vis_cor()to use perceptually uniform colours from
scico::scico(3, palette = "vik").
vis_cor()to have fixed legend values from -1 to +1 (#110) using options
limits. Special thanks to this SO thread for the answer
vis_compare()for comparing two dataframes of the same dimensions
vis_expect()for visualising where certain values of expectations occur in the data
vis_expectto show the percentage of expectations that are TRUE. #73
vis_corto visualise correlations in a dataframe
vis_guess()for displaying the likely type for each cell in a dataframe
vis_expectto make it easy to look at certain appearances of numbers in your data.
vis_corto use argument
vis_miss_ly- thanks to Stuart Lee
Fix bug reported in #75 where
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
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
vis_cor didn’t gather variables for plotting appropriately - now fixed
add_vis_dat_pal()(internal) to add a palette for
vis_guessnow gets a palette argument like
plotlyvis_*_ly interactive graphs:
vis_compare_ly()These simply wrap
plotly::ggplotly(vis_*(data)). In the future they will be written in
plotlyso that they can be generated much faster
vis_family are now flipped by default
vis_missnow shows the % missingness in a column, can be disabled by setting
show_perc_colargument to FALSE
flipargument, as this should be the default
vdiffr. Code coverage is now at 99%
paper.mdwritten and submitted to JOSS
flip = TRUE, to
vis_miss. This flips the x axis and the ordering of the rows. This more closely resembles a dataframe.
vis_miss_lyis a new function that uses plotly to plot missing data, like
vis_miss, but interactive, without the need to call
plotly::ggplotlyon 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_missnow gains a
show_percargument, which displays the % of missing and complete data. This is switched on by default and addresses issue #19.
vis_compareis 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_datgains 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
vis_dat_ly, as it currently does not work.
vis_compareare very beta
vis_compareto be different to the ggplot2 standards.
vis_misslegend labels are created using the internal function
miss_guide_labelwill 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_labellegend labels function.