visdat: vis_guess – R documentation

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vis_guess

Visualise type guess in a data.frame

Description

vis_guess visualises the class of every single individual cell in a dataframe and displays it as ggplot object, similar to vis_dat. Cells are coloured according to what class they are and whether the values are missing. vis_guess estimates the class of individual elements using readr::guess_parser. It may be currently slow on larger datasets.

Usage

vis_guess(x, palette = "default")

Arguments

`x`	a data.frame
`palette`	character "default", "qual" or "cb_safe". "default" (the default) provides the stock ggplot scale for separating the colours. "qual" uses an experimental qualitative colour scheme for providing distinct colours for each Type. "cb_safe" is a set of colours that are appropriate for those with colourblindness. "qual" and "cb_safe" are drawn from http://colorbrewer2.org/.

Value

ggplot2 object displaying the guess of the type of values in the data frame and the position of any missing values.

Examples

messy_vector <- c(TRUE,
                 "TRUE",
                 "T",
                 "01/01/01",
                 "01/01/2001",
                 NA,
                 NaN,
                 "NA",
                 "Na",
                 "na",
                 "10",
                 10,
                 "10.1",
                 10.1,
                 "abc",
                 "$%TG")
set.seed(1114)
messy_df <- data.frame(var1 = messy_vector,
                       var2 = sample(messy_vector),
                       var3 = sample(messy_vector))
vis_guess(messy_df)

visdat

Preliminary Visualisation of Data

v0.5.3

MIT + file LICENSE

Authors

Nicholas Tierney [aut, cre] (<https://orcid.org/0000-0003-1460-8722>), Sean Hughes [rev] (<https://orcid.org/0000-0002-9409-9405>, Sean Hughes reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/87), Mara Averick [rev] (Mara Averick reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/87), Stuart Lee [ctb], Earo Wang [ctb], Nic Crane [ctb]

Initial release