Another source showcasing the many possibilities of R Markdown is R Markdown: The Definitive Guide. A good reference to R Markdown is the R Markdown cookbook. As an introduction, there are many things left unsaid, so you will quite likely have to consult other sources to get properly acquainted with all the possibilities of R Markdown. This chapter provides a brief introduction to R Markdown, as well as the papaja package to produce output in the APA style. For instance, this book was completely written in R Markdown, with help from the additional bookfown package ( ). R Markdown is extremely flexible and useful.

Anyone with the R Markdown file would be able to reproduce everything in a scientific paper. Including all analysis in the same document allows for completely reproducible research. A big benefit of this is that can keep your analysis and write-up in one place, so you don’t have to copy-paste results from one place to another (which often results in errors and issues). Markdown documents can be “parsed” to produce documents in a variety of formats, including HTML, PDF, Open Document Type, Microsoft Word, you name it… R Markdown integrates Markdown with R, allowing you to use R to include figures, tables, and R output (as well as the corresponding code, if you wish) directly in the document. Markdown itself is a lightweight markup language, with a plain-text formatting syntax. One of the many things that makes R extremely useful as a data analysis platform is its ability to generate high-quality reproducible reports and documents via R Markdown. 10.1.5 Bayesian repeated-measures ANOVAĬhapter 11 Reproducible reports with RMarkdown.10 Bayesian hypothesis testing with Bayes Factors.9.2 Obtaining p-values with afex::mixed.9.1.4 Likelihood ratio test with the anova function.9.1.2 Visually assessing model assumptions.9.1 Formulating and estimating linear mixed-effects models with lme4.8.4 Repeated-measures ANOVA with the afex package.8.3 Repeated-measures ANOVA with the car package.8.2.2 Performing a repeated-measures ANOVA with separate models.8.2.1 Computing within-subjects composite scores.8.2 Repeated-measures ANOVA with separate GLMs.7.5 Testing general contrasts with emmeans.7.4 Planned comparisons and post-hoc tests with emmeans.7.2 Formulating, estimating, and testing a factorial ANOVA.6.1 Computing contrast-coded predictors.5.3.2 Investigating the moderated (indirect) effect with a bootstrap test.4.6 Multicollinearity and outlier detection.4.5 Plotting pairwise scatterplots for many variables.

4.3 Estimating and testing a multiple regression model.4.1 Estimating an testing a simple regression model.2.3 Hypothesis testing directly with the binomial distribution.1.8 Exploring data: Descriptive statistics.

Rmarkdown options