Preliminary Exploratory Visualisation of Data
vis_value()
for visualising all values in a dataset. It rescales values to be between 0 and 1. See #100vis_binary()
for visualising datasets with binary values - similar to vis_value()
, but just for binary data (0, 1, NA). See #125. Thank you to Trish Gilholm for her suggested use case for this.vis_dat()
and vis_cor()
, and vis_miss()
see (#78). The next release will implement facetting for vis_value()
, vis_binary()
, vis_compare()
, vis_expect()
, and vis_guess()
.data_vis_dat()
, data_vis_cor()
, and data_vis_miss()
see (#78).vis_dat()
vis_miss()
and vis_guess()
now render missing values in list-columns (@cregouby #138)abbreviate_vars()
function to assist with abbreviating data names (#140)vis_miss()
is now rounding to integers - for more accurate representation of missingness summaries please use the naniar
R package.gather_
(#141)vis_value()
displayed constant values as NA values (#128) - these constant values are now shown as 1.vis_expect
would reorder columns (#133), fixed in #143 by @muschellij2.cli
internally for error messages.vis_cor()
to use perceptually uniform colours from scico
package, using scico::scico(3, palette = "vik")
.vis_cor()
to have fixed legend values from -1 to +1 (#110) using options breaks
and limits
. Special thanks to this SO thread for the answer
glue
and glue_collapse()
instead of paste
and paste0
usethis::use_spell_check()
guess_parser
, to not
guess integer types by default. To opt-into the current behavior you
need to pass guess_integer = TRUE.
vis_compare()
for comparing two dataframes of the same dimensionsvis_expect()
for visualising where certain values of expectations occur in the data
vis_expect
show_perc
arg to vis_expect
to show the percentage of expectations that are TRUE. #73vis_cor
to visualise correlations in a dataframevis_guess()
for displaying the likely type for each cell in a dataframevis_expect
to make it easy to look at certain appearances of numbers in your data.vis_cor
to use argument na_action
not use_op
.vis_miss_ly
- thanks to Stuart Leepaper.md
for JOSSctb
.Fix bug reported in #75
where vis_dat(diamonds)
errored seq_len(nrow(x))
inside internal
function vis_gather_
, used to calculate the row numbers. Using
mutate(rows = dplyr::row_number())
solved the issue.
Fix bug reported in #72
where vis_miss
errored when one column was given to it. This was an issue
with using limits
inside scale_x_discrete
- which is used to order the
columns of the data. It is not necessary to order one column of data, so I
created an if-else to avoid this step and return the plot early.
Fix visdat x axis alignment when show_perc_col = FALSE - #82
fix visdat x axis alignment - issue 57
fix bug where the column percentage missing would print to be NA when it was exactly equal to 0.1% missing. - issue 62
vis_cor
didn't gather variables for plotting appropriately - now fixed
An updated release with an updated abstract summary for visdat suitable for publication in JOSS
First official github release for of visdat