From 4b71d144aee60cc8b25cec3dcf55dc432dc39dca Mon Sep 17 00:00:00 2001 From: Keshav Sharma Date: Sat, 9 Oct 2021 21:16:52 -0700 Subject: [PATCH] Create pandas.ipynb --- 2-Working-With-Data/R/pandas.ipynb | 978 +++++++++++++++++++++++++++++ 1 file changed, 978 insertions(+) create mode 100644 2-Working-With-Data/R/pandas.ipynb diff --git a/2-Working-With-Data/R/pandas.ipynb b/2-Working-With-Data/R/pandas.ipynb new file mode 100644 index 0000000..cb92883 --- /dev/null +++ b/2-Working-With-Data/R/pandas.ipynb @@ -0,0 +1,978 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "304296e3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Attaching package: 'dplyr'\n", + "\n", + "\n", + "The following objects are masked from 'package:stats':\n", + "\n", + " filter, lag\n", + "\n", + "\n", + "The following objects are masked from 'package:base':\n", + "\n", + " intersect, setdiff, setequal, union\n", + "\n", + "\n", + "-- \u001b[1mAttaching packages\u001b[22m ------------------------------------------------------------------------------- tidyverse 1.3.1 --\n", + "\n", + "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.5 \u001b[32mv\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.4\n", + "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.1.5 \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n", + "\u001b[32mv\u001b[39m \u001b[34mtidyr \u001b[39m 1.1.4 \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1\n", + "\u001b[32mv\u001b[39m \u001b[34mreadr \u001b[39m 2.0.2 \n", + "\n", + "-- \u001b[1mConflicts\u001b[22m ---------------------------------------------------------------------------------- tidyverse_conflicts() --\n", + "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n", + "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n", + "\n" + ] + } + ], + "source": [ + "library(dplyr)\n", + "library(tidyverse)" + ] + }, + { + "cell_type": "markdown", + "id": "d786e051", + "metadata": {}, + "source": [ + "## Series" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f659f553", + "metadata": {}, + "outputs": [], + "source": [ + "a<- 1:9" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9acc193d", + "metadata": {}, + "outputs": [], + "source": [ + "b = c(\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f577ec14", + "metadata": {}, + "outputs": [], + "source": [ + "a1 = length(a)\n", + "b1 = length(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "31e069a0", + "metadata": {}, + "outputs": [], + "source": [ + "a = data.frame(a,row.names = c(1:a1))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "29ce166e", + "metadata": {}, + "outputs": [], + "source": [ + "b = data.frame(b,row.names = c(1:b1))" + ] + }, + { + "cell_type": "markdown", + "id": "945feffd", + "metadata": {}, + "source": [ + "## DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "88a435ec", + "metadata": {}, + "outputs": [], + "source": [ + "a = data.frame(a,row.names = c(1:a1))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c4e2a6c1", + "metadata": {}, + "outputs": [], + "source": [ + "b = data.frame(b,row.names = c(1:b1))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2bb5177c", + "metadata": {}, + "outputs": [], + "source": [ + "df<- data.frame(a,b)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8f45d3a5", + "metadata": {}, + "outputs": [], + "source": [ + "df = \n", + " rename(df,\n", + " A = a,\n", + " B = b,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0efbf2d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 2
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": 16, + "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
<int>
11
22
33
44
55
66
77
88
99
\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": "code", + "execution_count": 17, + "id": "010bcba8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 4 × 2
AB
<int><chr>
11I
22like
33to
44use
\n" + ], + "text/latex": [ + "A data.frame: 4 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & A & B\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I \\\\\n", + "\t2 & 2 & like\\\\\n", + "\t3 & 3 & to \\\\\n", + "\t4 & 4 & use \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 4 × 2\n", + "\n", + "| | A <int> | B <chr> |\n", + "|---|---|---|\n", + "| 1 | 1 | I |\n", + "| 2 | 2 | like |\n", + "| 3 | 3 | to |\n", + "| 4 | 4 | use |\n", + "\n" + ], + "text/plain": [ + " A B \n", + "1 1 I \n", + "2 2 like\n", + "3 3 to \n", + "4 4 use " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[df$A<5,]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "082277db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A data.frame: 1 × 2
AB
<int><chr>
66and
\n" + ], + "text/latex": [ + "A data.frame: 1 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & A & B\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t6 & 6 & and\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 1 × 2\n", + "\n", + "| | A <int> | B <chr> |\n", + "|---|---|---|\n", + "| 6 | 6 | and |\n", + "\n" + ], + "text/plain": [ + " A B \n", + "6 6 and" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[df$A>5 & df$A<7,]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0bbd19f8", + "metadata": {}, + "outputs": [], + "source": [ + "df$DivA <- df$A - mean(df$A)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "f36d96af", + "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 × 3
ABDivA
<int><chr><dbl>
11I -4
22like -3
33to -2
44use -1
55Python 0
66and 1
77Pandas 2
88very 3
99much 4
\n" + ], + "text/latex": [ + "A data.frame: 9 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & A & B & DivA\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4\\\\\n", + "\t2 & 2 & like & -3\\\\\n", + "\t3 & 3 & to & -2\\\\\n", + "\t4 & 4 & use & -1\\\\\n", + "\t5 & 5 & Python & 0\\\\\n", + "\t6 & 6 & and & 1\\\\\n", + "\t7 & 7 & Pandas & 2\\\\\n", + "\t8 & 8 & very & 3\\\\\n", + "\t9 & 9 & much & 4\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 3\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> |\n", + "|---|---|---|---|\n", + "| 1 | 1 | I | -4 |\n", + "| 2 | 2 | like | -3 |\n", + "| 3 | 3 | to | -2 |\n", + "| 4 | 4 | use | -1 |\n", + "| 5 | 5 | Python | 0 |\n", + "| 6 | 6 | and | 1 |\n", + "| 7 | 7 | Pandas | 2 |\n", + "| 8 | 8 | very | 3 |\n", + "| 9 | 9 | much | 4 |\n", + "\n" + ], + "text/plain": [ + " A B DivA\n", + "1 1 I -4 \n", + "2 2 like -3 \n", + "3 3 to -2 \n", + "4 4 use -1 \n", + "5 5 Python 0 \n", + "6 6 and 1 \n", + "7 7 Pandas 2 \n", + "8 8 very 3 \n", + "9 9 much 4 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c67f2bd0", + "metadata": {}, + "outputs": [], + "source": [ + "df$LenB <- str_length(df$B)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "cef214b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 9 × 4
ABDivALenB
<int><chr><dbl><int>
11I -41
22like -34
33to -22
44use -13
55Python 06
66and 13
77Pandas 26
88very 34
99much 44
\n" + ], + "text/latex": [ + "A data.frame: 9 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & A & B & DivA & LenB\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4 & 1\\\\\n", + "\t2 & 2 & like & -3 & 4\\\\\n", + "\t3 & 3 & to & -2 & 2\\\\\n", + "\t4 & 4 & use & -1 & 3\\\\\n", + "\t5 & 5 & Python & 0 & 6\\\\\n", + "\t6 & 6 & and & 1 & 3\\\\\n", + "\t7 & 7 & Pandas & 2 & 6\\\\\n", + "\t8 & 8 & very & 3 & 4\\\\\n", + "\t9 & 9 & much & 4 & 4\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 9 × 4\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | I | -4 | 1 |\n", + "| 2 | 2 | like | -3 | 4 |\n", + "| 3 | 3 | to | -2 | 2 |\n", + "| 4 | 4 | use | -1 | 3 |\n", + "| 5 | 5 | Python | 0 | 6 |\n", + "| 6 | 6 | and | 1 | 3 |\n", + "| 7 | 7 | Pandas | 2 | 6 |\n", + "| 8 | 8 | very | 3 | 4 |\n", + "| 9 | 9 | much | 4 | 4 |\n", + "\n" + ], + "text/plain": [ + " A B DivA LenB\n", + "1 1 I -4 1 \n", + "2 2 like -3 4 \n", + "3 3 to -2 2 \n", + "4 4 use -1 3 \n", + "5 5 Python 0 6 \n", + "6 6 and 1 3 \n", + "7 7 Pandas 2 6 \n", + "8 8 very 3 4 \n", + "9 9 much 4 4 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "59fe5316", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 5 × 4
ABDivALenB
<int><chr><dbl><int>
11I -41
22like -34
33to -22
44use -13
55Python 06
\n" + ], + "text/latex": [ + "A data.frame: 5 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & A & B & DivA & LenB\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4 & 1\\\\\n", + "\t2 & 2 & like & -3 & 4\\\\\n", + "\t3 & 3 & to & -2 & 2\\\\\n", + "\t4 & 4 & use & -1 & 3\\\\\n", + "\t5 & 5 & Python & 0 & 6\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 5 × 4\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | I | -4 | 1 |\n", + "| 2 | 2 | like | -3 | 4 |\n", + "| 3 | 3 | to | -2 | 2 |\n", + "| 4 | 4 | use | -1 | 3 |\n", + "| 5 | 5 | Python | 0 | 6 |\n", + "\n" + ], + "text/plain": [ + " A B DivA LenB\n", + "1 1 I -4 1 \n", + "2 2 like -3 4 \n", + "3 3 to -2 2 \n", + "4 4 use -1 3 \n", + "5 5 Python 0 6 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[0:5,]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "f944a949", + "metadata": {}, + "outputs": [], + "source": [ + " df1 = group_by(df,LenB)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "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
LenBmymean
<int><dbl>
11
22
33
44
66
\n" + ], + "text/latex": [ + "A tibble: 5 × 2\n", + "\\begin{tabular}{ll}\n", + " LenB & mymean\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t 1 & 1\\\\\n", + "\t 2 & 2\\\\\n", + "\t 3 & 3\\\\\n", + "\t 4 & 4\\\\\n", + "\t 6 & 6\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 5 × 2\n", + "\n", + "| LenB <int> | mymean <dbl> |\n", + "|---|---|\n", + "| 1 | 1 |\n", + "| 2 | 2 |\n", + "| 3 | 3 |\n", + "| 4 | 4 |\n", + "| 6 | 6 |\n", + "\n" + ], + "text/plain": [ + " LenB mymean\n", + "1 1 1 \n", + "2 2 2 \n", + "3 3 3 \n", + "4 4 4 \n", + "5 6 6 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summarise(df1,mymean =mean(LenB))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b859950", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "5d3f0287", + "metadata": {}, + "source": [ + "## Printing and Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "69946dc7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in file(file, \"rt\"):\n", + "\"cannot open file 'file name': No such file or directory\"\n" + ] + }, + { + "ename": "ERROR", + "evalue": "Error in file(file, \"rt\"): cannot open the connection\n", + "output_type": "error", + "traceback": [ + "Error in file(file, \"rt\"): cannot open the connection\nTraceback:\n", + "1. read.csv(\"file name\")", + "2. read.table(file = file, header = header, sep = sep, quote = quote, \n . dec = dec, fill = fill, comment.char = comment.char, ...)", + "3. file(file, \"rt\")" + ] + } + ], + "source": [ + "dataset = read.csv(\"file name\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "009af652", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 4
ABDivALenB
<int><chr><dbl><int>
11I -41
22like -34
33to -22
44use -13
55Python 06
66and 13
\n" + ], + "text/latex": [ + "A data.frame: 6 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & A & B & DivA & LenB\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & I & -4 & 1\\\\\n", + "\t2 & 2 & like & -3 & 4\\\\\n", + "\t3 & 3 & to & -2 & 2\\\\\n", + "\t4 & 4 & use & -1 & 3\\\\\n", + "\t5 & 5 & Python & 0 & 6\\\\\n", + "\t6 & 6 & and & 1 & 3\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 4\n", + "\n", + "| | A <int> | B <chr> | DivA <dbl> | LenB <int> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | I | -4 | 1 |\n", + "| 2 | 2 | like | -3 | 4 |\n", + "| 3 | 3 | to | -2 | 2 |\n", + "| 4 | 4 | use | -1 | 3 |\n", + "| 5 | 5 | Python | 0 | 6 |\n", + "| 6 | 6 | and | 1 | 3 |\n", + "\n" + ], + "text/plain": [ + " A B DivA LenB\n", + "1 1 I -4 1 \n", + "2 2 like -3 4 \n", + "3 3 to -2 2 \n", + "4 4 use -1 3 \n", + "5 5 Python 0 6 \n", + "6 6 and 1 3 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "515c95b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAMFBMVEUAAABNTU1oaGh8fHyM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\nCQIICQIICQL8H5sEkT1X9RA0AAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(df$A,type = 'o',xlab = \"no\",ylab = \"A\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "41b872c9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAM1BMVEUAAABNTU1oaGh8fHyM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\nAAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "barplot(df$A, ylab = 'A',xlab = 'no')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11001454", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "670db495", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.1.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}