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https://github.com/ccs-amsterdam/r-course-material

A collection of R tutorials
https://github.com/ccs-amsterdam/r-course-material

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A collection of R tutorials

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README 

        



readme



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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


This is a set of tutorials designed to teach basic statistical modeling and analysis. The first tutorial includes examples for standard regression analysis as well as analysis of variance. Later tutorials exemplify the use of more advanced statistical modelling approaches including the generalized linear model and multilevel models.



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







Test theory and factor analysis


The following tutorials can be used to teach basics of test theory and particularly confirmatory and exploratory factor analysis approaches.



Tutorial

Video tutorial

Core packages / functions



Test Theory and Confirmatory Factor Analysis

CFA in R

psych: describe, mardia; lavaan: cfa, fitMeasures, modindices; semTools: reliability



Exploratory Factor Analysis

EFA in R

psych: describe, mardia, fa.parallel, nfactors, fa, fa.diagram, omega







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







Data collection



Tutorial

Video tutorial

Core packages / functions



Gathering Data



tidyverse, jsonlite



Webscraping with rvest

Web scraping in R

rvest







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.






Miscellaneous










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