Introduction
What is R?
Key Terms
Common Symbols
1
R Basics
1.1
Installing and Customizing R
1.2
Installing RStudio
1.3
RStudio Features
1.4
Setting up RStudio
1.5
R Files
1.5.1
.R - R Scripts
1.5.2
.rproj - R Projects
1.5.3
.RData - R Data Files
1.5.4
.RMD - R Markdown
1.6
R Data Structures
1.6.1
Vector
1.6.2
Data frame
1.6.3
Tibble
1.6.4
List
1.7
Installing Packages
1.7.1
CRAN
1.7.2
Github
1.8
Loading Packages
1.9
Importing Data
1.9.1
Importing a .CSV File
1.9.2
Importing an Excel file
1.9.3
Importing an SPSS data file
1.9.4
Importing Files from the Internet
1.10
RStudio Tips to Help Write Efficient Code
1.10.1
Commenting
1.10.2
Sectioning Scripts
1.10.3
Data Object Names
1.10.4
Shortcuts
2
Data Preparation and Cleaning in R
2.1
Introduction to the Tidyverse
2.1.1
Extracting a variable
2.1.2
Make new variables (columns)
2.2
Viewing Your Data
2.2.1
The RStudio Environment
2.2.2
Structure of Data - str()
2.2.3
Variable/Column Names - names()
2.2.4
First n Rows - head()
2.3
Renaming Variables
2.4
Renaming Multiple Variables
2.5
Cleaning Names with janitor
2.5.1
Summary Stats - describe()
2.5.2
Summary Stats - describeBy()
2.5.3
Summary Stats - summary()
2.5.4
Summary Stats - skim()
2.5.5
Crosstabs - table()
2.6
Keeping and Dropping Variables
2.7
Keeping and Dropping Rows
2.8
More Frequencies and Descriptives
2.9
Spotting Coding Mistakes
2.10
Modifying Data - mutate()
2.11
Reordering Categories - factor()
2.12
Clearing Whitespace in Text - str_trim()
2.13
Combining Categories - case_when()
2.14
Splitting variables with split()
2.15
Descriptives
2.15.1
Mean - mean()
2.15.2
Median - median()
2.15.3
Using the summarize() function - summarize()
2.15.4
Analyzing Data by Groups - group_by()
2.16
Spotting Outliers
2.16.1
Detecting Numerical Outliers
2.16.2
Detecting outliers with Z-scores
2.16.3
Dealing with Outliers
2.17
Assessing Normality
2.17.1
Histograms
2.17.2
Density (Curve) Plots
2.17.3
QQ Plots
2.17.4
Skewness and Kurtosis
2.17.5
Tests of Normality
2.17.6
Transforming Variables
2.18
Identifying missing data
2.18.1
Per Variable
2.18.2
Entire Data Frame
2.19
Dropping missing data
2.20
Replacing missing data with 0s
2.21
Mean imputation
2.22
Multiple imputation
3
More Data Manipulation in R
3.1
Wide and Long Data
3.2
Pivoting data from wide to long - pivot_longer()
3.2.1
Pivoting survey data
3.3
Pivoting data from long to wide - pivot_wider()
3.4
Joining multiple data objects
3.4.1
Left Join
3.4.2
Left Joining our Survey Data
4
Statistics in R
4.1
T-Tests
4.1.1
Independent Means T-Test
4.2
Chi Square
4.2.1
Chi Square Goodness of Fit
4.2.2
Chi Square Test of Independence
4.3
Analysis of Variance (ANOVA)
4.3.1
One-Way Between Subjects ANOVA
4.3.2
One-Way Within Subjects ANOVA
4.4
Factorial Between Subjects ANOVA
5
Data Visualization in Base R
5.1
Scatter Plot
5.2
Bar Graph
5.3
Histogram
5.4
Box Plot
6
Data Visualization with
ggplot
6.1
ggplot
6.2
Grammar of Graphics
6.3
Data
6.4
Aesthetics
6.5
Geometries
6.5.1
Bar Charts
6.5.2
Histograms
6.5.3
Boxplots
6.5.4
Scatterplots
6.5.5
Barbell Charts
6.5.6
Line Charts
6.5.7
Colors
6.5.8
Labels
6.5.9
Multiple Plots
6.5.10
Themes
7
Resources
7.1
Programming in R
7.2
Statistics
7.3
Data Visualization
7.4
Troubleshooting
7.5
Getting Help
R Software Handbook
7
Resources
7.1
Programming in R
Allison Hort’s Stats Illustrations
Data Carpentry
How to Clean Messy Data in R
7.2
Statistics
Data Science in Education using R
Learning Statistics on YouTube
7.3
Data Visualization
R Graph Gallery
.
7.4
Troubleshooting
Working with
rtools
7.5
Getting Help
Stack Overflow