ctb
.vis_compare()
for comparing two dataframes of the same dimensionsvis_guess()
for displaying the likely type for each cell in a dataframeInteractive, plotly
versions for each of the vis_*
family
vis_dat
and vis_miss
add_vis_dat_pal()
(internal) to add a palette for vis_dat
and vis_guess
vis_guess
now gets a palette argument like vis_dat
plotly
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 fastervdiffr
. 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.