{
"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": {
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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",
"A data.frame: 53 × 1\n",
"\n",
"\t | additional_product |
|---|
\n",
"\t | <dbl> |
|---|
\n",
"\n",
"\n",
"\t| 2020-01-01 | 10 |
|---|
\n",
"\t| 2020-01-08 | 10 |
|---|
\n",
"\t| 2020-01-15 | 10 |
|---|
\n",
"\t| 2020-01-22 | 10 |
|---|
\n",
"\t| 2020-01-29 | 10 |
|---|
\n",
"\t| 2020-02-05 | 10 |
|---|
\n",
"\t| 2020-02-12 | 10 |
|---|
\n",
"\t| 2020-02-19 | 10 |
|---|
\n",
"\t| 2020-02-26 | 10 |
|---|
\n",
"\t| 2020-03-04 | 10 |
|---|
\n",
"\t| 2020-03-11 | 10 |
|---|
\n",
"\t| 2020-03-18 | 10 |
|---|
\n",
"\t| 2020-03-25 | 10 |
|---|
\n",
"\t| 2020-04-01 | 10 |
|---|
\n",
"\t| 2020-04-08 | 10 |
|---|
\n",
"\t| 2020-04-15 | 10 |
|---|
\n",
"\t| 2020-04-22 | 10 |
|---|
\n",
"\t| 2020-04-29 | 10 |
|---|
\n",
"\t| 2020-05-06 | 10 |
|---|
\n",
"\t| 2020-05-13 | 10 |
|---|
\n",
"\t| 2020-05-20 | 10 |
|---|
\n",
"\t| 2020-05-27 | 10 |
|---|
\n",
"\t| 2020-06-03 | 10 |
|---|
\n",
"\t| 2020-06-10 | 10 |
|---|
\n",
"\t| 2020-06-17 | 10 |
|---|
\n",
"\t| 2020-06-24 | 10 |
|---|
\n",
"\t| 2020-07-01 | 10 |
|---|
\n",
"\t| 2020-07-08 | 10 |
|---|
\n",
"\t| 2020-07-15 | 10 |
|---|
\n",
"\t| 2020-07-22 | 10 |
|---|
\n",
"\t| 2020-07-29 | 10 |
|---|
\n",
"\t| 2020-08-05 | 10 |
|---|
\n",
"\t| 2020-08-12 | 10 |
|---|
\n",
"\t| 2020-08-19 | 10 |
|---|
\n",
"\t| 2020-08-26 | 10 |
|---|
\n",
"\t| 2020-09-02 | 10 |
|---|
\n",
"\t| 2020-09-09 | 10 |
|---|
\n",
"\t| 2020-09-16 | 10 |
|---|
\n",
"\t| 2020-09-23 | 10 |
|---|
\n",
"\t| 2020-09-30 | 10 |
|---|
\n",
"\t| 2020-10-07 | 10 |
|---|
\n",
"\t| 2020-10-14 | 10 |
|---|
\n",
"\t| 2020-10-21 | 10 |
|---|
\n",
"\t| 2020-10-28 | 10 |
|---|
\n",
"\t| 2020-11-04 | 10 |
|---|
\n",
"\t| 2020-11-11 | 10 |
|---|
\n",
"\t| 2020-11-18 | 10 |
|---|
\n",
"\t| 2020-11-25 | 10 |
|---|
\n",
"\t| 2020-12-02 | 10 |
|---|
\n",
"\t| 2020-12-09 | 10 |
|---|
\n",
"\t| 2020-12-16 | 10 |
|---|
\n",
"\t| 2020-12-23 | 10 |
|---|
\n",
"\t| 2020-12-30 | 10 |
|---|
\n",
"\n",
"
\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",
"A data.frame: 366 × 1\n",
"\n",
"\t | total |
|---|
\n",
"\t | <dbl> |
|---|
\n",
"\n",
"\n",
"\t| 2020-01-01 | 53.59979 |
|---|
\n",
"\t| 2020-01-02 | NA |
|---|
\n",
"\t| 2020-01-03 | NA |
|---|
\n",
"\t| 2020-01-04 | NA |
|---|
\n",
"\t| 2020-01-05 | NA |
|---|
\n",
"\t| 2020-01-06 | NA |
|---|
\n",
"\t| 2020-01-07 | NA |
|---|
\n",
"\t| 2020-01-08 | 40.93455 |
|---|
\n",
"\t| 2020-01-09 | NA |
|---|
\n",
"\t| 2020-01-10 | NA |
|---|
\n",
"\t| 2020-01-11 | NA |
|---|
\n",
"\t| 2020-01-12 | NA |
|---|
\n",
"\t| 2020-01-13 | NA |
|---|
\n",
"\t| 2020-01-14 | NA |
|---|
\n",
"\t| 2020-01-15 | 59.24704 |
|---|
\n",
"\t| 2020-01-16 | NA |
|---|
\n",
"\t| 2020-01-17 | NA |
|---|
\n",
"\t| 2020-01-18 | NA |
|---|
\n",
"\t| 2020-01-19 | NA |
|---|
\n",
"\t| 2020-01-20 | NA |
|---|
\n",
"\t| 2020-01-21 | NA |
|---|
\n",
"\t| 2020-01-22 | 38.26416 |
|---|
\n",
"\t| 2020-01-23 | NA |
|---|
\n",
"\t| 2020-01-24 | NA |
|---|
\n",
"\t| 2020-01-25 | NA |
|---|
\n",
"\t| 2020-01-26 | NA |
|---|
\n",
"\t| 2020-01-27 | NA |
|---|
\n",
"\t| 2020-01-28 | NA |
|---|
\n",
"\t| 2020-01-29 | 44.58327 |
|---|
\n",
"\t| 2020-01-30 | NA |
|---|
\n",
"\t| ... | ... |
|---|
\n",
"\t| 2020-12-02 | 41.74811 |
|---|
\n",
"\t| 2020-12-03 | NA |
|---|
\n",
"\t| 2020-12-04 | NA |
|---|
\n",
"\t| 2020-12-05 | NA |
|---|
\n",
"\t| 2020-12-06 | NA |
|---|
\n",
"\t| 2020-12-07 | NA |
|---|
\n",
"\t| 2020-12-08 | NA |
|---|
\n",
"\t| 2020-12-09 | 37.85650 |
|---|
\n",
"\t| 2020-12-10 | NA |
|---|
\n",
"\t| 2020-12-11 | NA |
|---|
\n",
"\t| 2020-12-12 | NA |
|---|
\n",
"\t| 2020-12-13 | NA |
|---|
\n",
"\t| 2020-12-14 | NA |
|---|
\n",
"\t| 2020-12-15 | NA |
|---|
\n",
"\t| 2020-12-16 | 46.73560 |
|---|
\n",
"\t| 2020-12-17 | NA |
|---|
\n",
"\t| 2020-12-18 | NA |
|---|
\n",
"\t| 2020-12-19 | NA |
|---|
\n",
"\t| 2020-12-20 | NA |
|---|
\n",
"\t| 2020-12-21 | NA |
|---|
\n",
"\t| 2020-12-22 | NA |
|---|
\n",
"\t| 2020-12-23 | 40.42143 |
|---|
\n",
"\t| 2020-12-24 | NA |
|---|
\n",
"\t| 2020-12-25 | NA |
|---|
\n",
"\t| 2020-12-26 | NA |
|---|
\n",
"\t| 2020-12-27 | NA |
|---|
\n",
"\t| 2020-12-28 | NA |
|---|
\n",
"\t| 2020-12-29 | NA |
|---|
\n",
"\t| 2020-12-30 | 41.20298 |
|---|
\n",
"\t| 2020-12-31 | NA |
|---|
\n",
"\n",
"
\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",
"A data.frame: 366 × 1\n",
"\n",
"\t | total |
|---|
\n",
"\t | <dbl> |
|---|
\n",
"\n",
"\n",
"\t| 2020-01-01 | 53.59979 |
|---|
\n",
"\t| 2020-01-02 | 30.41127 |
|---|
\n",
"\t| 2020-01-03 | 48.54839 |
|---|
\n",
"\t| 2020-01-04 | 39.20897 |
|---|
\n",
"\t| 2020-01-05 | 39.09894 |
|---|
\n",
"\t| 2020-01-06 | 47.53019 |
|---|
\n",
"\t| 2020-01-07 | 44.94766 |
|---|
\n",
"\t| 2020-01-08 | 40.93455 |
|---|
\n",
"\t| 2020-01-09 | 37.66561 |
|---|
\n",
"\t| 2020-01-10 | 31.68825 |
|---|
\n",
"\t| 2020-01-11 | 45.30576 |
|---|
\n",
"\t| 2020-01-12 | 26.45509 |
|---|
\n",
"\t| 2020-01-13 | 45.81249 |
|---|
\n",
"\t| 2020-01-14 | 46.84547 |
|---|
\n",
"\t| 2020-01-15 | 59.24704 |
|---|
\n",
"\t| 2020-01-16 | 29.28688 |
|---|
\n",
"\t| 2020-01-17 | 32.41731 |
|---|
\n",
"\t| 2020-01-18 | 45.23295 |
|---|
\n",
"\t| 2020-01-19 | 48.54330 |
|---|
\n",
"\t| 2020-01-20 | 36.69353 |
|---|
\n",
"\t| 2020-01-21 | 43.09588 |
|---|
\n",
"\t| 2020-01-22 | 38.26416 |
|---|
\n",
"\t| 2020-01-23 | 45.56863 |
|---|
\n",
"\t| 2020-01-24 | 25.70944 |
|---|
\n",
"\t| 2020-01-25 | 37.38721 |
|---|
\n",
"\t| 2020-01-26 | 44.53955 |
|---|
\n",
"\t| 2020-01-27 | 46.88427 |
|---|
\n",
"\t| 2020-01-28 | 48.05540 |
|---|
\n",
"\t| 2020-01-29 | 44.58327 |
|---|
\n",
"\t| 2020-01-30 | 26.19490 |
|---|
\n",
"\t| ... | ... |
|---|
\n",
"\t| 2020-12-02 | 41.74811 |
|---|
\n",
"\t| 2020-12-03 | 35.03915 |
|---|
\n",
"\t| 2020-12-04 | 25.84637 |
|---|
\n",
"\t| 2020-12-05 | 27.73147 |
|---|
\n",
"\t| 2020-12-06 | 39.00993 |
|---|
\n",
"\t| 2020-12-07 | 41.03187 |
|---|
\n",
"\t| 2020-12-08 | 26.33862 |
|---|
\n",
"\t| 2020-12-09 | 37.85650 |
|---|
\n",
"\t| 2020-12-10 | 41.98943 |
|---|
\n",
"\t| 2020-12-11 | 36.68901 |
|---|
\n",
"\t| 2020-12-12 | 46.96883 |
|---|
\n",
"\t| 2020-12-13 | 39.70374 |
|---|
\n",
"\t| 2020-12-14 | 46.59464 |
|---|
\n",
"\t| 2020-12-15 | 41.24742 |
|---|
\n",
"\t| 2020-12-16 | 46.73560 |
|---|
\n",
"\t| 2020-12-17 | 32.68275 |
|---|
\n",
"\t| 2020-12-18 | 46.64238 |
|---|
\n",
"\t| 2020-12-19 | 25.22163 |
|---|
\n",
"\t| 2020-12-20 | 39.79997 |
|---|
\n",
"\t| 2020-12-21 | 34.45013 |
|---|
\n",
"\t| 2020-12-22 | 48.71183 |
|---|
\n",
"\t| 2020-12-23 | 40.42143 |
|---|
\n",
"\t| 2020-12-24 | 32.41991 |
|---|
\n",
"\t| 2020-12-25 | 39.12296 |
|---|
\n",
"\t| 2020-12-26 | 29.43616 |
|---|
\n",
"\t| 2020-12-27 | 39.09337 |
|---|
\n",
"\t| 2020-12-28 | 38.09288 |
|---|
\n",
"\t| 2020-12-29 | 41.00681 |
|---|
\n",
"\t| 2020-12-30 | 41.20298 |
|---|
\n",
"\t| 2020-12-31 | 43.25232 |
|---|
\n",
"\n",
"
\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": {
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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": "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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",
"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",
"A data.frame: 9 × 2\n",
"\n",
"\t | a | b |
|---|
\n",
"\t | <int> | <chr> |
|---|
\n",
"\n",
"\n",
"\t| 1 | 1 | I |
|---|
\n",
"\t| 2 | 2 | like |
|---|
\n",
"\t| 3 | 3 | to |
|---|
\n",
"\t| 4 | 4 | use |
|---|
\n",
"\t| 5 | 5 | Python |
|---|
\n",
"\t| 6 | 6 | and |
|---|
\n",
"\t| 7 | 7 | Pandas |
|---|
\n",
"\t| 8 | 8 | very |
|---|
\n",
"\t| 9 | 9 | much |
|---|
\n",
"\n",
"
\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",
"A data.frame: 9 × 2\n",
"\n",
"\t | A | B |
|---|
\n",
"\t | <int> | <chr> |
|---|
\n",
"\n",
"\n",
"\t| 1 | 1 | I |
|---|
\n",
"\t| 2 | 2 | like |
|---|
\n",
"\t| 3 | 3 | to |
|---|
\n",
"\t| 4 | 4 | use |
|---|
\n",
"\t| 5 | 5 | Python |
|---|
\n",
"\t| 6 | 6 | and |
|---|
\n",
"\t| 7 | 7 | Pandas |
|---|
\n",
"\t| 8 | 8 | very |
|---|
\n",
"\t| 9 | 9 | much |
|---|
\n",
"\n",
"
\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": "markdown",
"id": "8ac0204f",
"metadata": {},
"source": [
"We can also select a column in a dataframe using select function"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "88b51fdc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column A (series):\n"
]
},
{
"data": {
"text/html": [
"\n",
"A data.frame: 9 × 1\n",
"\n",
"\t | A |
|---|
\n",
"\t | <int> |
|---|
\n",
"\n",
"\n",
"\t| 1 | 1 |
|---|
\n",
"\t| 2 | 2 |
|---|
\n",
"\t| 3 | 3 |
|---|
\n",
"\t| 4 | 4 |
|---|
\n",
"\t| 5 | 5 |
|---|
\n",
"\t| 6 | 6 |
|---|
\n",
"\t| 7 | 7 |
|---|
\n",
"\t| 8 | 8 |
|---|
\n",
"\t| 9 | 9 |
|---|
\n",
"\n",
"
\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",
"A data.frame: 4 × 2\n",
"\n",
"\t | A | B |
|---|
\n",
"\t | <int> | <chr> |
|---|
\n",
"\n",
"\n",
"\t| 1 | 1 | I |
|---|
\n",
"\t| 2 | 2 | like |
|---|
\n",
"\t| 3 | 3 | to |
|---|
\n",
"\t| 4 | 4 | use |
|---|
\n",
"\n",
"
\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"
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" A B \n",
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"source": [
"df[df$A<5,]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "082277db",
"metadata": {},
"outputs": [
{
"data": {
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"\n",
"A data.frame: 1 × 2\n",
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|---|
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"\n",
"\n",
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"metadata": {},
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"source": [
"df[df$A>5 & df$A<7,]"
]
},
{
"cell_type": "markdown",
"id": "bf537050",
"metadata": {},
"source": [
"Creating a new columns. \n",
"\n",
"Code below creates a series which calculates the divergence of a from its mean value then merging into a existing dataframe."
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "0bbd19f8",
"metadata": {},
"outputs": [],
"source": [
"df$DivA <- df$A - mean(df$A)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "f36d96af",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"A data.frame: 9 × 3\n",
"\n",
"\t | A | B | DivA |
|---|
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"\t | <int> | <chr> | <dbl> |
|---|
\n",
"\n",
"\n",
"\t| 1 | 1 | I | -4 |
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"\t| 2 | 2 | like | -3 |
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"A data.frame: 9 × 3\n",
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"\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",
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"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": {
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"\n",
"A data.frame: 9 × 4\n",
"\n",
"\t | A | B | DivA | LenB |
|---|
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"\t | <int> | <chr> | <dbl> | <int> |
|---|
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"\n",
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"\t| 1 | 1 | I | -4 | 1 |
|---|
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"\t9 & 9 & much & 4 & 4\\\\\n",
"\\end{tabular}\n"
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"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"
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" A B DivA LenB\n",
"1 1 I -4 1 \n",
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"8 8 very 3 4 \n",
"9 9 much 4 4 "
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],
"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",
"A data.frame: 5 × 4\n",
"\n",
"\t | A | B | DivA | LenB |
|---|
\n",
"\t | <int> | <chr> | <dbl> | <int> |
|---|
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"A data.frame: 5 × 4\n",
"\\begin{tabular}{r|llll}\n",
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"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"
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" A B DivA LenB\n",
"1 1 I -4 1 \n",
"2 2 like -3 4 \n",
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],
"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",
"A tibble: 5 × 2\n",
"\n",
"\t| LenB | a |
|---|
\n",
"\t| <int> | <dbl> |
|---|
\n",
"\n",
"\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",
"\n",
"
\n"
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"A tibble: 5 × 2\n",
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" & \\\\\n",
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"\\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",
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]
},
"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 information."
]
},
{
"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",
"A data.frame: 6 × 4\n",
"\n",
"\t | A | B | DivA | LenB |
|---|
\n",
"\t | <int> | <chr> | <dbl> | <int> |
|---|
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|---|
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|---|
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|---|
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"\n",
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\n"
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"A data.frame: 6 × 4\n",
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" & A & B & DivA & LenB\\\\\n",
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"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",
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"6 6 and 1 3 "
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},
"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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",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 360,
"width": 720
}
},
"output_type": "display_data"
}
],
"source": [
"plot(df$A,type = 'o',xlab = \"no\",ylab = \"A\")"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "41b872c9",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 360,
"width": 720
}
},
"output_type": "display_data"
}
],
"source": [
"barplot(df$A, ylab = 'A',xlab = 'no')"
]
}
],
"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
}