1
About this tutorial
1.1
Download and install R and RStudio
1.2
Getting familiar with RStudio
1.2.1
Console vs. script
1.2.2
Comments
1.2.3
Packages
2
Introduction to R
2.1
Importing data
2.1.1
Importing CSV files
2.1.2
Setting your working directory
2.1.3
Assigning data to objects
2.1.4
Importing Excel files
2.1.5
Inspecting the Airbnb dataset
2.1.6
Importing data from Qualtrics
2.2
Manipulating data frames
2.2.1
Transforming variables
2.2.2
Including or excluding and renaming variables (columns)
2.2.3
Including or excluding observations (rows)
2.3
The pipe operator
2.3.1
One way to write code
2.3.2
A better way to write code
2.4
Grouping & summarizing
2.4.1
Frequency tables
2.4.2
Descriptive statistics
2.5
Exporting (summaries of) data
2.6
Graphs
2.6.1
Scatterplot
2.6.2
Jitter
2.6.3
Histogram
2.6.4
Log-transformation
2.6.5
Plot the median
2.6.6
Plot the mean
2.6.7
Saving images
3
Basic data analysis: analyzing secondary data
3.1
Data
3.1.1
Import
3.1.2
Manipulate
3.1.3
Merging datasets
3.1.4
Recap: importing & manipulating
3.2
Independent samples
t
-test
3.3
One-way ANOVA
3.3.1
Assumption: normality of residuals
3.3.2
Assumption: homogeneity of variances
3.3.3
The actual ANOVA
3.3.4
Tukey’s honest significant difference test
3.4
Linear regression
3.4.1
Simple linear regression
3.4.2
Correlations
3.4.3
Multiple linear regression, without interaction
3.4.4
Multiple linear regression, with interaction
3.4.5
Assumptions
3.5
Chi-squared test
3.6
Logistic regression (optional)
3.6.1
Measuring the fit of a logistic regression: percentage correctly classified
4
Basic data analysis: experiments
4.1
Data
4.1.1
Import
4.1.2
Manipulate
4.1.3
Recap: importing & manipulating
4.2
t
-tests
4.2.1
Independent samples
t
-test
4.2.2
Dependent samples
t
-test
4.2.3
One sample
t
-test
4.3
Two-way ANOVA
4.3.1
Following up with contrasts
4.4
Moderation analysis: Interaction between continuous and categorical independent variables
4.4.1
Spotlight analysis
4.5
ANCOVA
4.6
Repeated measures ANOVA
5
Principal component analysis for perceptual maps (office dataset)
5.1
Data
5.1.1
Import
5.1.2
Manipulate
5.1.3
Recap: importing & manipulating
5.2
How many factors should we retain?
5.3
Principal components analysis:
5.3.1
Factor loadings
5.3.2
Loading plot and biplot
6
Principal component analysis for perceptual maps (toothpaste dataset)
6.1
Data
6.1.1
Import
6.1.2
Manipulate
6.1.3
Recap: importing & manipulating
6.2
How many factors should we retain?
6.3
Principal component analysis
6.3.1
Factor loadings
6.3.2
Loading plot and biplot
7
Cluster analysis for segmentation
7.1
Data
7.1.1
Import
7.1.2
Manipulate
7.1.3
Recap: importing & manipulating
7.2
Cluster analysis
7.2.1
Standardizing or not?
7.3
Hierarchical clustering
7.4
Non-hierarchical clustering
7.5
Canonical LDA
8
Conjoint analysis
8.1
Data
8.1.1
Import
8.1.2
Manipulate
8.1.3
Recap: importing & manipulating
8.2
Design of experiments
8.3
One respondent
8.3.1
Estimate part-worths and importance weights
8.3.2
Profiles: predicted utilities
8.4
Many respondents
8.4.1
Estimate part-worths and importance weights
8.4.2
Profiles: predicted utilities
8.5
Market simulation
R for marketing students
2
Introduction to R
In this introductory chapter, you will learn:
how to import data
how to manipulate a dataset with the pipe operator
how to summarize a dataset
how to make scatterplots and histograms