{"id":5,"date":"2022-04-06T20:12:43","date_gmt":"2022-04-07T00:12:43","guid":{"rendered":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/?p=5"},"modified":"2023-05-05T13:23:49","modified_gmt":"2023-05-05T17:23:49","slug":"chapter-1","status":"publish","type":"chapter","link":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/chapter\/chapter-1\/","title":{"raw":"Introduction to R","rendered":"Introduction to R"},"content":{"raw":"<em>For the files associated with this Intro, including the Rmd file used to create this page, go to <a href=\"https:\/\/github.com\/amygoldlist\/BusinessAnalytics\/tree\/main\/Introduction_to_R\">https:\/\/github.com\/amygoldlist\/BusinessAnalytics\/tree\/main\/Introduction_to_R<\/a> <\/em>\r\n\r\nHere\u2019s some fun information for you to do on your own:\r\n\r\n[embed]https:\/\/www.youtube.com\/embed\/wBybv2ikk7U[\/embed]\r\n<div id=\"installing-r-on-your-own-computer\" class=\"section level2\">\r\n<h2>Installing R on your own computer:<\/h2>\r\n[embed]https:\/\/www.youtube.com\/embed\/RZxT3UrshsQ[\/embed]\r\n<div id=\"links\" class=\"section level3\">\r\n<h3>Links:<\/h3>\r\nThere are two things to install. RStudio is the IDE (development environment), and R is the scripting language. They are both free!\r\n<ul>\r\n \t<li><a class=\"uri\" href=\"https:\/\/rstudio.com\/products\/rstudio\/download\/\">https:\/\/rstudio.com\/products\/rstudio\/download\/<\/a><\/li>\r\n \t<li><a class=\"uri\" href=\"https:\/\/cran.rstudio.com\/\">https:\/\/cran.rstudio.com\/<\/a><\/li>\r\n<\/ul>\r\n<\/div>\r\n<\/div>\r\n<div id=\"r-as-a-giant-calculator\" class=\"section level2\">\r\n<h2>R as a giant calculator:<\/h2>\r\n<pre class=\"r\"><code>## when I start a line with a #, it's a comment\r\n\r\n### Try to understand how the code is working!\r\n\r\n\r\n# The &lt;- (or alt -)  assigns a variable\r\nx &lt;- 5\r\n## now x is always 5\r\n\r\n\r\n##  what is 8 times 5?\r\n8*x<\/code><\/pre>\r\n<pre><code>## [1] 40<\/code><\/pre>\r\n<pre class=\"r\"><code>## now make x be 7\r\nx &lt;- 7\r\n\r\n## hmm, 8x is different!\r\n8*x<\/code><\/pre>\r\n<pre><code>## [1] 56<\/code><\/pre>\r\n<\/div>\r\n<div id=\"a-data-problem\" class=\"section level2\">\r\n<h2>A data problem:<\/h2>\r\nSee the \u201cGiG\u201d worksheet, which is included in this bundle. This is from the textbook \u201cBusiness Analytics: Communicating with Numbers\u201d by Jaggia et al, available from McGraw Hill.\r\n\r\nThis data set contains Employees, categorized by Wage, Industry and Job. It contains missing info. Here\u2019s a glimpse:\r\n<table>\r\n<thead>\r\n<tr class=\"header\">\r\n<th><strong>EmployeeID<\/strong><\/th>\r\n<th><strong>HourlyWage<\/strong><\/th>\r\n<th><strong>Industry<\/strong><\/th>\r\n<th><strong>Job<\/strong><\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr class=\"odd\">\r\n<td>20<\/td>\r\n<td>26.09<\/td>\r\n<td>Construction<\/td>\r\n<td>Consultant<\/td>\r\n<\/tr>\r\n<tr class=\"even\">\r\n<td>21<\/td>\r\n<td>49.59<\/td>\r\n<td>Construction<\/td>\r\n<td><\/td>\r\n<\/tr>\r\n<tr class=\"odd\">\r\n<td>22<\/td>\r\n<td>47.97<\/td>\r\n<td>Construction<\/td>\r\n<td>Accountant<\/td>\r\n<\/tr>\r\n<tr class=\"even\">\r\n<td>23<\/td>\r\n<td>48.77<\/td>\r\n<td>Construction<\/td>\r\n<td>Engineer<\/td>\r\n<\/tr>\r\n<tr class=\"odd\">\r\n<td>24<\/td>\r\n<td>42.58<\/td>\r\n<td><\/td>\r\n<td>Sales Rep<\/td>\r\n<\/tr>\r\n<tr class=\"even\">\r\n<td>25<\/td>\r\n<td>49.7<\/td>\r\n<td>Automotive<\/td>\r\n<td>Engineer<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<div id=\"questions\" class=\"section level3\">\r\n<h3>Questions<\/h3>\r\nWe are now going to answer the following questions:\r\n<div id=\"find-number-of-missing-values\" class=\"section level5\">\r\n<h5>Find number of missing Values:<\/h5>\r\n<ul>\r\n \t<li>Hourly Wage<\/li>\r\n \t<li>Industry<\/li>\r\n \t<li>Job<\/li>\r\n<\/ul>\r\n<\/div>\r\n<div id=\"the-number-of-employees-who\" class=\"section level5\">\r\n<h5>The Number of employees who:<\/h5>\r\n<ul>\r\n \t<li>work in the automotive industry<\/li>\r\n \t<li>Earn More than $30 per hour<\/li>\r\n \t<li>Automotive Industry and earn more than $30 per hour<\/li>\r\n<\/ul>\r\n<\/div>\r\n<div id=\"find-the-hourly-wages\" class=\"section level5\">\r\n<h5>Find the Hourly wages:<\/h5>\r\n<ul>\r\n \t<li>Lowest:<\/li>\r\n \t<li>Highest:<\/li>\r\n \t<li>Lowest accountant in automotive:<\/li>\r\n \t<li>Highest accountant in automotive:<\/li>\r\n \t<li>Lowest accountant in tech:<\/li>\r\n \t<li>Highest accountant in tech:<\/li>\r\n<\/ul>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<div id=\"lets-try-excel\" class=\"section level2\">\r\n<h2>Let\u2019s try Excel<\/h2>\r\n[embed]https:\/\/www.youtube.com\/embed\/J80i6S7fUIs[\/embed]\r\n\r\n<\/div>\r\n<div id=\"and-r\" class=\"section level2\">\r\n<h2>And R:<\/h2>\r\n[embed]https:\/\/www.youtube.com\/embed\/PUDEHnBgUnw[\/embed]\r\n<div id=\"packages-or-libraries\" class=\"section level3\">\r\n<h3>Packages (or libraries)<\/h3>\r\nWhy recreate the wheel, when someone has already doen the work for us?\r\n<pre class=\"r\"><code>### if this is your first time using this, open these files:\r\n# Dlete the # at the beginign of the line!\r\n\r\n## Go to the end of each line and press ctrl+ enter\r\n# install.packages(\"dplyr\")\r\n# install.packages(\"openxlsx\")\r\n# install.packages(\"ggplot2\")\r\n# install.packages(\"palmerpenguins\")\r\n\r\n## opening up these libraries or packages lets us use them\r\nlibrary(dplyr)  #dplyr is really great for organizing dataframes<\/code><\/pre>\r\n<pre><code>## Warning: package 'dplyr' was built under R version 4.0.5<\/code><\/pre>\r\n<pre><code>## \r\n## Attaching package: 'dplyr'<\/code><\/pre>\r\n<pre><code>## The following objects are masked from 'package:stats':\r\n## \r\n##     filter, lag<\/code><\/pre>\r\n<pre><code>## The following objects are masked from 'package:base':\r\n## \r\n##     intersect, setdiff, setequal, union<\/code><\/pre>\r\n<pre class=\"r\"><code>library(openxlsx)  #openxlsx lets us read and write to Excel files. \r\nlibrary(ggplot2) ## this is for making visualizations<\/code><\/pre>\r\n<pre><code>## Warning: package 'ggplot2' was built under R version 4.0.5<\/code><\/pre>\r\n<pre class=\"r\"><code>library(palmerpenguins) # this is actually just cute penguins!<\/code><\/pre>\r\n<pre><code>## Warning: package 'palmerpenguins' was built under R version 4.0.5<\/code><\/pre>\r\n<\/div>\r\n<div id=\"open-the-file\" class=\"section level3\">\r\n<h3>Open the file<\/h3>\r\n<pre class=\"r\"><code>## Wait, where am I on my computer?\r\n\r\ngetwd()<\/code><\/pre>\r\n<pre><code>## [1] \"S:\/Personal Folders\/2021_current\/BABI_courses\/Starting_R_for_BABI\"<\/code><\/pre>\r\n<pre class=\"r\"><code>## Let's read in our Excel file.  The sheet option tells us which worksheet to use\r\ngig &lt;- read.xlsx(\"jaggia_ba_1e_ch02_Data_Files.xlsx\",sheet =\"Gig\")<\/code><\/pre>\r\n<\/div>\r\n<div id=\"and-lets-look-at-the-file\" class=\"section level3\">\r\n<h3>And let\u2019s look at the file:<\/h3>\r\n<pre class=\"r\"><code>## how big is our data set?\r\ndim(gig)  <\/code><\/pre>\r\n<pre><code>## [1] 604   4<\/code><\/pre>\r\n<pre class=\"r\"><code>## look at the first few rows\r\nhead(gig)  <\/code><\/pre>\r\n<pre><code>##   EmployeeID HourlyWage     Industry        Job\r\n## 1          1      32.81 Construction    Analyst\r\n## 2          2      46.00   Automotive   Engineer\r\n## 3          3      43.13 Construction  Sales Rep\r\n## 4          4      48.09   Automotive      Other\r\n## 5          5      43.62   Automotive Accountant\r\n## 6          6      46.98 Construction   Engineer<\/code><\/pre>\r\n<pre class=\"r\"><code>## look at the whole thing in a different window\r\n# View(gig)  \r\n\r\n\r\n## what are the columns like?  str = structure\r\nstr(gig)<\/code><\/pre>\r\n<pre><code>## 'data.frame':    604 obs. of  4 variables:\r\n##  $ EmployeeID: num  1 2 3 4 5 6 7 8 9 10 ...\r\n##  $ HourlyWage: num  32.8 46 43.1 48.1 43.6 ...\r\n##  $ Industry  : chr  \"Construction\" \"Automotive\" \"Construction\" \"Automotive\" ...\r\n##  $ Job       : chr  \"Analyst\" \"Engineer\" \"Sales Rep\" \"Other\" ...<\/code><\/pre>\r\n<\/div>\r\n<div id=\"answering-the-questions\" class=\"section level3\">\r\n<h3>Answering the questions<\/h3>\r\n<div id=\"find-number-of-missing-values-1\" class=\"section level5\">\r\n<h5>Find number of missing Values:<\/h5>\r\n<ul>\r\n \t<li>Hourly Wage<\/li>\r\n<\/ul>\r\n<pre class=\"r\"><code>## ctrl shift m makes the cool \"pipe\" %&gt;%\r\n##step1:  pull up the gig dataset\r\ngig %&gt;% \r\n  ##step 2:  filter only the blank hourly wage\r\n  filter(is.na(HourlyWage))<\/code><\/pre>\r\n<pre><code>## [1] EmployeeID HourlyWage Industry   Job       \r\n## &lt;0 rows&gt; (or 0-length row.names)<\/code><\/pre>\r\n<ul>\r\n \t<li>Industry<\/li>\r\n<\/ul>\r\n<pre class=\"r\"><code>gig %&gt;% \r\n  ## filter the blanks in Industry\r\n  filter(is.na(Industry))<\/code><\/pre>\r\n<pre><code>##    EmployeeID HourlyWage Industry        Job\r\n## 1          24      42.58     &lt;NA&gt;  Sales Rep\r\n## 2         139      42.18     &lt;NA&gt;   Engineer\r\n## 3         361      31.33     &lt;NA&gt;      Other\r\n## 4         378      48.09     &lt;NA&gt;      Other\r\n## 5         441      32.35     &lt;NA&gt; Accountant\r\n## 6         446      30.76     &lt;NA&gt; Accountant\r\n## 7         479      42.85     &lt;NA&gt; Consultant\r\n## 8         500      43.13     &lt;NA&gt;  Sales Rep\r\n## 9         531      43.13     &lt;NA&gt;   Engineer\r\n## 10        565      38.98     &lt;NA&gt; Accountant<\/code><\/pre>\r\n<ul>\r\n \t<li>Job<\/li>\r\n<\/ul>\r\n<pre class=\"r\"><code>### and the blank jobs\r\ngig %&gt;% \r\n  filter(is.na(Job))<\/code><\/pre>\r\n<pre><code>##    EmployeeID HourlyWage     Industry  Job\r\n## 1          21      49.59 Construction &lt;NA&gt;\r\n## 2          58      44.90 Construction &lt;NA&gt;\r\n## 3          66      26.09 Construction &lt;NA&gt;\r\n## 4          89      41.93 Construction &lt;NA&gt;\r\n## 5         108      43.12 Construction &lt;NA&gt;\r\n## 6         175      48.80   Automotive &lt;NA&gt;\r\n## 7         212      30.74 Construction &lt;NA&gt;\r\n## 8         253      44.90 Construction &lt;NA&gt;\r\n## 9         291      26.09 Construction &lt;NA&gt;\r\n## 10        347      26.09 Construction &lt;NA&gt;\r\n## 11        355      45.00   Automotive &lt;NA&gt;\r\n## 12        387      28.44 Construction &lt;NA&gt;\r\n## 13        388      32.96 Construction &lt;NA&gt;\r\n## 14        555      44.90 Construction &lt;NA&gt;\r\n## 15        577      27.90   Automotive &lt;NA&gt;\r\n## 16        593      48.98   Automotive &lt;NA&gt;<\/code><\/pre>\r\n<\/div>\r\n<div id=\"the-number-of-employees-who-1\" class=\"section level5\">\r\n<h5>The Number of employees who:<\/h5>\r\n<ul>\r\n \t<li>work in the automotive industry<\/li>\r\n<\/ul>\r\n<pre class=\"r\"><code>## Now lets try to count stuff using summarize...\r\n\r\n## first take our whole data.frame\r\ngig %&gt;% \r\n  ## group by industry\r\n  group_by(Industry) %&gt;%\r\n  #then count the numbers n()\r\n  summarize(n())<\/code><\/pre>\r\n<pre><code>## # A tibble: 4 x 2\r\n##   Industry     `n()`\r\n##   &lt;chr&gt;        &lt;int&gt;\r\n## 1 Automotive     190\r\n## 2 Construction   366\r\n## 3 Tech            38\r\n## 4 &lt;NA&gt;            10<\/code><\/pre>\r\n<ul>\r\n \t<li>Earn More than $30 per hour<\/li>\r\n<\/ul>\r\n<pre class=\"r\"><code>gig %&gt;% \r\n  ## filter by wage greater than $30\r\n  filter(HourlyWage&gt; 30) %&gt;% \r\n  ##and count them\r\n  count()<\/code><\/pre>\r\n<pre><code>##     n\r\n## 1 536<\/code><\/pre>\r\n<ul>\r\n \t<li>Automotive Industry and earn more than $30 per hour<\/li>\r\n<\/ul>\r\n<pre class=\"r\"><code>gig %&gt;% \r\n  filter(HourlyWage&gt; 30) %&gt;% \r\n  ## group by industry\r\n  group_by(Industry) %&gt;%\r\n  #then count the numbers n()\r\n  summarize(n())<\/code><\/pre>\r\n<pre><code>## # A tibble: 4 x 2\r\n##   Industry     `n()`\r\n##   &lt;chr&gt;        &lt;int&gt;\r\n## 1 Automotive     181\r\n## 2 Construction   311\r\n## 3 Tech            34\r\n## 4 &lt;NA&gt;            10<\/code><\/pre>\r\n<\/div>\r\n<div id=\"find-the-hourly-wages-1\" class=\"section level5\">\r\n<h5>Find the Hourly wages:<\/h5>\r\n<ul>\r\n \t<li>Lowest and Highest:<\/li>\r\n<\/ul>\r\n<pre class=\"r\"><code>gig %&gt;% \r\n  #then find maximum and minimum HourlyWage\r\n  summarize(min(HourlyWage),max(HourlyWage))<\/code><\/pre>\r\n<pre><code>##   min(HourlyWage) max(HourlyWage)\r\n## 1           24.28              51<\/code><\/pre>\r\n<ul>\r\n \t<li>Lowest and Highest accountant in automotive \/ Tech :<\/li>\r\n<\/ul>\r\n<pre class=\"r\"><code>## Step 1: pull up gig dataset\r\ngig %&gt;% \r\n  ## Step 2: filter by only Accountants\r\n  filter(Job == \"Accountant\") %&gt;% \r\n  ## Step 3: group by industry\r\n  group_by(Industry) %&gt;% \r\n  #then find maximum and minimum wage\r\n  summarize(min(HourlyWage),max(HourlyWage))<\/code><\/pre>\r\n<pre><code>## # A tibble: 4 x 3\r\n##   Industry     `min(HourlyWage)` `max(HourlyWage)`\r\n##   &lt;chr&gt;                    &lt;dbl&gt;             &lt;dbl&gt;\r\n## 1 Automotive                28.7              49.3\r\n## 2 Construction              24.3              49.9\r\n## 3 Tech                      36.1              49.5\r\n## 4 &lt;NA&gt;                      30.8              39.0<\/code><\/pre>\r\n<\/div>\r\n<\/div>\r\n<div id=\"pretty-pictures\" class=\"section level3\">\r\n<h3>Pretty pictures<\/h3>\r\nJust some basic plots:\r\n<pre class=\"r\"><code>## try commenting and uncommenting:\r\n\r\n#plot(gig)\r\n\r\n\r\n# gig %&gt;% ggplot(aes(y =HourlyWage))+\r\n#   geom_boxplot()\r\n# \r\n# gig %&gt;% ggplot(aes(y =HourlyWage, colour = Industry))+\r\n#   geom_boxplot()+\r\n#   theme_bw\r\n# \r\n# gig %&gt;% ggplot(aes(y =HourlyWage, colour = Job))+\r\n#   geom_boxplot()+\r\n#   theme_bw()\r\n# \r\n# \r\n# \r\n# gig %&gt;% ggplot(aes(x =HourlyWage))+\r\n#   geom_histogram()+\r\n#   theme_bw()\r\n# \r\n gig %&gt;% ggplot(aes(x =HourlyWage))+\r\n   geom_histogram(aes(fill = Industry))+\r\n   theme_bw()<\/code><\/pre>\r\n<pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.<\/code><\/pre>\r\n<img 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\" width=\"672\" \/>\r\n\r\n<\/div>\r\n<\/div>\r\n<div id=\"getting-fancy\" class=\"section level2\">\r\n<h2>Getting Fancy!<\/h2>\r\nLet\u2019s look at a different dataset. This one is all about penguins. Intrigued? (<a class=\"uri\" href=\"https:\/\/allisonhorst.github.io\/palmerpenguins\/\">https:\/\/allisonhorst.github.io\/palmerpenguins\/<\/a>)\r\n<pre class=\"r\"><code>## Look at the data\r\n\r\n\r\n\r\n## Look at penguins\r\n\r\npenguins<\/code><\/pre>\r\n<pre><code>## # A tibble: 344 x 8\r\n##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g\r\n##    &lt;fct&gt;   &lt;fct&gt;              &lt;dbl&gt;         &lt;dbl&gt;             &lt;int&gt;       &lt;int&gt;\r\n##  1 Adelie  Torgersen           39.1          18.7               181        3750\r\n##  2 Adelie  Torgersen           39.5          17.4               186        3800\r\n##  3 Adelie  Torgersen           40.3          18                 195        3250\r\n##  4 Adelie  Torgersen           NA            NA                  NA          NA\r\n##  5 Adelie  Torgersen           36.7          19.3               193        3450\r\n##  6 Adelie  Torgersen           39.3          20.6               190        3650\r\n##  7 Adelie  Torgersen           38.9          17.8               181        3625\r\n##  8 Adelie  Torgersen           39.2          19.6               195        4675\r\n##  9 Adelie  Torgersen           34.1          18.1               193        3475\r\n## 10 Adelie  Torgersen           42            20.2               190        4250\r\n## # ... with 334 more rows, and 2 more variables: sex &lt;fct&gt;, year &lt;int&gt;<\/code><\/pre>\r\n<div id=\"references\" class=\"section level3\">\r\n<h3>References:<\/h3>\r\n<ul>\r\n \t<li>Take some time and learn about R from the experts: <a class=\"uri\" href=\"https:\/\/education.rstudio.com\/learn\/beginner\/\">https:\/\/education.rstudio.com\/learn\/beginner\/<\/a><\/li>\r\n \t<li>This is an hour long webinar, but will give you a good overview of data wrangling in R: <a class=\"uri\" href=\"https:\/\/rstudio.com\/resources\/webinars\/a-gentle-introduction-to-tidy-statistics-in-r\/\">https:\/\/rstudio.com\/resources\/webinars\/a-gentle-introduction-to-tidy-statistics-in-r\/<\/a><\/li>\r\n \t<li>Follow some tutorials at <a class=\"uri\" href=\"https:\/\/stat545.com\/\">https:\/\/stat545.com\/<\/a><\/li>\r\n \t<li>Like this example? Buy the textbook: <a class=\"uri\" href=\"https:\/\/www.mheducation.com\/highered\/product\/business-analytics-jaggia-kelly\/M9781260785005.html\">https:\/\/www.mheducation.com\/highered\/product\/business-analytics-jaggia-kelly\/M9781260785005.html<\/a><\/li>\r\n<\/ul>\r\n<\/div>\r\n<\/div>","rendered":"<p><em>For the files associated with this Intro, including the Rmd file used to create this page, go to <a href=\"https:\/\/github.com\/amygoldlist\/BusinessAnalytics\/tree\/main\/Introduction_to_R\">https:\/\/github.com\/amygoldlist\/BusinessAnalytics\/tree\/main\/Introduction_to_R<\/a> <\/em><\/p>\n<p>Here\u2019s some fun information for you to do on your own:<\/p>\n<p><iframe loading=\"lazy\" id=\"oembed-1\" title=\"Preamble on R and Excel\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/wBybv2ikk7U?feature=oembed&#38;rel=0\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<div id=\"installing-r-on-your-own-computer\" class=\"section level2\">\n<h2>Installing R on your own computer:<\/h2>\n<p><iframe loading=\"lazy\" id=\"oembed-2\" title=\"Installing R and RStudio on your computer.\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/RZxT3UrshsQ?feature=oembed&#38;rel=0\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<div id=\"links\" class=\"section level3\">\n<h3>Links:<\/h3>\n<p>There are two things to install. RStudio is the IDE (development environment), and R is the scripting language. They are both free!<\/p>\n<ul>\n<li><a class=\"uri\" href=\"https:\/\/rstudio.com\/products\/rstudio\/download\/\">https:\/\/rstudio.com\/products\/rstudio\/download\/<\/a><\/li>\n<li><a class=\"uri\" href=\"https:\/\/cran.rstudio.com\/\">https:\/\/cran.rstudio.com\/<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n<div id=\"r-as-a-giant-calculator\" class=\"section level2\">\n<h2>R as a giant calculator:<\/h2>\n<pre class=\"r\"><code>## when I start a line with a #, it's a comment\r\n\r\n### Try to understand how the code is working!\r\n\r\n\r\n# The &lt;- (or alt -)  assigns a variable\r\nx &lt;- 5\r\n## now x is always 5\r\n\r\n\r\n##  what is 8 times 5?\r\n8*x<\/code><\/pre>\n<pre><code>## [1] 40<\/code><\/pre>\n<pre class=\"r\"><code>## now make x be 7\r\nx &lt;- 7\r\n\r\n## hmm, 8x is different!\r\n8*x<\/code><\/pre>\n<pre><code>## [1] 56<\/code><\/pre>\n<\/div>\n<div id=\"a-data-problem\" class=\"section level2\">\n<h2>A data problem:<\/h2>\n<p>See the \u201cGiG\u201d worksheet, which is included in this bundle. This is from the textbook \u201cBusiness Analytics: Communicating with Numbers\u201d by Jaggia et al, available from McGraw Hill.<\/p>\n<p>This data set contains Employees, categorized by Wage, Industry and Job. It contains missing info. Here\u2019s a glimpse:<\/p>\n<table>\n<thead>\n<tr class=\"header\">\n<th><strong>EmployeeID<\/strong><\/th>\n<th><strong>HourlyWage<\/strong><\/th>\n<th><strong>Industry<\/strong><\/th>\n<th><strong>Job<\/strong><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td>20<\/td>\n<td>26.09<\/td>\n<td>Construction<\/td>\n<td>Consultant<\/td>\n<\/tr>\n<tr class=\"even\">\n<td>21<\/td>\n<td>49.59<\/td>\n<td>Construction<\/td>\n<td><\/td>\n<\/tr>\n<tr class=\"odd\">\n<td>22<\/td>\n<td>47.97<\/td>\n<td>Construction<\/td>\n<td>Accountant<\/td>\n<\/tr>\n<tr class=\"even\">\n<td>23<\/td>\n<td>48.77<\/td>\n<td>Construction<\/td>\n<td>Engineer<\/td>\n<\/tr>\n<tr class=\"odd\">\n<td>24<\/td>\n<td>42.58<\/td>\n<td><\/td>\n<td>Sales Rep<\/td>\n<\/tr>\n<tr class=\"even\">\n<td>25<\/td>\n<td>49.7<\/td>\n<td>Automotive<\/td>\n<td>Engineer<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div id=\"questions\" class=\"section level3\">\n<h3>Questions<\/h3>\n<p>We are now going to answer the following questions:<\/p>\n<div id=\"find-number-of-missing-values\" class=\"section level5\">\n<h5>Find number of missing Values:<\/h5>\n<ul>\n<li>Hourly Wage<\/li>\n<li>Industry<\/li>\n<li>Job<\/li>\n<\/ul>\n<\/div>\n<div id=\"the-number-of-employees-who\" class=\"section level5\">\n<h5>The Number of employees who:<\/h5>\n<ul>\n<li>work in the automotive industry<\/li>\n<li>Earn More than $30 per hour<\/li>\n<li>Automotive Industry and earn more than $30 per hour<\/li>\n<\/ul>\n<\/div>\n<div id=\"find-the-hourly-wages\" class=\"section level5\">\n<h5>Find the Hourly wages:<\/h5>\n<ul>\n<li>Lowest:<\/li>\n<li>Highest:<\/li>\n<li>Lowest accountant in automotive:<\/li>\n<li>Highest accountant in automotive:<\/li>\n<li>Lowest accountant in tech:<\/li>\n<li>Highest accountant in tech:<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<div id=\"lets-try-excel\" class=\"section level2\">\n<h2>Let\u2019s try Excel<\/h2>\n<p><iframe loading=\"lazy\" id=\"oembed-3\" title=\"Intro to R: But first, Excel\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/J80i6S7fUIs?feature=oembed&#38;rel=0\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<\/div>\n<div id=\"and-r\" class=\"section level2\">\n<h2>And R:<\/h2>\n<p><iframe loading=\"lazy\" id=\"oembed-4\" title=\"Intro to R: Now actually with R\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/PUDEHnBgUnw?feature=oembed&#38;rel=0\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<div id=\"packages-or-libraries\" class=\"section level3\">\n<h3>Packages (or libraries)<\/h3>\n<p>Why recreate the wheel, when someone has already doen the work for us?<\/p>\n<pre class=\"r\"><code>### if this is your first time using this, open these files:\r\n# Dlete the # at the beginign of the line!\r\n\r\n## Go to the end of each line and press ctrl+ enter\r\n# install.packages(\"dplyr\")\r\n# install.packages(\"openxlsx\")\r\n# install.packages(\"ggplot2\")\r\n# install.packages(\"palmerpenguins\")\r\n\r\n## opening up these libraries or packages lets us use them\r\nlibrary(dplyr)  #dplyr is really great for organizing dataframes<\/code><\/pre>\n<pre><code>## Warning: package 'dplyr' was built under R version 4.0.5<\/code><\/pre>\n<pre><code>## \r\n## Attaching package: 'dplyr'<\/code><\/pre>\n<pre><code>## The following objects are masked from 'package:stats':\r\n## \r\n##     filter, lag<\/code><\/pre>\n<pre><code>## The following objects are masked from 'package:base':\r\n## \r\n##     intersect, setdiff, setequal, union<\/code><\/pre>\n<pre class=\"r\"><code>library(openxlsx)  #openxlsx lets us read and write to Excel files. \r\nlibrary(ggplot2) ## this is for making visualizations<\/code><\/pre>\n<pre><code>## Warning: package 'ggplot2' was built under R version 4.0.5<\/code><\/pre>\n<pre class=\"r\"><code>library(palmerpenguins) # this is actually just cute penguins!<\/code><\/pre>\n<pre><code>## Warning: package 'palmerpenguins' was built under R version 4.0.5<\/code><\/pre>\n<\/div>\n<div id=\"open-the-file\" class=\"section level3\">\n<h3>Open the file<\/h3>\n<pre class=\"r\"><code>## Wait, where am I on my computer?\r\n\r\ngetwd()<\/code><\/pre>\n<pre><code>## [1] \"S:\/Personal Folders\/2021_current\/BABI_courses\/Starting_R_for_BABI\"<\/code><\/pre>\n<pre class=\"r\"><code>## Let's read in our Excel file.  The sheet option tells us which worksheet to use\r\ngig &lt;- read.xlsx(\"jaggia_ba_1e_ch02_Data_Files.xlsx\",sheet =\"Gig\")<\/code><\/pre>\n<\/div>\n<div id=\"and-lets-look-at-the-file\" class=\"section level3\">\n<h3>And let\u2019s look at the file:<\/h3>\n<pre class=\"r\"><code>## how big is our data set?\r\ndim(gig)  <\/code><\/pre>\n<pre><code>## [1] 604   4<\/code><\/pre>\n<pre class=\"r\"><code>## look at the first few rows\r\nhead(gig)  <\/code><\/pre>\n<pre><code>##   EmployeeID HourlyWage     Industry        Job\r\n## 1          1      32.81 Construction    Analyst\r\n## 2          2      46.00   Automotive   Engineer\r\n## 3          3      43.13 Construction  Sales Rep\r\n## 4          4      48.09   Automotive      Other\r\n## 5          5      43.62   Automotive Accountant\r\n## 6          6      46.98 Construction   Engineer<\/code><\/pre>\n<pre class=\"r\"><code>## look at the whole thing in a different window\r\n# View(gig)  \r\n\r\n\r\n## what are the columns like?  str = structure\r\nstr(gig)<\/code><\/pre>\n<pre><code>## 'data.frame':    604 obs. of  4 variables:\r\n##  $ EmployeeID: num  1 2 3 4 5 6 7 8 9 10 ...\r\n##  $ HourlyWage: num  32.8 46 43.1 48.1 43.6 ...\r\n##  $ Industry  : chr  \"Construction\" \"Automotive\" \"Construction\" \"Automotive\" ...\r\n##  $ Job       : chr  \"Analyst\" \"Engineer\" \"Sales Rep\" \"Other\" ...<\/code><\/pre>\n<\/div>\n<div id=\"answering-the-questions\" class=\"section level3\">\n<h3>Answering the questions<\/h3>\n<div id=\"find-number-of-missing-values-1\" class=\"section level5\">\n<h5>Find number of missing Values:<\/h5>\n<ul>\n<li>Hourly Wage<\/li>\n<\/ul>\n<pre class=\"r\"><code>## ctrl shift m makes the cool \"pipe\" %&gt;%\r\n##step1:  pull up the gig dataset\r\ngig %&gt;% \r\n  ##step 2:  filter only the blank hourly wage\r\n  filter(is.na(HourlyWage))<\/code><\/pre>\n<pre><code>## [1] EmployeeID HourlyWage Industry   Job       \r\n## &lt;0 rows&gt; (or 0-length row.names)<\/code><\/pre>\n<ul>\n<li>Industry<\/li>\n<\/ul>\n<pre class=\"r\"><code>gig %&gt;% \r\n  ## filter the blanks in Industry\r\n  filter(is.na(Industry))<\/code><\/pre>\n<pre><code>##    EmployeeID HourlyWage Industry        Job\r\n## 1          24      42.58     &lt;NA&gt;  Sales Rep\r\n## 2         139      42.18     &lt;NA&gt;   Engineer\r\n## 3         361      31.33     &lt;NA&gt;      Other\r\n## 4         378      48.09     &lt;NA&gt;      Other\r\n## 5         441      32.35     &lt;NA&gt; Accountant\r\n## 6         446      30.76     &lt;NA&gt; Accountant\r\n## 7         479      42.85     &lt;NA&gt; Consultant\r\n## 8         500      43.13     &lt;NA&gt;  Sales Rep\r\n## 9         531      43.13     &lt;NA&gt;   Engineer\r\n## 10        565      38.98     &lt;NA&gt; Accountant<\/code><\/pre>\n<ul>\n<li>Job<\/li>\n<\/ul>\n<pre class=\"r\"><code>### and the blank jobs\r\ngig %&gt;% \r\n  filter(is.na(Job))<\/code><\/pre>\n<pre><code>##    EmployeeID HourlyWage     Industry  Job\r\n## 1          21      49.59 Construction &lt;NA&gt;\r\n## 2          58      44.90 Construction &lt;NA&gt;\r\n## 3          66      26.09 Construction &lt;NA&gt;\r\n## 4          89      41.93 Construction &lt;NA&gt;\r\n## 5         108      43.12 Construction &lt;NA&gt;\r\n## 6         175      48.80   Automotive &lt;NA&gt;\r\n## 7         212      30.74 Construction &lt;NA&gt;\r\n## 8         253      44.90 Construction &lt;NA&gt;\r\n## 9         291      26.09 Construction &lt;NA&gt;\r\n## 10        347      26.09 Construction &lt;NA&gt;\r\n## 11        355      45.00   Automotive &lt;NA&gt;\r\n## 12        387      28.44 Construction &lt;NA&gt;\r\n## 13        388      32.96 Construction &lt;NA&gt;\r\n## 14        555      44.90 Construction &lt;NA&gt;\r\n## 15        577      27.90   Automotive &lt;NA&gt;\r\n## 16        593      48.98   Automotive &lt;NA&gt;<\/code><\/pre>\n<\/div>\n<div id=\"the-number-of-employees-who-1\" class=\"section level5\">\n<h5>The Number of employees who:<\/h5>\n<ul>\n<li>work in the automotive industry<\/li>\n<\/ul>\n<pre class=\"r\"><code>## Now lets try to count stuff using summarize...\r\n\r\n## first take our whole data.frame\r\ngig %&gt;% \r\n  ## group by industry\r\n  group_by(Industry) %&gt;%\r\n  #then count the numbers n()\r\n  summarize(n())<\/code><\/pre>\n<pre><code>## # A tibble: 4 x 2\r\n##   Industry     `n()`\r\n##   &lt;chr&gt;        &lt;int&gt;\r\n## 1 Automotive     190\r\n## 2 Construction   366\r\n## 3 Tech            38\r\n## 4 &lt;NA&gt;            10<\/code><\/pre>\n<ul>\n<li>Earn More than $30 per hour<\/li>\n<\/ul>\n<pre class=\"r\"><code>gig %&gt;% \r\n  ## filter by wage greater than $30\r\n  filter(HourlyWage&gt; 30) %&gt;% \r\n  ##and count them\r\n  count()<\/code><\/pre>\n<pre><code>##     n\r\n## 1 536<\/code><\/pre>\n<ul>\n<li>Automotive Industry and earn more than $30 per hour<\/li>\n<\/ul>\n<pre class=\"r\"><code>gig %&gt;% \r\n  filter(HourlyWage&gt; 30) %&gt;% \r\n  ## group by industry\r\n  group_by(Industry) %&gt;%\r\n  #then count the numbers n()\r\n  summarize(n())<\/code><\/pre>\n<pre><code>## # A tibble: 4 x 2\r\n##   Industry     `n()`\r\n##   &lt;chr&gt;        &lt;int&gt;\r\n## 1 Automotive     181\r\n## 2 Construction   311\r\n## 3 Tech            34\r\n## 4 &lt;NA&gt;            10<\/code><\/pre>\n<\/div>\n<div id=\"find-the-hourly-wages-1\" class=\"section level5\">\n<h5>Find the Hourly wages:<\/h5>\n<ul>\n<li>Lowest and Highest:<\/li>\n<\/ul>\n<pre class=\"r\"><code>gig %&gt;% \r\n  #then find maximum and minimum HourlyWage\r\n  summarize(min(HourlyWage),max(HourlyWage))<\/code><\/pre>\n<pre><code>##   min(HourlyWage) max(HourlyWage)\r\n## 1           24.28              51<\/code><\/pre>\n<ul>\n<li>Lowest and Highest accountant in automotive \/ Tech :<\/li>\n<\/ul>\n<pre class=\"r\"><code>## Step 1: pull up gig dataset\r\ngig %&gt;% \r\n  ## Step 2: filter by only Accountants\r\n  filter(Job == \"Accountant\") %&gt;% \r\n  ## Step 3: group by industry\r\n  group_by(Industry) %&gt;% \r\n  #then find maximum and minimum wage\r\n  summarize(min(HourlyWage),max(HourlyWage))<\/code><\/pre>\n<pre><code>## # A tibble: 4 x 3\r\n##   Industry     `min(HourlyWage)` `max(HourlyWage)`\r\n##   &lt;chr&gt;                    &lt;dbl&gt;             &lt;dbl&gt;\r\n## 1 Automotive                28.7              49.3\r\n## 2 Construction              24.3              49.9\r\n## 3 Tech                      36.1              49.5\r\n## 4 &lt;NA&gt;                      30.8              39.0<\/code><\/pre>\n<\/div>\n<\/div>\n<div id=\"pretty-pictures\" class=\"section level3\">\n<h3>Pretty pictures<\/h3>\n<p>Just some basic plots:<\/p>\n<pre class=\"r\"><code>## try commenting and uncommenting:\r\n\r\n#plot(gig)\r\n\r\n\r\n# gig %&gt;% ggplot(aes(y =HourlyWage))+\r\n#   geom_boxplot()\r\n# \r\n# gig %&gt;% ggplot(aes(y =HourlyWage, colour = Industry))+\r\n#   geom_boxplot()+\r\n#   theme_bw\r\n# \r\n# gig %&gt;% ggplot(aes(y =HourlyWage, colour = Job))+\r\n#   geom_boxplot()+\r\n#   theme_bw()\r\n# \r\n# \r\n# \r\n# gig %&gt;% ggplot(aes(x =HourlyWage))+\r\n#   geom_histogram()+\r\n#   theme_bw()\r\n# \r\n gig %&gt;% ggplot(aes(x =HourlyWage))+\r\n   geom_histogram(aes(fill = Industry))+\r\n   theme_bw()<\/code><\/pre>\n<pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.<\/code><\/pre>\n<p><img decoding=\"async\" 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\" width=\"672\" alt=\"image\" \/><\/p>\n<\/div>\n<\/div>\n<div id=\"getting-fancy\" class=\"section level2\">\n<h2>Getting Fancy!<\/h2>\n<p>Let\u2019s look at a different dataset. This one is all about penguins. Intrigued? (<a class=\"uri\" href=\"https:\/\/allisonhorst.github.io\/palmerpenguins\/\">https:\/\/allisonhorst.github.io\/palmerpenguins\/<\/a>)<\/p>\n<pre class=\"r\"><code>## Look at the data\r\n\r\n\r\n\r\n## Look at penguins\r\n\r\npenguins<\/code><\/pre>\n<pre><code>## # A tibble: 344 x 8\r\n##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g\r\n##    &lt;fct&gt;   &lt;fct&gt;              &lt;dbl&gt;         &lt;dbl&gt;             &lt;int&gt;       &lt;int&gt;\r\n##  1 Adelie  Torgersen           39.1          18.7               181        3750\r\n##  2 Adelie  Torgersen           39.5          17.4               186        3800\r\n##  3 Adelie  Torgersen           40.3          18                 195        3250\r\n##  4 Adelie  Torgersen           NA            NA                  NA          NA\r\n##  5 Adelie  Torgersen           36.7          19.3               193        3450\r\n##  6 Adelie  Torgersen           39.3          20.6               190        3650\r\n##  7 Adelie  Torgersen           38.9          17.8               181        3625\r\n##  8 Adelie  Torgersen           39.2          19.6               195        4675\r\n##  9 Adelie  Torgersen           34.1          18.1               193        3475\r\n## 10 Adelie  Torgersen           42            20.2               190        4250\r\n## # ... with 334 more rows, and 2 more variables: sex &lt;fct&gt;, year &lt;int&gt;<\/code><\/pre>\n<div id=\"references\" class=\"section level3\">\n<h3>References:<\/h3>\n<ul>\n<li>Take some time and learn about R from the experts: <a class=\"uri\" href=\"https:\/\/education.rstudio.com\/learn\/beginner\/\">https:\/\/education.rstudio.com\/learn\/beginner\/<\/a><\/li>\n<li>This is an hour long webinar, but will give you a good overview of data wrangling in R: <a class=\"uri\" href=\"https:\/\/rstudio.com\/resources\/webinars\/a-gentle-introduction-to-tidy-statistics-in-r\/\">https:\/\/rstudio.com\/resources\/webinars\/a-gentle-introduction-to-tidy-statistics-in-r\/<\/a><\/li>\n<li>Follow some tutorials at <a class=\"uri\" href=\"https:\/\/stat545.com\/\">https:\/\/stat545.com\/<\/a><\/li>\n<li>Like this example? Buy the textbook: <a class=\"uri\" href=\"https:\/\/www.mheducation.com\/highered\/product\/business-analytics-jaggia-kelly\/M9781260785005.html\">https:\/\/www.mheducation.com\/highered\/product\/business-analytics-jaggia-kelly\/M9781260785005.html<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n","protected":false},"author":883,"menu_order":1,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[48],"contributor":[],"license":[],"class_list":["post-5","chapter","type-chapter","status-publish","hentry","chapter-type-standard"],"part":154,"_links":{"self":[{"href":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/wp-json\/pressbooks\/v2\/chapters\/5","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/wp-json\/wp\/v2\/users\/883"}],"version-history":[{"count":3,"href":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/wp-json\/pressbooks\/v2\/chapters\/5\/revisions"}],"predecessor-version":[{"id":75,"href":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/wp-json\/pressbooks\/v2\/chapters\/5\/revisions\/75"}],"part":[{"href":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/wp-json\/pressbooks\/v2\/parts\/154"}],"metadata":[{"href":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/wp-json\/pressbooks\/v2\/chapters\/5\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/wp-json\/wp\/v2\/media?parent=5"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/wp-json\/pressbooks\/v2\/chapter-type?post=5"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/wp-json\/wp\/v2\/contributor?post=5"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/businessanalytics\/wp-json\/wp\/v2\/license?post=5"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}