This toolbox has been compiled to make the intro to R and statistics with R a little easier.
Besides that, it also contains some neat helper functions for tasks or problems one might run in frequently in our field.

High Level Functions

There are several high level functions aimed at quick output generation:

The point of these function is to combine routine steps into one function, so let’s showcase them.

t-Tests: tadaa_t.test

On of the first implemented functions, tadaa_t.test, automagically checks for homogenity of variance via car::leveneTest, and if the resulting p-value is below .1, homogenity of variance is assumed in the following call to stats::t.test. Afterwards the effect size \(d\) is is calculated with pooled/weighted variances to ensure accuracy, and the power of the test is calculated via the pwr package (also keeping in mind whether the test is paired or not). It then formats the output according to broom::tidy, sprinkles it with pixiedust and prints either to console, markdown or whatever printing method is passed via the print argument to pixiedust::sprinkle_print_method.

tadaa_t.test(ngo, stunzahl, geschl, print = "markdown")
Männlich Weiblich t p df conf_low conf_high d power
33.616 33.664 -0.108 0.91 248 -0.92 0.824 0.014 0.051

ANOVA: tadaa_aov

tadaa_aov(stunzahl ~ geschl, data = ngo, print = "markdown")
term df sumsq meansq F p.value part.eta.sq
geschl 1 0.144 0.144 0.012 0.91 0
Residuals 248 3037.456 12.248 NA NA 0.5

Statistics Wrappers

These are pretty self-explanatory. The goal is to provide simple functions for commonly used statistics that look and behave the same, and also only return a single numerical value to play nice with dplyr::summarize.

Summaries

General Helper Functions

Intervals and recoding

Plotting helpers

library(ggplot2)

ggplot(data = ngo, aes(x = jahrgang, y = stunzahl)) +
  stat_summary(fun.data = "mean_ci_t", geom = "errorbar")

Prebuilt plotting functions

  • tadaa_int: Simple interaction plot template.
library(ggplot2)

tadaa_int(data = ngo, response = stunzahl, group1 = jahrgang, group2 = geschl)

Data

The infamous ngo dataset is included for teaching purposes as well. It differs from ryouready’s provided version with regards to classes and labels. The code below was used to generate the provided version of the dataset:

(Note that \u00e4 is a unicode encoded Umlaut for compatibility reasons)

ngo <- ryouready::d.ngo

## sjPlot value labels
ngo$geschl   <- sjmisc::set_labels(ngo$geschl,   c("M\u00e4nnlich", "Weiblich"))
ngo$abschalt <- sjmisc::set_labels(ngo$abschalt, c("Ja", "Nein"))
ngo$jahrgang <- sjmisc::set_labels(ngo$jahrgang, c("11", "12", "13"))
ngo$hausauf  <- sjmisc::set_labels(ngo$hausauf,  c("gar nicht", "weniger als halbe Stunde",
                                           "halbe Stunde bis Stunde", "1 bis 2 Stunden",
                                           "2 bis 3 Stunden", "3 bis 4 Stunden",
                                           "mehr als 4 Stunden"))

## factors
ngo$geschl   <- factor(ngo$geschl,   labels = c("M\u00e4nnlich", "Weiblich"))
ngo$jahrgang <- factor(ngo$jahrgang, labels = c("11", "12", "13"), ordered = TRUE)
ngo$hausauf  <- car::recode(ngo$hausauf,  "0 = NA")
ngo$abschalt <- car::recode(ngo$abschalt, "0 = NA")
ngo$abschalt <- factor(ngo$abschalt, labels = c("Ja", "Nein"))

## Variable labels
ngo$geschl   <- sjmisc::set_label(ngo$geschl, "Geschlecht")
ngo$abschalt <- sjmisc::set_label(ngo$abschalt, "Abschalten")
ngo$jahrgang <- sjmisc::set_label(ngo$jahrgang, "Jahrgang")
ngo$hausauf  <- sjmisc::set_label(ngo$hausauf, "Hausaufgaben")

## Saving
ngo <- dplyr::tbl_df(ngo)