Skip to contents

A dataset demonstrating Simpson's Paradox with a strongly positively correlated dataset (simpson_1) and a dataset with the same positive correlation as simpson_1, but where individual groups have a strong negative correlation (simpson_2).

Usage

simpsons_paradox

Format

A data frame with 444 rows and 3 variables:

  • dataset: indicates which of the two datasets the data are from, simpson_1 or simpson_2

  • x: x-values

  • y: y-values

References

Matejka, J., & Fitzmaurice, G. (2017). Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing. CHI 2017 Conference proceedings: ACM SIGCHI Conference on Human Factors in Computing Systems. Retrieved from https://www.research.autodesk.com/publications/same-stats-different-graphs/. #nolint

Examples

if (require(ggplot2)) {
  ggplot(simpsons_paradox, aes(x = x, y = y, colour = dataset)) +
    geom_point() +
    theme(legend.position = "none") +
    facet_wrap(~dataset, ncol = 3)
}


# Base R Plots
state = par("mfrow")

par(mfrow = c(1, 2))

sets = unique(datasaurus_dozen$dataset)

for (i in 1:2) {
  df = simpsons_paradox[simpsons_paradox$dataset == paste0("simpson_", i), ]
  plot(df$x, df$y, pch = 16, xlab = "", ylab = "")
  title(paste0("Simpson\'s Paradox ", i))
}


par(state)
#> Warning: argument 1 does not name a graphical parameter
#> NULL