9 much " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df<- data.frame(a,b)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "6531fe0e", + "metadata": {}, + "source": [ + "We can also rename the column name by using rename function" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "8f45d3a5", + "metadata": {}, + "outputs": [], + "source": [ + "df = \n", + " rename(df,\n", + " A = a,\n", + " B = b,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "0efbf2d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 2
AB
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66and
77Pandas
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A data.frame: 9 × 1
A
<int>
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\n" + ], + "text/latex": [ + "A data.frame: 9 × 1\n", + "\\begin{tabular}{r|l}\n", + " & A\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t1 & 1\\\\\n", + "\t2 & 2\\\\\n", + "\t3 & 3\\\\\n", + "\t4 & 4\\\\\n", + "\t5 & 5\\\\\n", + "\t6 & 6\\\\\n", + "\t7 & 7\\\\\n", + "\t8 & 8\\\\\n", + "\t9 & 9\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 1\n", + "\n", + "| | A <int> |\n", + "|---|---|\n", + "| 1 | 1 |\n", + "| 2 | 2 |\n", + "| 3 | 3 |\n", + "| 4 | 4 |\n", + "| 5 | 5 |\n", + "| 6 | 6 |\n", + "| 7 | 7 |\n", + "| 8 | 8 |\n", + "| 9 | 9 |\n", + "\n" + ], + "text/plain": [ + " A\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + "5 5\n", + "6 6\n", + "7 7\n", + "8 8\n", + "9 9" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cat(\"Column A (series):\\n\")\n", + "select(df,'A')" + ] + }, + { + "cell_type": "markdown", + "id": "45397ec4", + "metadata": {}, + "source": [ + "We will extract rows that meet a certain logical criteria on series" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "010bcba8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 4 × 2
AB
<int><chr>
11I
22like
33to
44use
\n" + ], + "text/latex": [ + "A data.frame: 4 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & A & B\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I \\\\\n", + "\t2 & 2 & like\\\\\n", + "\t3 & 3 & to \\\\\n", + "\t4 & 4 & use \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 4 × 2\n", + "\n", + "| | A <int> | B <chr> |\n", + "|---|---|---|\n", + "| 1 | 1 | I |\n", + "| 2 | 2 | like |\n", + "| 3 | 3 | to |\n", + "| 4 | 4 | use |\n", + "\n" + ], + "text/plain": [ + " A B \n", + "1 1 I \n", + "2 2 like\n", + "3 3 to \n", + "4 4 use " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[df$A<5,]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "082277db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A data.frame: 1 × 2
AB
<int><chr>
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A data.frame: 9 × 3
ABDivA
<int><chr><dbl>
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\n" + ], + "text/latex": [ + "A data.frame: 9 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & A & B & DivA\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4\\\\\n", + "\t2 & 2 & like & -3\\\\\n", + "\t3 & 3 & to & -2\\\\\n", + "\t4 & 4 & use & -1\\\\\n", + "\t5 & 5 & Python & 0\\\\\n", + "\t6 & 6 & and & 1\\\\\n", + "\t7 & 7 & Pandas & 2\\\\\n", + "\t8 & 8 & very & 3\\\\\n", + "\t9 & 9 & much & 4\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 3\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> |\n", + "|---|---|---|---|\n", + "| 1 | 1 | I | -4 |\n", + "| 2 | 2 | like | -3 |\n", + "| 3 | 3 | to | -2 |\n", + "| 4 | 4 | use | -1 |\n", + "| 5 | 5 | Python | 0 |\n", + "| 6 | 6 | and | 1 |\n", + "| 7 | 7 | Pandas | 2 |\n", + "| 8 | 8 | very | 3 |\n", + "| 9 | 9 | much | 4 |\n", + "\n" + ], + "text/plain": [ + " A B DivA\n", + "1 1 I -4 \n", + "2 2 like -3 \n", + "3 3 to -2 \n", + "4 4 use -1 \n", + "5 5 Python 0 \n", + "6 6 and 1 \n", + "7 7 Pandas 2 \n", + "8 8 very 3 \n", + "9 9 much 4 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "2be67ef7", + "metadata": {}, + "source": [ + "We are creating a series which calculates the length of string of A column then merge into existing dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "c67f2bd0", + "metadata": {}, + "outputs": [], + "source": [ + "df$LenB <- str_length(df$B)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "cef214b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 4
ABDivALenB
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\n" + ], + "text/latex": [ + "A data.frame: 9 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & A & B & DivA & LenB\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4 & 1\\\\\n", + "\t2 & 2 & like & -3 & 4\\\\\n", + "\t3 & 3 & to & -2 & 2\\\\\n", + "\t4 & 4 & use & -1 & 3\\\\\n", + "\t5 & 5 & Python & 0 & 6\\\\\n", + "\t6 & 6 & and & 1 & 3\\\\\n", + "\t7 & 7 & Pandas & 2 & 6\\\\\n", + "\t8 & 8 & very & 3 & 4\\\\\n", + "\t9 & 9 & much & 4 & 4\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 4\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | I | -4 | 1 |\n", + "| 2 | 2 | like | -3 | 4 |\n", + "| 3 | 3 | to | -2 | 2 |\n", + "| 4 | 4 | use | -1 | 3 |\n", + "| 5 | 5 | Python | 0 | 6 |\n", + "| 6 | 6 | and | 1 | 3 |\n", + "| 7 | 7 | Pandas | 2 | 6 |\n", + "| 8 | 8 | very | 3 | 4 |\n", + "| 9 | 9 | much | 4 | 4 |\n", + "\n" + ], + "text/plain": [ + " A B DivA LenB\n", + "1 1 I -4 1 \n", + "2 2 like -3 4 \n", + "3 3 to -2 2 \n", + "4 4 use -1 3 \n", + "5 5 Python 0 6 \n", + "6 6 and 1 3 \n", + "7 7 Pandas 2 6 \n", + "8 8 very 3 4 \n", + "9 9 much 4 4 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "e37d50de", + "metadata": {}, + "source": [ + "Selecting rows based on numbers " + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "59fe5316", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 5 × 4
ABDivALenB
<int><chr><dbl><int>
11I -41
22like -34
33to -22
44use -13
55Python 06
\n" + ], + "text/latex": [ + "A data.frame: 5 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & A & B & DivA & LenB\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4 & 1\\\\\n", + "\t2 & 2 & like & -3 & 4\\\\\n", + "\t3 & 3 & to & -2 & 2\\\\\n", + "\t4 & 4 & use & -1 & 3\\\\\n", + "\t5 & 5 & Python & 0 & 6\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 5 × 4\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | I | -4 | 1 |\n", + "| 2 | 2 | like | -3 | 4 |\n", + "| 3 | 3 | to | -2 | 2 |\n", + "| 4 | 4 | use | -1 | 3 |\n", + "| 5 | 5 | Python | 0 | 6 |\n", + "\n" + ], + "text/plain": [ + " A B DivA LenB\n", + "1 1 I -4 1 \n", + "2 2 like -3 4 \n", + "3 3 to -2 2 \n", + "4 4 use -1 3 \n", + "5 5 Python 0 6 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[0:5,]" + ] + }, + { + "cell_type": "markdown", + "id": "6abec1b7", + "metadata": {}, + "source": [ + "***Grouping means which groups the multiple columns based on certain conditions and we will use summarise function to see the difference***\n", + "\n", + "Suppose that we want to compute the mean value of column A for each given number of LenB. Then we can group our DataFrame by LenB, and find mean name them as a" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "f944a949", + "metadata": {}, + "outputs": [], + "source": [ + "df1 = df %>% group_by(LenB) %>% summarise(a = mean(A))" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "8ffd39cd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 5 × 2
LenBa
<int><dbl>
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35.000000
46.333333
66.000000
\n" + ], + "text/latex": [ + "A tibble: 5 × 2\n", + "\\begin{tabular}{ll}\n", + " LenB & a\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t 1 & 1.000000\\\\\n", + "\t 2 & 3.000000\\\\\n", + "\t 3 & 5.000000\\\\\n", + "\t 4 & 6.333333\\\\\n", + "\t 6 & 6.000000\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 5 × 2\n", + "\n", + "| LenB <int> | a <dbl> |\n", + "|---|---|\n", + "| 1 | 1.000000 |\n", + "| 2 | 3.000000 |\n", + "| 3 | 5.000000 |\n", + "| 4 | 6.333333 |\n", + "| 6 | 6.000000 |\n", + "\n" + ], + "text/plain": [ + " LenB a \n", + "1 1 1.000000\n", + "2 2 3.000000\n", + "3 3 5.000000\n", + "4 4 6.333333\n", + "5 6 6.000000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df1" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "3b859950", + "metadata": {}, + "outputs": [], + "source": [ + "df2 = df %>% group_by(LenB) %>%\n", + "summarise(MEAN = mean(A),count =length(DivA))" + ] + }, + { + "cell_type": "markdown", + "id": "5d3f0287", + "metadata": {}, + "source": [ + "## Printing and Plotting\n", + " When We call head(df) it will print out dataframe in a tabular form.\n", + "\n", + "The first step of any data science project is data cleaning and visualization, thus it is important to visualize the dataset and extract some useful imformation." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "69946dc7", + "metadata": {}, + "outputs": [], + "source": [ + "#dataset = read.csv(\"file name\")" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "4976f190", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 4
ABDivALenB
<int><chr><dbl><int>
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33to -22
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\n" + ], + "text/latex": [ + "A data.frame: 6 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & A & B & DivA & LenB\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4 & 1\\\\\n", + "\t2 & 2 & like & -3 & 4\\\\\n", + "\t3 & 3 & to & -2 & 2\\\\\n", + "\t4 & 4 & use & -1 & 3\\\\\n", + "\t5 & 5 & Python & 0 & 6\\\\\n", + "\t6 & 6 & and & 1 & 3\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 4\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | I | -4 | 1 |\n", + "| 2 | 2 | like | -3 | 4 |\n", + "| 3 | 3 | to | -2 | 2 |\n", + "| 4 | 4 | use | -1 | 3 |\n", + "| 5 | 5 | Python | 0 | 6 |\n", + "| 6 | 6 | and | 1 | 3 |\n", + "\n" + ], + "text/plain": [ + " A B DivA LenB\n", + "1 1 I -4 1 \n", + "2 2 like -3 4 \n", + "3 3 to -2 2 \n", + "4 4 use -1 3 \n", + "5 5 Python 0 6 \n", + "6 6 and 1 3 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(df)" + ] + }, + { + "cell_type": "markdown", + "id": "dcca35a8", + "metadata": {}, + "source": [ + "ggplot2 is a very good library as it simple to create complex plots from data in a data frame.\n", + "\n", + "It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "515c95b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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