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R course material

This page contains a collection of R tutorials, developed at the Vrije Universiteit Amsterdam for Communication Science courses that use R.

The goal is to organize relevant material into modular components, for more efficient design and maintenance of material, that can be used across courses, and that are accessible to students during and after their studies.

Below we list the relevant handouts/tutorials. Each links to the md file, see the Rmd file with the same name for the source code of the tutorials.

R Basics

Data mangling in the tidyverse

This is a set of tutorials designed to teach using the tidyverse functions for data cleaning, reshaping, visualizing etc. The chapter numbers are relevant chapters from the excellent (and freely available) book "R for Data Scientists" (R4DS)

Handout Video Tutorial R4DS ch. Core packages / functions
Fun with R Fun with R 3 tidyverse,ggplot2,igraph
R Basics Intro to R 4 (base R functions)
Transforming Data Importing and Cleaning 5 dplyr: filter, select, arrange, mutate
Summarizing Data Grouping and Summarizing 5 dplyr: group_by, summarize
Visualizing Data ggplot 1 7 ggplot2
Reshaping data Reshaping 12 tidyr: spread, gather
Combining (merging) Data Joining 13 dplyr: inner_join, left_join, etc.
Basic string (text) handling 14 readr: str_detect, str_extract etc., iconv

Statistical Analysis

Tutorial Video tutorial Core packages / functions
Basic statistics Basic stats stats: lm, aov, t.test
Advanced statistics overview see GLM and Multilevel stats: glm, lme4: lmer, glmer
Generalized linear models GLM (on family argument) stats: glm, family, sjPlot: tab_model, plot_model
Multilevel Models Multilevel lme4: lmer, glmer, sjPlot: tab_model, plot_model

Text analysis

For a general introduction to text analysis (in R), see these videos on preprocessing and different analysis approaches

Tutorial Video tutorial Core packages / functions
Text analysis corpus stats quanteda
Lexical sentiment analysis dictionaries quanteda, corpustools
LDA Topic Modeling Video series, Tutorial demo topicmodels,quanteda
Structural Topic Modeling Variants of Topic Models; Structural Topic Models stm, quanteda
NLP Preprocessing with Spacy(r) spacyr, quanteda (see also spacy itself)
Supervised machine learning for text classification Supervised Machine Learning caret
Creating a topic browser with LDA corpustools

Note on installing packages

In general, most R packages can be installed without any issues. However, there are some exceptions that you need to know about. For quanteda (that we use in the text analysis tutorials), your computer needs certain software that is not always installed, as mentioned on the quanteda website. You can either install this software, but we rather recommend using R version 4.0.0 (or higher) where this is no longer required. To see your current R version, enter version in your R console. To update, visit the R website (Windows, Mac).

When running install.packages() You sometimes get the message that There is a binary version available but the source version is later (we're mainly seen this on Mac). You then get the question whether you want to install from sources the package which needs compilation (Yes/no) . To answer this question, you have to type "yes" or "no" in your R console. Most often, you'll want to say no. Simply put, R tells you that it has a new version of a package, but if you want to use it your computer will need to build it. The problem is that this requires some development software that you might not have installed. If you say no, you'll install an older version that has already been build for you. In rare cases, installing from source is the only way, in which case you'll have to install the software that R refers to.

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