This is an Introduction to R workshop offered to business analytics students at Kelley School of Business

1. R Crash Course
Kelley 303
Some Material is adapted from YaRrr! The Pirate’s Guide to R
by Nathaniel D. Phillips (2018)
https://bookdown.org/ndphillips/YaRrr/
Technology and Business Analyst
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Olga Scrivner @obscrivn
https://www.linkedin.com/in/olgascrivner/

2. Outline
Part 1 – Introduction to R and RStudio
Part 2 – Working with data
Part 3 – R Graphics
Part 4 - Deployment, Sharing, and Publishing with R
2

3. Rstudio Basics
If you know Rstudio – skip this section
3

4. Getting Started
Two software packages: Base-R, and RStudio
4

5. Getting Started
RStudio is a graphical user interface interface for R.
http://www.rstudio.com/products/rstudio/download/
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6. Rstudio Window
1. SSRC desktop – start button
2. Rstudio Shortcut Icon
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7. Rstudio Screen
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8. Open New File
R scripts are just text files with the “.R” extension 8

9. Rstudio Tour
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10. Editor – “Source/Script”
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11. Console– “Command Line”
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12. History and Objects
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13. Extremely Useful Panel
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14. Files
The files panel gives you access to the file directory on your hard drive14

15. Files for Workshop
1. Download and unzip files from
https://languagevariationsuite.wordpress.com/
2. Create a Folder for CrashCourse in Desktop
3. Place unzip files in the CrashCourse folder
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16. Telling R Where Your Files Are
Select Session > Set Working Directory > Choose Directory >
Select CrashCourse folder in Desktop > Open
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17. Packages - Useful Libraries
1. Shows a list of all the R packages installed
2. Indicates whether the package is currently loaded
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18. Packages
Click to Activate and Deactivate a package
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19. Select Package Content - Stats
Click on Package to Access its content
Shortcut to find
it quick
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20. Scroll Stats Content
Do you recognize any statistical functions?
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21. Help!
1. TYPE IN CONSOLE:
?hist
2. CLICK ENTER
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22. Help!
1. Review the Help Panel
2. Scroll down the
histogram function:
What are the
arguments?
hist(x,...)
x – “a vector of values
for which the histogram
is desired”
22

23. Create Histogram
1. Return to your Editor file (it is empty)
2. Place your curser on Line 1
3. Type any set of numbers
• x = c(1,2,3,4,5)
4. Keep your cursor on line 1
5. Click RUN
6. On line 2, type
• hist(x)
7. Click RUN
Name
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24. Organizing Rstudio Layout
Try to adjust and change the size of panels.
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25. RUN versus ENTER: Editor versus Console
1. TYPE IN CONSOLE
2. CLICK ENTER KEY - keyboard
5. RESULTS - CONSOLE
3. TYPE IN EDITOR
4. CLICK RUN
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26. Opening and Saving Files
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27. Packages
Source: https://bookdown.org/ndphillips/YaRrr/packages.html
An R package is a bunch of data,
functions, examples (vignettes)
stored in one package.
CRAN is a main source of R packages.
https://cran.r-project.org/
27

28. Installing Packages Practice - Wordcloud
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29. Exploring Data
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30. Load CSV into R
NOTE: Remember about Working Directory? Go to Slide 16, if needed
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pirates = read.csv(“pirates.csv”)
By default for csv:
read.csv(file, header = TRUE, sep = “,”)
You can use single quotes or double quotes for file names

31. Data Storage in R
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32. View Entire Dataset
Click on pirates
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33. Take a Closer Look at Your Data
head() - take a look at the first few rows
Type head(pirates) in your script. Click run.
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34. Descriptive Statistics: Min, Max, Mean, Table
mean(pirates$age)
max(pirates$height)
table(pirates$sex)
pirates$age
Table name $ Column name
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Categorical variable
Continuous variable

35. Summary of Stats Functions
practice
each
function:
Type in
your
editor
examples
and RUN
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36. Aggregate Function
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Names of columns
Name of dataset
Type of Statistics
Change mean to sum: aggregate(formula = age ~ sex, data = pirates, FUN = sum)

37. Plotting
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You can select and highlight two lines, then click RUN. Or RUN each line at a time
If plot does not fit your window, adjust the Rstudio layout and RUN plot again
Error in plot.new() : figure margins too large

38. Saving Plot
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39. Adding Title, Labels, and Color
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40. Adding Regression Line
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41. Box Plot
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boxplot

42. How to Interpret Box Plot
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Source: https://www.wellbeingatschool.org.nz/information-sheet/understanding-and-interpreting-box-plots
Median - the mid-point of the data
Inter-quartile range - the middle 50%

43. How to Interpret Box Plot
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Source: https://www.wellbeingatschool.org.nz/information-sheet/understanding-and-interpreting-box-plots
The box plot is tall – a lot of variation
The box plot is short – a very small variation deviation
One box plot is much higher or lower than another –
a difference between groups
The 4 sections of the box plot are uneven in size –
more or less variation in different quartiles
Same median, different distribution

44. Hypothesis testing
44

45. T-test – two sample
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Difference between two variables
TWO SIDED - you are testing for the
possibility of the relationship in
both directions
ONE SIDED - you are testing for the
possibility of the relationship in one
direction (x > y OR x < y)
Source: https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-are-the-differences-between-one-tailed-and-two-tailed-tests/

46. T-test – two sample
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Difference between two variables
p-value of 0.7 - we fail to reject our
NULL hypothesis that there is NO
difference in the men age of pirates
who wear headbands and those that
do not

47. Correlation Testing
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Correlation between two variables
p-value < 0.0000000000000002 -
we reject our NULL hypothesis and
there is a significant (positive)
relationship between a pirate’s
height and weight

48. Linear Regression Model
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Can we use pirate’s age, weight, and number of tattoos
to predict how many treasure chests they found?
DV – dependent variable
IV – independent variable

49. Time Series
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50. Import EU Stocks Data 1991-1998
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eu <- read.csv("eustockmarkets.csv")
head(eu)

51. Create Time Series EU Stocks 1991-1998
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time_series <- ts(eu, start=1991, end=1998, frequency = 260)
start(time_series)
end(time_series)

52. Plot Time Series EU Stocks 1991-1998
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plot(time_series)

53. Time Series Plot EU Stocks 1991-1998
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ts.plot(time_series, col = 1:4, xlab = "Year", ylab = "Index Value", main
= " Major European Stock Indices, 1991-1998 ")
Color – based on columns

54. Add Legend - Time Series Plot EU Stocks 1991-1998
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ts.plot(time_series, col = 1:4, xlab = "Year", ylab = "Index Value", main
= " Major European Stock Indices, 1991-1998 ")
legend("topleft", legend = colnames(time_series), col = 1:4, lty = 1)

55. Debugging
55

56. Debugging R
Source: https://bookdown.org/ndphillips/YaRrr/debugging.html
The + symbol means that R is
Waiting for you to (properly) finish
Code (example – you forgot a closing
parenthesis)
The > symbol means that R is Ready
for new code.
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57. Debugging R
Source: https://bookdown.org/ndphillips/YaRrr/debugging.html
Console:
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58. Debugging R
Source: https://bookdown.org/ndphillips/YaRrr/debugging.html 58

59. Debugging R
Source: https://bookdown.org/ndphillips/YaRrr/debugging.html
Console:
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60. Debugging R
Source: https://bookdown.org/ndphillips/YaRrr/debugging.html
Console:
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61. Syntax
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62. R Syntax
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63. Strings
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64. Strings: Practice
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65. Vector
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66. Length
length() – a function
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