From ce53eb68b59c5609395e4b2bb00da85e8ffbaa70 Mon Sep 17 00:00:00 2001 From: Keshav Sharma Date: Mon, 11 Oct 2021 14:00:20 -0700 Subject: [PATCH] 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 00000000..eec49104 --- /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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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\n0OUioMPF9oECelkE9FQDdLkI6HCxfaCAXhYBPdUAXS4COlxsHyigl0VATzVAl4uADhfbBwro\nZRHQUw3Q5SKgw8X2gQJ6WQT0VAN0uQjocLF9oIBeFgE91QBdLgI6XGwfKKCXRUBPNUCXi4AO\nF9sHCuhlEdBTDdDlIqDDxfaBAnpZBPRUA3S5COhwsX2ggF4WAT3VAF0uAjpcbB8ooJdFQE81\nQJeLgA4X2wcK6GUR0FMN0OUioMPF9oECelkE9FQDdLkI6HCxfaCAXhYBPdUAXS4COlxsHyig\nl0VATzVAl4uADhfbBwroZRHQUw3Q5SKgw8X2gQJ6WQT0VAN0uQjocLF9oIBeFgE91QBdLgI6\nXGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBTDdDlIqDDxfaBAnpZBPRUA3S5COhwsX2ggF4WAT3V\nAF0uAjpcbB8ooJdFQE81QJeLgA4X2wcK6GUR0FMN0OUioMPF9oECelkE9FQDdLkI6HCxfaCA\nXhYBPdUAXS4COlxsHyigl0VATzVAl4uADhfbBwroZRHQUw3Q5SKgw8X2gQJ6WQT0VAN0uQjo\ncLF9oIBeFgE91QBdLgI6XGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBTDdDlIqDDxfaBAnpZBPRU\nA3S5COhwsX2ggF4WAT3VAF0uAjpcbB8ooJdFQE81QJeLgA4X2wcK6GUR0FMN0OUioMPF9oEC\nelkE9FQDdLkI6HCxfaCAXhYBPdUAXS4COlxsHyigl0VATzVAl4uADhfbBwroZRHQUw3Q5SKg\nw8X2gQJ6WQT0VAN0uQjocLF9oIBeFgE91QBdLgI6XGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBT\nDdDlIqDDxfaBAnpZBPRUA3S5COhwsX2ggF4WAT3VAF0uAjpcbB8ooJdFQE81QJeLgA4X2wcK\n6GUR0FMN0OUioMPF9oECelkE9FQDdLkI6HCxfaCAXhYBPdUAXS4COlxsHyigl0VATzVAl4uA\nDhfbBwroZRHQUw3Q5SKgw8X2gQJ6WQT0VAN0uQjocLF9oIBeFgE91QBdLgI6XGwfKKCXRUBP\nNUCXi4AOF9sH+nygnzcXvqC3U3z1r+2Vi2/6xbuce8naj1D88b6+WM4n6G8an6Cnmk/Q5aJP\n0OFi+0ABvSwCeqoBulwEdLjYPlBAL4uAnmqALhcBHS62DxTQyyKgpxqgy0VAh4vtAwX0sgjo\nqQbochHQ4WL7QAG9LAJ6qgG6XAR0uNg+UEAvi4CeaoAuFwEdLrYPFNDLIqCnGqDLRUCHi+0D\nBfSyCOipBuhyEdDhYvtAAb0sAnqqAbpcBHS42D5QQC+LgJ5qgC4XAR0utg8U0MsioKcaoMtF\nQIeL7QMF9LII6KkG6HIR0OFi+0ABvSwCeqoBulwEdLjYPlBAL4uAnmqALhcBHS62DxTQyyKg\npxqgy0VAh4vtAwX0sgjoqQbochHQ4WL7QAG9LAJ6qgG6XAR0uNg+UEAvi4CeaoAuFwEdLrYP\nFNDLIqCnGqDLRUCHi+0DBfSyCOipBuhyEdDhYvtAAb0sAnqqAbpcBHS42D5QQC+LgJ5qgC4X\nAR0utg8U0MsioKcaoMtFQIeL7QMF9LII6KkG6HIR0OFi+0ABvSwCeqoBulwEdLjYPlBAL4uA\nnmqALhcBHS62DxTQyyKgpxqgy0VAh4vtAwX0sgjoqQbochHQ4WL7QAG9LAJ6qgG6XAR0uNg+\nUEAvi4CeaoAuFwEdLrYPFNDLIqCnGqDLRUCHi+0DBfSyCOipBuhyEdDhYvtAAb0sAnqqAbpc\nBHS42D5QQC+LgJ5qgC4XAR0utg8U0MsioKcaoMtFQIeL7QMF9LII6KkG6HIR0OFi+0ABvSwC\neqoBulwEdLjYPlBAL4uAnmqALhcBHS62DxTQyyKgpxqgy0VAh4vtAwX0sgjoqQbochHQ4WL7\nQAG9LAJ6qgG6XAR0uNg+UEAvi4CeaoAuFwEdLrYPFNDLIqCnGqDLRUCHi+0DBfSyCOipBuhy\nEdDhYvtAAb0sAnqqAbpcBHS42D5QQC+LgJ5qgC4XAR0utg8U0MsioKcaoMtFQIeL7QMF9LII\n6KkG6HIR0OFi+0ABvSwCeqoBulwEdLjYPlBAL4uAnmqALhcBHS62DxTQyyKgpxqgy0VAh4vt\nAwX0sgjoqQbochHQ4WL7QAG9LAJ6qgG6XAR0uNg+UEAvi4CeaoAuFwEdLrYPFNDLIqCnGqDL\nRUCHi+0DBfSyCOipBuhyEdDhYvtALwN983gA/XgAPdUAXS4COlxsHyigl0VATzVAl4uADhfb\nB3oZ6M08r//K77jnFwE91QBdLgI6XGwfKKCXRUBPNUCXi4AOF9sH+jTQv/kpjksD6KkG6HIR\n0OFi+0CfBPo3Pwd9cQA91QBdLgI6XGwf6JNA3978793NH3++u/kvoB8PoKcaoMtFQIeL7QN9\nEuj3n5z/dfP73Z837wD9eAA91QBdLgI6XGwf6AT07zf//uuPgH40gJ5qgC4XAR0utg/0SaB/\nufnPHzc/3/0X0J8PoKcaoMtFQIeL7QN9Euh7md/d/xrhr4B+PICeaoAuFwEdLrYP9Emg737/\n+e7u15ub3/7BZ0BvUstd19N+twG6XAR0uNg+0KeB/tZ5Xv+V33HPLwJ6qgG6XAR0uNg+0A3Q\nt+/n8R8BvU4td11P+90G6HIR0OFi+0CfBPrvXxx8sPj24ze3n34A6G1quet62u82QJeLgA4X\n2wd6GejbS/82O0DfD6CnGqDLRUCHi+0DvQz0vx/5/O/Pf5oD0M9JLXddT/vdBuhyEdDhYvtA\n//GnOL6Yz4D+6X4u/se+eS58Qc/7G15T8dW/tlcuvukX73LuJWs/QvHH+/piuW/6pzhu73yC\nfk5quet6XrcYePHaH1C2uZesJYo+QYeL7QN9Gug/f/v55ubn3/4E9GcD6KkG6HIR0OFi+0Cf\nBPqPj79QePvHFz4D+hmp5a7rab/bAF0uAjpcbB/ok0D/evPuPc1/vHv8W71vv1Aa0NvUctf1\ntN9tgC4XAR0utg/0SaD//kXCR79YePvlx2hAb1PLXdfTfrcBulwEdLjYPtAF0Le3H38Lod9J\n+N2p5a7rab/bAF0uAjpcbB/o6qc4Ls7z+q/8jnt+EdBTDdDlIqDDxfaBPgn0xV8kBDSgxxqg\ny0VAh4vtA30S6Mv/mB2gAT3VAF0uAjpcbB/o00B/6zyv/8rvuOcXAT3VAF0uAjpcbB8ooJdF\nQE81QJeLgA4X2wf6JNBf/+tGAX0/gJ5qgC4XAR0utg/0MtAX/3WjgL4fQE81QJeLgA4X2wd6\nGein/3WjgH5WarnretrvNkCXi4AOF9sHehnouyf/daOAflZquet62u82QJeLgA4X2wf6JNDf\nPM/rv/I77vlFQE81QJeLgA4X2wcK6GUR0FMN0OUioMPF9oECelkE9FQDdLkI6HCxfaCAXhYB\nPdUAXS4COlxsHyigl0VATzVAl4uADhfbBwroZRHQUw3Q5SKgw8X2gQJ6WQT0VAN0uQjocLF9\noIBeFgE91QBdLgI6XGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBTDdDlIqDDxfaBAnpZBPRUA3S5\nCOhwsX2ggF4WAT3VAF0uAjpcbB8ooJdFQE81QJeLgA4X2wcK6GUR0FMN0OUioMPF9oECelkE\n9FQDdLkI6HCxfaCAXhYBPdUAXS4COlxsHyigl0VATzVAl4uADhfbBwroZRHQUw3Q5SKgw8X2\ngQJ6WQT0VAN0uQjocLF9oIBeFgE91QBdLgI6XGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBTDdDl\nIqDDxfaBAnpZBPRUA3S5COhwsX2ggF4WAT3VAF0uAjpcbB8ooJdFQE81QJeLgA4X2wcK6GUR\n0FMN0OUioMPF9oECelkE9FQDdLkI6HCxfaCAXhYBPdUAXS4COlxsHyigl0VATzVAl4uADhfb\nBwroZRHQUw3Q5SKgw8X2gQJ6WQT0VAN0uQjocLF9oIBeFgE91QBdLgI6XGwfKKCXRUBPNUCX\ni4AOF9sHCuhlEdBTDdDlIqDDxfaBAnpZBPRUA3S5COhwsX2ggF4WAT3VAF0uAjpcbB8ooJdF\nQE81QJeLgA4X2wcK6GUR0FMN0OUioMPF9oECelkE9FQDdLkI6HCxfaCAXhYBPdUAXS4COlxs\nHyigl0VATzVAl4uADhfbBwroZRHQUw3Q5SKgw8X2gQJ6WQT0VAN0uQjocLF9oIBeFgE91QBd\nLgI6XGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBTDdDlIqDDxfaBAnpZBPRUA3S5COhwsX2ggF4W\nAT3VAF0uAjpcbB8ooJdFQE81QJeLgA4X2wcK6GUR0FMN0OUioMPF9oECelkE9FQDdLkI6HCx\nfaCAXhYBPdUAXS4COlxsHyigl0VATzVAl4uADhfbBwroZRHQUw3Q5SKgw8X2gQJ6WQT0VAN0\nuQjocLF9oIBeFgE91QBdLgI6XGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBTDdDlIqDDxfaBPh/o\n582FL+jtFF/9a3vl4pt+8S7nXrL2IxR/vK8vlvMJ+pvGJ+ip5hN0uegTdLjYPlBAL4uAnmqA\nLhcBHS62DxTQyyKgpxqgy0VAh4vtAwX0sgjoqQbochHQ4WL7QAG9LAJ6qgG6XAR0uNg+UEAv\ni4CeaoAuFwEdLrYPFNDLIqCnGqDLRUCHi+0DBfSyCOipBuhyEdDhYvtAAb0sAnqqAbpcBHS4\n2D5QQC+LgJ5qgC4XAR0utg8U0MsioKcaoMtFQIeL7QMF9LII6KkG6HIR0OFi+0ABvSwCeqoB\nulwEdLjYPlBAL4uAnmqALhcBHS62DxTQyyKgpxqgy0VAh4vtAwX0sgjoqQbochHQ4WL7QAG9\nLAJ6qgG6XAR0uNg+UEAvi4CeaoAuFwEdLrYPFNDLIqCnGqDLRUCHi+0DBfSyCOipBuhyEdDh\nYvtAAb0sAnqqAbpcBHS42D5QQC+LgJ5qgC4XAR0utg8U0MsioKcaoMtFQIeL7QMF9LII6KkG\n6HIR0OFi+0ABvSwCeqoBulwEdLjYPlBAL4uAnmqALhcBHS62DxTQyyKgpxqgy0VAh4vtAwX0\nsgjoqQbochHQ4WL7QAG9LAJ6qgG6XAR0uNg+UEAvi4CeaoAuFwEdLrYPFNDLIqCnGqDLRUCH\ni+0DBfSyCOipBuhyEdDhYvtAAb0sAnqqAbpcBHS42D5QQC+LgJ5qgC4XAR0utg8U0MsioKca\noMtFQIeL7QMF9LII6KkG6HIR0OFi+0ABvSwCeqoBulwEdLjYPlBAL4uAnmqALhcBHS62DxTQ\nyyKgpxqgy0VAh4vtAwX0sgjoqQbochHQ4WL7QAG9LAJ6qgG6XAR0uNg+UEAvi4CeaoAuFwEd\nLrYPFNDLIqCnGqDLRUCHi+0DBfSyCOipBuhyEdDhYvtAAb0sAnqqAbpcBHS42D5QQC+LgJ5q\ngC4XAR0utg8U0MsioKcaoMtFQIeL7QMF9LII6KkG6HIR0OFi+0ABvSwCeqoBulwEdLjYPlBA\nL4uAnmqALhcBHS62DxTQyyKgpxqgy0VAh4vtAwX0sgjoqQbochHQ4WL7QAG9LAJ6qgG6XAR0\nuNg+UEAvi4CeaoAuFwEdLrYPFNDLIqCnGqDLRUCHi+0DBfSyCOipBuhyEdDhYvtAAb0sAnqq\nAbpcBHS42D5QQC+LgJ5qgC4XAR0utg8U0MsioKcaoMtFQIeL7QMF9LII6KkG6HIR0OFi+0AB\nvSwCeqoBulwEdLjYPlBAL4uAnmqALhcBHS62DxTQyyKgpxqgy0VAh4vtAwX0sgjoqQbochHQ\n4WL7QAG9LAJ6qgG6XAR0uNg+UEAvi4CeaoAuFwEdLrYPFNDLIqCnGqDLRUCHi+0DBfSyCOip\nBuhyEdDhYvtAAb0sAnqqAbpcBHS42D7QHdC3H759P4D+rtRy1/W0322ALhcBHS62D3QF9AeX\nH74B9Da13HU97XcboMtFQIeL7QPdAH17B2hAjzVAl4uADhfbB7r6BA1oQM81QJeLgA4X2wf6\nLKB/up9v+K8Nc+ELet7f8JqKr/61vXLxTb94l3MvWfsRij/e1xfL+QT9TeMT9FTzCbpc9Ak6\nXGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBTDdDlIqDDxfaBAnpZBPRUA3S5COhwsX2ggF4WAT3V\nAF0uAjpcbGeesAgAAA57SURBVB/odwDtdxJ+f2q563ra7zZAl4uADhfbB7oD+tI8r//K77jn\nFwE91QBdLgI6XGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBTDdDlIqDDxfaBAnpZBPRUA3S5COhw\nsX2ggF4WAT3VAF0uAjpcbB8ooJdFQE81QJeLgA4X2wcK6GUR0FMN0OUioMPF9oECelkE9FQD\ndLkI6HCxfaCAXhYBPdUAXS4COlxsHyigl0VATzVAl4uADhfbBwroZRHQUw3Q5SKgw8X2gQJ6\nWQT0VAN0uQjocLF9oIBeFgE91QBdLgI6XGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBTDdDlIqDD\nxfaBAnpZBPRUA3S5COhwsX2ggF4WAT3VAF0uAjpcbB8ooJdFQE81QJeLgA4X2wcK6GUR0FMN\n0OUioMPF9oECelkE9FQDdLkI6HCxfaCAXhYBPdUAXS4COlxsHyigl0VATzVAl4uADhfbBwro\nZRHQUw3Q5SKgw8X2gQJ6WQT0VAN0uQjocLF9oIBeFgE91QBdLgI6XGwfKKCXRUBPNUCXi4AO\nF9sHCuhlEdBTDdDlIqDDxfaBAnpZBPRUA3S5COhwsX2ggF4WAT3VAF0uAjpcbB8ooJdFQE81\nQJeLgA4X2wcK6GUR0FMN0OUioMPF9oECelkE9FQDdLkI6HCxfaCAXhYBPdUAXS4COlxsHyig\nl0VATzVAl4uADhfbBwroZRHQUw3Q5SKgw8X2gQJ6WQT0VAN0uQjocLF9oIBeFgE91QBdLgI6\nXGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBTDdDlIqDDxfaBAnpZBPRUA3S5COhwsX2ggF4WAT3V\nAF0uAjpcbB8ooJdFQE81QJeLgA4X2wcK6GUR0FMN0OUioMPF9oECelkE9FQDdLkI6HCxfaCA\nXhYBPdUAXS4COlxsHyigl0VATzVAl4uADhfbBwroZRHQUw3Q5SKgw8X2gQJ6WQT0VAN0uQjo\ncLF9oIBeFgE91QBdLgI6XGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBTDdDlIqDDxfaBAnpZBPRU\nA3S5COhwsX2ggF4WAT3VAF0uAjpcbB8ooJdFQE81QJeLgA4X2wcK6GUR0FMN0OUioMPF9oEC\nelkE9FQDdLkI6HCxfaCAXhYBPdUAXS4COlxsHyigl0VATzVAl4uADhfbBwroZRHQUw3Q5SKg\nw8X2gQJ6WQT0VAN0uQjocLF9oIBeFgE91QBdLgI6XGwfKKCXRUBPNUCXi4AOF9sHCuhlEdBT\nDdDlIqDDxfaBAnpZBPRUA3S5COhwsX2ggF4WAT3VAF0uAjpcbB8ooJdFQE81QJeLgA4X2wcK\n6GUR0FMN0OUioMPF9oECelkE9FQDdLkI6HCxfaDPB/p5c+ELejvFV//aXrn4pl+8y7mXrP0I\nxR/v64vlfIL+pvEJeqr5BF0u+gQdLrYPFNDLIqCnGqDLRUCHi+0DBfSyCOipBuhyEdDhYvtA\nAb0sAnqqAbpcBHS42D5QQC+LgJ5qgC4XAR0utg8U0MsioKcaoMtFQIeL7QMF9LII6KkG6HIR\n0OFi+0ABvSwCeqoBulwEdLjYPlBAL4uAnmqALhcBHS62DxTQyyKgpxqgy0VAh4vtAwX0sgjo\nqQbochHQ4WL7QAG9LAJ6qgG6XAR0uNg+UEAvi4CeaoAuFwEdLrYPFNDLIqCnGqDLRUCHi+0D\nBfSyCOipBuhyEdDhYvtAAb0sAnqqAbpcBHS42D5QQC+LgJ5qgC4XAR0utg8U0MsioKcaoMtF\nQIeL7QMF9LII6KkG6HIR0OFi+0ABvSwCeqoBulwEdLjYPlBAL4uAnmqALhcBHS62DxTQyyKg\npxqgy0VAh4vtAwX0sgjoqQbochHQ4WL7QAG9LAJ6qgG6XAR0uNg+UEAvi4CeaoAuFwEdLrYP\nFNDLIqCnGqDLRUCHi+0DBfSyCOipBuhyEdDhYvtAAb0sAnqqAbpcBHS42D5QQC+LgJ5qgC4X\nAR0utg8U0MsioKcaoMtFQIeL7QMF9LII6KkG6HIR0OFi+0ABvSwCeqoBulwEdLjYPlBAL4uA\nnmqALhcBHS62DxTQyyKgpxqgy0VAh4vtAwX0sgjoqQbochHQ4WL7QAG9LAJ6qgG6XAR0uNg+\nUEAvi4CeaoAuFwEdLrYPFNDLIqCnGqDLRUCHi+0DBfSyCOipBuhyEdDhYvtAAb0sAnqqAbpc\nBHS42D5QQC+LgJ5qgC4XAR0utg8U0MsioKcaoMtFQIeL7QMF9LII6KkG6HIR0OFi+0ABvSwC\neqoBulwEdLjYPlBAL4uAnmqALhcBHS62DxTQyyKgpxqgy0VAh4vtAwX0sgjoqQbochHQ4WL7\nQAG9LAJ6qgG6XAR0uNg+UEAvi4CeaoAuFwEdLrYPFNDLIqCnGqDLRUCHi+0DBfSyCOipBuhy\nEdDhYvtAAb0sAnqqAbpcBHS42D5QQC+LgJ5qgC4XAR0utg8U0MsioKcaoMtFQIeL7QMF9LII\n6KkG6HIR0OFi+0ABvSwCeqoBulwEdLjYPlBAL4uAnmqALhcBHS62DxTQyyKgpxqgy0VAh4vt\nAwX0sgjoqQbochHQ4WL7QAG9LAJ6qgG6XAR0uNg+UEAvi4CeaoAuFwEdLrYPFNDLIqCnGqDL\nRUCHi+0DBfSyCOipBuhyEdDhYvtAAb0sAnqqAbpcBHS42D5QQC+LgJ5qgC4XAR0utg8U0Msi\noKcaoMtFQIeL7QMF9LII6KkG6HIR0OFi+0ABvSwCeqoBulwEdLjYPlBAL4uAnmqALhcBHS62\nD/R7gL59P4D+rtRy1/W0322ALhcBHS62D/Q7gL799A2gt6nlrutpv9sAXS4COlxsHyigl0VA\nTzVAl4uADhfbBwroZRHQUw3Q5SKgw8X2gT4L6J/u51v/a8YYY75zOp+gH/6XIvT3eek5ZM9D\n1jxlz0PWtGh82nsCejmH7HnImqfseciaFo1Pe09AL+eQPQ9Z85Q9D1nTovFp7wno5Ryy5yFr\nnrLnIWtaND7tPQG9nEP2PGTNU/Y8ZE2Lxqe953cAHf2dhA+LhP4+Lz2H7HnImqfseciaFo1P\ne8/vAfrzSS0S+vu89Byy5yFrnrLnIWtaND7tPQG9nEP2PGTNU/Y8ZE2Lxqe9J6CXc8ieh6x5\nyp6HrGnR+LT3BPRyDtnzkDVP2fOQNS0an/aegF7OIXsesuYpex6ypkXj094T0Ms5ZM9D1jxl\nz0PWtGh82nsCejmH7HnImqfseciaFo1Pe09AL+eQPQ9Z85Q9D1nTovFp7wno5Ryy5yFrnrLn\nIWtaND7tPQG9nEP2PGTNU/Y8ZE2Lxqe9J6CXc8ieh6x5yp6HrGnR+LT3BPRyDtnzkDVP2fOQ\nNS0an/aegF7OIXsesuYpex6ypkXj094T0Ms5ZM9D1jxlz0PWtGh82nsCejmH7HnImqfsecia\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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