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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 answerglue and glue_collapse() instead of
paste and paste0usethis::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_expectshow_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
vis_dat and vis_missadd_vis_dat_pal() (internal) to add a palette for
vis_dat and vis_guessvis_guess now gets a palette argument like
vis_datplotly
vis_*_ly interactive graphs:
vis_guess_ly()vis_dat_ly()vis_compare_ly() These simply wrap
plotly::ggplotly(vis_*(data)). In the future they will be
written in plotly so that they can be generated much
fastervis_* familyvis_ family are now flipped by defaultvis_miss now shows the % missingness in a column, can
be disabled by setting show_perc_col argument to FALSEflip argument, as this should be the
defaultvis_create_, vis_gather_ and
vis_extract_value_.vdiffr. Code
coverage is now at 99%goodpractice::gp()paper.md written and submitted to JOSSflip = TRUE, to vis_dat and
vis_miss. This flips the x axis and the ordering of the
rows. This more closely resembles a dataframe.vis_miss_ly is a new function that uses plotly to plot
missing data, like vis_miss, but interactive, without the
need to call plotly::ggplotly on it. It’s fast, but at the
moment it needs a bit of love on the legend front to maintain the style
and features (clustering, etc) of current vis_miss.vis_miss now gains a show_perc argument,
which displays the % of missing and complete data. This is switched on
by default and addresses issue #19.vis_compare is a new function that allows you to
compare two dataframes of the same dimension. It gives a fairly ugly
warning if they are not of the same dimension.vis_dat gains a “palette” argument in line with issue 26, drawn
from http://colorbrewer2.org/, there are currently three arguments,
“default”, “qual”, and “cb_safe”. “default” provides the ggplot
defaults, “qual” uses some colour blind unfriendly
colours, and “cb_safe” provides some colours friendly for colour
blindness.1:rnow(x) and replaced with
seq_along(nrow(x)).vis_miss_ly.vis_dat_ly, as it currently does not
work.vis_guess() and
vis_compare are very betavis_dat(),
vis_miss(), vis_compare(), and
vis_guess()vis_compare to be
different to the ggplot2 standards.vis_miss legend labels are created using the internal
function miss_guide_label. miss_guide_label
will check if data is 100% missing or 100% present and display this in
the figure. Additionally, if there is less than 0.1% missing data,
“<0.1% missingness” will also be displayed. This sort of gets around
issue #18 for the moment.miss_guide_label legend
labels function.vis_miss,
vis_dat, and vis_guess.vis_dat() to use
purrr::dmap(fingerprint) instead of
mutate_each_(). This solves issue #3 where
vis_dat couldn’t take variables with spaces in their
name.plotly::ggplotly! Funcions
vis_guess(), vis_dat(), and
vis_miss were updated so that you can make them all
interactive using the latest dev version of plotly from
Carson Sievert.vis_guess(), a function that uses the
unexported function collectorGuess from
readr.vis_miss() and vis_dat actually runThese 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.