In this two day workshop, we will provide a general introduction to R and how to do statistical data analysis using R. On the first day, we will provide an introduction to R fundamentals (RStudio overview, R commands and operations, assignment, data structures, functions, scripts, packages, reading in data files, viewing and summarizing data). This will be followed by data wrangling (using dplyr, tidyr, and pipelines), and data visualization using ggplot2. On the second data, we begin by covering introductory statistical tests (t-tests, correlation, Chi-square), and then proceed to linear models (simple linear regression, multiple linear regression, general linear models, oneway anova, factorial anova). We will then cover generalized linear models (logistic regression, Poisson regression, negative binomial regression), and general and generalized linear mixed effects (multilevel) modelling.

Schedule

• Day 1, part 1: Introduction to R fundamentals (RStudio overview, R commands and operations, assignment, data structures, functions, scripts, packages, reading in data files, viewing and summarizing data).

• Day 1, part 2: Data wrangling the tidyverse way (using dplyr, tidyr, and pipelines); data visualization using ggplot2 (histograms, density plots, barplots, boxplots, scatterplots, facet plots, etc).

• Day 2, part 1: Introductory statistical tests (t-tests, correlation, Chi-square), linear models (simple linear regression, multiple linear regression, general linear models, oneway anova, factorial anova).

• Day 2, part 2: Generalized linear models (logistic regression, Poisson regression, negative binomial regression). Linear and generalized linear mixed effects modelling (and as part of this we’ll deal with repeated measures Anova and and how and why liner mixed effects models effectively supersede repeated measures Anova).

For Day 1, the only required R package is tidyverse (which is a package of packages). For Day 2, you need the R packages reghelper, car, lme4, and brms (which requires rstan, see next section).

GitHub resources

Further resources for this training course can be found on Github at mark-andrews/u-herts-r-workshop.