The intention behind this is to create an updating well of R resources for scientists who focus on education research. If you have any more suggestions please write a comment below or contact me on social media and I’ll add them.
Data manipulation and visualization
Tidyverse (Sam Parsons)- A collection of packages which allow for comprehensive data exploration and control over creating graphs, the most well known being ggplot2. I’ve had this recommended to me so many times, I really need to sit down and learn it. There are a great collection of options here.
Writing up results
Papaja– I can’t recommend this highly enough. It builds off R Markdown and produces APA formatted PDFs. It is very intuitive to use and the supporting documents are clear. If you’re familiar with LaTeX there will be no learning curve. But even if you aren’t (which I wasn’t) you will quickly get the hang of it. Plus one of the guys who wrote the package (Frederik Aust) is very quick to help and give advice which I’m personally very grateful for.
Modelling data (structural equation modelling, etc.)
lavaan (Dana Wanzer & Oscar Olvera)- For those who use SEM with it’s own syntax to specify formulas for your model.
lme4 (Dana Wanzer & Joshua Rosenberg)- Useful for running linear mixed effects and mutli-level models. A guide (recommended by @fedor_le) can be found here.
mirt (Oscar Olvera)- If you want to analyse response data (with 2 or more response options) using latent trait models, this is the package for you.
psych (Oscar Olvera)- A package designed for those in psychometric theory, it provides a variety of functions for giving descriptive statistics as well as more complex multivariate analysis (factor and component analysis etc.), correlations, and scale construction.
lmerTest (Ben Brummernhenrich)- The lmer package fits a linear mixed-effects model to data and lmerTest provides p-values in type I, II or III anova and summary tables for lmer models.
Statistical Rethinking package (Tim van der Zee)- If you want to explore Bayesian statistics then you can’t do much better than the Statistical Rethinking book and associated packages.
BayesFactor (Tim van der Zee)- Bayes Factors are a type of Bayesian statistics that is gaining popularity among some academics. This is the most common package that performs this.
brms (Oli Clark)- A wider range of Bayesian statistics including linear and non-linear multivariate modelling.
Caret (Emily Bovee)- A collection of packages for streamlining model training for complex regression.
Emily Bovee recommended the RStudio list of useful packages which contains many valuable tools (I pretty much live in R Markdown now).
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