Welcome
1
Getting started with R
1.1
Installing R
1.1.1
Windows instructions
1.1.2
Mac OS X instructions
1.2
Installing RStudio
1.2.1
Mac OS X notes
1.3
Running RStudio
1.4
Installing your first packages
2
Tips for effective R programming
2.1
Working directories
2.2
Using scripts
2.2.1
Script layout
2.3
Writing readable code
2.3.1
Basic formatting tips
2.3.2
Keeping a consistent style
2.3.3
Writing comments
2.4
Don’t panic: dealing with SPSS withdrawal
2.4.1
RStudio has a data viewer
2.4.2
R can read SPSS files (and csvs, and almost every kind of file!)
3
The Basics of R
3.1
Basic data types
3.1.1
Numbers
3.1.2
Characters (text)
3.1.3
Logical (True/False)
3.2
Converting between types
3.3
Variables: Storing Results
3.3.1
Missing values
4
Dataframes and More
4.1
Factors (categorical data)
4.2
Dataframes!
4.2.1
Accessing parts of dataframes
5
Functions
5.1
Arguments
6
Example data cleaning and manipulation; Example descriptive statistics
6.1
Loading libraries
6.2
Loading data
6.3
Recoding
6.4
Descriptive Statistics
6.4.1
Quick data summary
6.4.2
Frequency tables
6.4.3
Histograms: distributions of continuous variables
6.4.4
Scatterplots: relationship between two continuous variables
6.4.5
END OF WORKSHOP (continue reading for tips on analysis and more examples)
6.5
Analysis
6.5.1
T-test
6.5.2
Formulas: a simple mini-language for expressing models
6.5.3
Regression
6.6
Pointless flashy nonsense
7
Easier analysis with the tidyverse
7.1
Introduction to the tidyverse
7.2
dplyr
: Turning complex analyses into simple steps
7.2.1
Bending the rules: Non-standard evaluation
7.2.2
Pipes:
%>%
7.2.3
Common tasks with
dplyr
8
Better plots with
ggplot2
8.1
Using
ggplot2
properly
8.2
Mapping aesthetics
8.3
geoms: representing the data with different components
8.4
Combining geoms
8.5
Scales and themes: changing the look of your plot
8.5.1
Scales
8.5.2
Themes
8.5.3
Finding extra scales and themes
8.6
Other packages use
ggplot2
too
9
Real world data example
9.1
Data overview
9.1.1
SURPS overview
9.1.2
BSI overview
9.2
Basic setup
9.2.1
Load libraries
9.2.2
Load the data
9.2.3
Solve major issues
9.3
Recoding and scoring
9.3.1
Scoring SURPS the easy way
9.3.2
Scoring SURPS the harder way
9.3.3
Scoring the BSI scales
9.3.4
Recoding
9.4
Saving data
9.5
Descriptive statistics
9.5.1
Correlations between variables
9.5.2
Contingency tables for categorical data
9.5.3
Understanding complex data
9.6
Analysis
9.6.1
Simple but wrong: Logistic regression
9.6.2
More complex modelling: Mixed models with
lme4
Learning more/Getting help
More courses/books
Checking out the R community
Getting help
Why R is Good
R is a Language
R is a programming language
R has an active community
References/Resources used
Matilda Intro to R Workshop
References/Resources used