datarium: Data bank for statistical analyses and visualization
Contains 23 curated data sets for teaching classical statistical
inference in R. Each data set is paired with the analysis it is designed
to teach — one data set per test. The same map is available in R via
?datarium. Each analysis below links to its worked tutorial on
Datanovia.
Comparing means
| Analysis | Data set |
|---|---|
| One-sample t-test | mice |
| Paired-samples t-test | mice2 |
| Two-samples (independent) t-test | genderweight |
Analysis of variance (ANOVA)
| Analysis | Data set |
|---|---|
| Two-way ANOVA | jobsatisfaction |
| Three-way ANOVA | headache, heartattack |
| One-way repeated measures ANOVA | selfesteem |
| Two-way repeated measures ANOVA | selfesteem2 |
| Three-way repeated measures ANOVA | weightloss |
| Two-way mixed ANOVA | anxiety, depression |
| Three-way mixed ANOVA | performance |
| Two-way ANCOVA | stress |
Categorical data and proportions
| Analysis | Data set |
|---|---|
| Chi-square test of independence | properties, housetasks.raw |
| Categorical descriptive statistics | titanic.raw |
| McNemar’s test | antismoking |
| Cochran’s Q test | taskachievment |
| Cochran-Armitage trend test | renalstone |
Regression
| Analysis | Data set |
|---|---|
| Linear regression | marketing |
| Poisson regression (count data) | infections |
| Logistic regression (real, messy clinical data) | heartdisease |
Time series
| Analysis | Data set |
|---|---|
| Time series | AirPassengersDf |
Helper functions
The two data sets (Titanic and housetasks) are frequency/contingency
table. We’ll create our demo data sets by recovering the original data
from Titanic and housetasks tables.
To do so, first copy and paste the following helper function:
counts_to_cases <- function(x, countcol = "Freq") {
if(!inherits(x, "table")) x <- as.table(as.matrix(x))
x <- as.data.frame(x)
# Get the row indices to pull from x
idx <- rep.int(seq_len(nrow(x)), x[[countcol]])
# Drop count column
x[[countcol]] <- NULL
# Get the rows from x
x <- x[idx, ]
rownames(x) <- 1:nrow(x)
x
}Then, recover the original data as follow:
# Load the data
data("Titanic")
data("housetasks", package = "factoextra")
# Recover the original raw data
titanic.raw <- counts_to_cases(Titanic)
housetasks.raw <- counts_to_cases(housetasks)