New Features vis_compare() for comparing two dataframes of the same dimensions vis_expect() for visualising where certain values of expectations occur in the data Added NA colours to vis_expect Added show_perc arg to vis_expect to show the percentage of expectations that are TRUE. #73 vis_cor to visualise correlations in a dataframe vis_guess() for displaying the likely type for each cell in a dataframe Added draft vis_expect to make it easy to look at certain appearances of numbers in your data. visdat is now under the rOpenSci github repository Minor Changes added CITATION for visdat to cite the JOSS article updated options for vis_cor to use argument na_action not use_op. cleaned up the organisation of the files and internal functions Added appropriate legend and x axis for vis_miss_ly - thanks to Stuart Lee Updated the paper.md for JOSS Updated some old links in doco Added Sean Hughes and Mara Averick to the DESCRIPTION with ctb. Minor changes to the paper for JOSS Bug Fixes 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