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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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.