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Anscombe’s quartet are a set of four two-variable datasets that have
several common summary statistics (essentially means, variances and
correlation) but which have very different joint distributions. This
becomes apparent when the data are plotted, which illustrates the
importance of using graphical displays in Statistics. The
anscombiser package provides a quick and easy way to create
several datasets that have common values for Anscombe’s summary
statistics but display very different behaviour when plotted. It does
this by transforming (shifting, scaling and rotating) the dataset to
achieve target summary statistics.
The mimic() function transforms an input dataset
(dino below left) so that it has the same values of
Anscombe’s summary statistics as another dataset (trump
below right).
library(anscombiser)
library(datasauRus)
dino <- datasaurus_dozen_wide[, c("dino_x", "dino_y")]
new_dino <- mimic(dino, trump)
plot(new_dino, legend_args = list(x = "topright"))
plot(new_dino, input = TRUE, legend_args = list(x = "bottomright"), pch = 20)
In this example these images had similar summary statistics from the
outset and therefore the appearance of the dino dataset has
changed little. Otherwise, the first dataset will be deformed but its
general shape will still be recognisable.
The rotation applied to the input dataset is not unique. The function
mimic (and a function anscombise that is
specific to Anscombe’s quartet) has an argument idempotent
that controls how the rotation is performed. In the special case where
the input dataset already has the desired summary statistics, using
idempotent = TRUE ensures that the output dataset is the
same as the input dataset.
To get the current released version from CRAN:
install.packages("anscombiser")See
vignette("intro-to-anscombiser", package = "anscombiser")
for an overview of the package.
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.