The document provides instructions for an introductory R session, including downloading materials from a GitHub repository and opening an R project file. It outlines logging in, downloading an R project folder containing intro materials, and opening the project file in RStudio.

1. Welcome to an R intro!
1. Log in
2. Go to github.com/sjfox/2016_fall_intro_r,
and download the materials
3. Open up the 2016_fall_intro_r.Rproj in RStudio

2. Introduction to R
Spencer Fox
20 October 2016
spncrfx@gmail.com
@foxandtheflu

3. Why program?

4. Why program?
• Simulation

5. Why program?
• Simulation
• Automation

6. Why program?
• Simulation
• Automation
• Reproducibility

7. Why use R?

8. Why use R?
• Free

9. Why use R?
• Free
• Powerful Statistics

10. Why use R?
• Free
• Powerful Statistics
• Packages!

11. Why use R?
• Free
• Powerful Statistics
• Packages!
• Increasingly popular

12. Why use R?
• Free
• Powerful Statistics
• Packages!
• Increasingly popular
• Visualization

13. Always start with your end goal in mind

14. Always start with your end goal in mind
fivethirtyeight

15. Example R “Pipeline”

16. Example R “Pipeline”
1. Generate data

17. Example R “Pipeline”
1. Generate data
2. Analyze data

18. Example R “Pipeline”
1. Generate data
2. Analyze data
3. Show analysis

19. Example R “Pipeline”
1. Generate data
2. Analyze data
3. Show analysis
in R

20. Data Get data into R Analyze/calcu-
late data
Generate beautiful
figures
Share your results
Data analysis in the “tidyverse”
Slide created by
Sean Leonard

21. Data Get data into R Analyze/calcu-
late data
Generate beautiful
figures
Share your results
Data analysis in the “tidyverse”
Slide created by
Sean Leonard

22. Data Get data into R Analyze/calcu-
late data
Generate beautiful
figures
Share your results
Data analysis in the “tidyverse”
Slide created by
Sean Leonard

23. Data Get data into R Analyze/calcu-
late data
Generate beautiful
figures
Share your results
Data analysis in the “tidyverse”
Slide created by
Sean Leonard

24. Data Get data into R Analyze/calcu-
late data
Generate beautiful
figures
Share your results
Data analysis in the “tidyverse”
Slide created by
Sean Leonard

25. Using R (RStudio)

26. Using R (RStudio)
Console

27. Using R (RStudio)
Editor
Console

28. Using R (RStudio)
EnvironmentEditor
Console

29. Using R (RStudio)
EnvironmentEditor
Console
Misc.

30. 1st Programming Exercise
1. open up the 2016_fall_intro_r.Rproj
2. Navigate to the code folder and open up
r_intro.Rmd
3. Start playing with code
1. Do: Ask Questions, run code, change things
and see what happens
How to run code:
1. Move cursor to a linecoding block
2. Highlight line(s) of code
3. Type, ctrl+enter (windows) or cmd+enter (mac)
4. See code running in console
5. View output/figures

31. Now you can run code in R, so just need
ingredients for your recipe

32. Now you can run code in R, so just need
ingredients for your recipe
Vectors

33. Now you can run code in R, so just need
ingredients for your recipe
Vectors
Data frames

34. Now you can run code in R, so just need
ingredients for your recipe
Vectors
Data frames
Functions

35. Now you can run code in R, so just need
ingredients for your recipe
Vectors
Data frames
Functions
Stop me if you see anything
on this screen that doesn’t
make sense!

36. numeric character logical
R data structure flowchart
factor

37. numeric character logical
R data structure flowchart
5 “tupac” TRUEe.g.
factor
control (1)
treatment (2)

38. numeric character logical
vector
R data structure flowchart
5 “tupac” TRUEe.g.
factor
control (1)
treatment (2)

39. numeric character logical
vector
data frame
R data structure flowchart
5 “tupac” TRUEe.g.
tibble or
factor
control (1)
treatment (2)

40. numeric character logical
vector
data frame
R data structure flowchart
5 “tupac” TRUEe.g.
tibble or
factor
control (1)
treatment (2)

41. Everything in R is a function

42. Everything in R is a function
Function form:
fxn(arg1, arg2, …)

43. Everything in R is a function
Function form:
fxn(arg1, arg2, …)
> sum(5, 10, 15)
[1] 30

44. Everything in R is a function
Function form:
fxn(arg1, arg2, …)
5 + 10 equivalent to `+`(5,10)
> sum(5, 10, 15)
[1] 30

45. R data structures

46. R data structures

47. R data structures

48. Data Get data into R Analyze/calcu-
late data
Generate beautiful
figures
Share your results

49. dplyr provides functions for
manipulating and analyzing data frames

50. dplyr provides functions for
manipulating and analyzing data frames
pipes (magrittr): %>%

51. dplyr provides functions for
manipulating and analyzing data frames
pipes (magrittr): %>%

52. dplyr provides functions for
manipulating and analyzing data frames
pipes (magrittr): %>%
equivalent to:

53. dplyr provides functions for
manipulating and analyzing data frames
pipes (magrittr): %>%
equivalent to:

54. pipes (magrittr): %>%

55. filter(): Subset the rows in the df

56. filter(): Subset the rows in the df
df %>% filter(expression)

57. filter(): Subset the rows in the df
df %>% filter(expression)

58. filter(): Subset the rows in the df
df %>% filter(expression)

59. Expression
Comparison between left and
right side
== Equality
!= Inequality
< Less than
> Greater than
<= Less than or equal to
>= greater than or equal to

60. select(): Select columns in df

61. select(): Select columns in df
df %>% select(columns)

62. select(): Select columns in df
df %>% select(columns)

63. select(): Select columns in df
df %>% select(columns)

64. select(): Select columns in df
df %>% select(columns)

65. select syntax Description
select(col1:colx) All columns between col1 and colx
select(1:x) Columns 1 through x
select(col1, col2) All columns listed
select(-col1) All columns except col1
select(col1:col10, -col3)
All columns between col1 and
col10 except for col3

66. %>% allow stringing functions together

67. %>% allow stringing functions together

68. %>% allow stringing functions together

69. 2nd Programming Exercise

70. mutate(): add a new column to df

71. mutate(): add a new column to df
df %>% mutate(new_col_name = expression)

72. mutate(): add a new column to df
df %>% mutate(new_col_name = expression)

73. mutate(): add a new column to df
df %>% mutate(new_col_name = expression)

74. Operation Description
+ Addition
- Subtraction
* Multiplication
/ Division
^ Exponentiate
sqrt() Take the square root
log() Take the logarithm (defaults to ln)
exp() Exponentiates (defaults to e^x)

75. group_by(): Make implicit groupings
summarise(): compute summary of groups

76. group_by(): Make implicit groupings
summarise(): compute summary of groups

77. group_by(): Make implicit groupings
summarise(): compute summary of groups

78. group_by(): Make implicit groupings
summarise(): compute summary of groups

79. group_by(): Make implicit groupings
summarise(): compute summary of groups
How would the code change if you wanted to
find the average gdp for each country instead?

80. Summary Fxn Description
mean() Mean of values
sum() Sum values
median() Median
sd() Standard deviation
var() Variance
cor() Correlation

81. 3rd Programming Exercise

82. Visualizing data
www.reddit.com/r/dataisbeautiful

83. Visualizing data
www.reddit.com/r/dataisbeautiful
ggplot2

84. ggplot2 visualizations

85. ggplot2 visualizations

86. The grammar of graphics (ggplot)

87. The grammar of graphics (ggplot)
1. Data
•Raw data for plotting

88. The grammar of graphics (ggplot)
1. Data
•Raw data for plotting
2. Geometries
•The shape that will represent the data
•point, line, bar, etc.

89. The grammar of graphics (ggplot)
1. Data
•Raw data for plotting
2. Geometries
•The shape that will represent the data
•point, line, bar, etc.
3. Aesthetics
•axis, color, size, shape, etc.

90. The grammar of graphics (ggplot)
1. Data
•Raw data for plotting
2. Geometries
•The shape that will represent the data
•point, line, bar, etc.
3. Aesthetics
•axis, color, size, shape, etc.
4. Scales
•Mapping data to aesthetic (how to color geoms,
data range to plot, etc)

91. A simple example

92. A simple example

93. A simple example

94. A simple example
note that this uses “cowplot,” because I can’t stand ggplot2
default themes
ggplot2 default cowplot default

95. A simple example

96. A simple example
Data frame

97. A simple example
Data frame
Aesthetics

98. A simple example
Data frame
Aesthetics
Geometry

99. A simple example
Data frame
Aesthetics
Geometry
Link with +

100. A simple example
Data frame
Aesthetics
Geometry
Link with +
data column names

101. A second example

102. A second example

103. A second example

104. 4th Programming Exercise

105. Principles of “tidy” data
1. Every variable forms a column
2. Each observation forms a row

106. Principles of “tidy” data
1. Every variable forms a column
2. Each observation forms a row
Patient Age Height Weight
Jack 30 72 180
Jill 28 64 115
Mary 27 62 112
Messy / Wide

107. Principles of “tidy” data
1. Every variable forms a column
2. Each observation forms a row
Patient Age Height Weight
Jack 30 72 180
Jill 28 64 115
Mary 27 62 112
Patient Characteristic Value
Jack Age 30
Jack Height 72
Jack Weight 180
Jill Age 28
Jill Height 64
Jill Weight 115
Mary Age 27
Mary Height 62
Mary Weight 112
Messy / Wide
Tidy / Long

108. Principles of “tidy” data
1. Every variable forms a column
2. Each observation forms a row
Patient Age Height Weight
Jack 30 72 180
Jill 28 64 115
Mary 27 62 112
Patient Characteristic Value
Jack Age 30
Jack Height 72
Jack Weight 180
Jill Age 28
Jill Height 64
Jill Weight 115
Mary Age 27
Mary Height 62
Mary Weight 112
Messy / Wide
Tidy / Long

109. Principles of “tidy” data
1. Every variable forms a column
2. Each observation forms a row

110. Principles of “tidy” data
1. Every variable forms a column
2. Each observation forms a row

111. gather(key=income, value=freq, -religion)

112. 5th Programming Exercise
gather(key=income, value=freq, -religion)

113. Adding in more aesthetics

114. Adding in more aesthetics

115. Frequently used geoms + aesthetics
• geom_bar()
• geom_line()
• geom_point()
• geom_histogram()
• geom_ribbon()
• geom_text()
• geom_boxplot()
• color
• size
• fill
• alpha
• shape
• linetype
• group
http://docs.ggplot2.org/current/

120. 6th Programming Exercise

121. 6th Programming Exercise

122. R resources
• stack overflow (google)
• Hadley Wickham’s website - http://hadley.nz/
• http://www.r-bloggers.com/how-to-learn-r-2/
• A Beginner's Guide to R (Use R!) by Alain Zuur,
Elena N. Ieno, and Erik Misters
• The Art of R Programming: A Tour of Statistical
Software Design by Norman Matloff
• ggplot2: Elegant Graphics for Data Analysis (Use R!)
by Hadley Wickham. — Maybe wait for the second
edition (it’s slightly outdated)