Changes in version 0.6.0.9000 Changes in version 0.6.0 (2023-02-02) New Feature - vis_value() for visualising all values in a dataset. It rescales values to be between 0 and 1. See #100 - vis_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. - Implemented facetting in 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(). - Implemented data methods for plots with 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) - Added abbreviate_vars() function to assist with abbreviating data names (#140) - Percentage missing in columns for vis_miss() is now rounding to integers - for more accurate representation of missingness summaries please use the naniar R package. - A new vignette on customising colour palettes in visdat, "Customising colour palettes in visdat". Bug Fix - no longer use old version of gather_ (#141) - resolve bug where vis_value() displayed constant values as NA values (#128) - these constant values are now shown as 1. - removed use of the now deprecated "aes_string" from ggplot2 - output of plot in vis_expect would reorder columns (#133), fixed in #143 by @muschellij2. - vis_miss() displayed missing percentages between 0.1% and 0.5% as 0% due to rounding. Now it dislpays "<1%" by @zeehio at https://github.com/ropensci/visdat/pull/162. Misc - No longer uses gdtools for testing (#145) - Use cli internally for error messages. - Speed up some internal functions in visdat Changes in version 0.5.3 (2019-02-15) Minor Change - Update vis_cor() to use perceptually uniform colours from scico package, using scico::scico(3, palette = "vik"). - Update 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 - Uses glue and glue_collapse() instead of paste and paste0 - adds WORDLIST for spelling thanks to usethis::use_spell_check() Changes in version 0.5.2 (2018-12-10) Minor Change - Internal error message has been improved by Nic in #102 Bug Fix - Jim Hester fixed recent changes in readr 1.2.0 in PR #103, which changes the default behavior of the guess_parser, to not guess integer types by default. To opt-into the current behavior you need to pass guess_integer = TRUE. Changes in version 0.5.1 (2018-07-04) New Feature - 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 Changes in version 0.1.0 (2017-07-11) - lightweight CRAN submission - will only contain functions vis_dat and vis_miss Changes in version 0.0.7.9100 New Features - 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 - Added protoype/placeholder functions for 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 faster Minor improvements - corrected testing for vis_* family - added .svg graphics for correct vdiffr testing - improved hover print method for plotly. Changes in version 0.0.6.9000 New Features - axes in vis_ family are now flipped by default - vis_miss now shows the % missingness in a column, can be disabled by setting show_perc_col argument to FALSE - removed flip argument, as this should be the default Minor Improvements - added internal functions to improve extensibility and debugging - vis_create_, vis_gather_ and vis_extract_value_. - suppress unneeded warnings arising from compiling factors Changes in version 0.0.5.9000 Minor Improvements - Added testing for visualisations with vdiffr. Code coverage is now at 99% - Fixed up suggestions from goodpractice::gp() - Submitted to rOpenSci onboarding - paper.md written and submitted to JOSS Changes in version 0.0.4.9999 New Feature - Added feature flip = 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. New Feature (under development) - 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. Minor Improvements - All lines are < 80 characters long - removed all instances of 1:rnow(x) and replaced with seq_along(nrow(x)). - Updated documentation, improved legend and colours for vis_miss_ly. - removed export for vis_dat_ly, as it currently does not work. - Removed a lot of unnecessary @importFrom tags, included magrittr in this, and added magrittr to Imports - Changes ALL CAPS Headers in news to Title Case - Made it clear that vis_guess() and vis_compare are very beta - updated documentation in README and vis_dat(), vis_miss(), vis_compare(), and vis_guess() - updated pkgdown docs - updated DESCRIPTION URL and bug report - Changed the default colours of 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. - tests have been added for the miss_guide_label legend labels function. - Changed legend label for vis_miss, vis_dat, and vis_guess. - updated README - Added vignette folder (but not vignettes added yet) - Added appveyor-CI and travis-CI, addressing issues #22 and #23 Bug Fixes - Update 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. Changes in version 0.0.3.9000 ========================= New Features - Interactivity with 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. Changes in version 0.0.2.9000 ========================= New Features - Introducing vis_guess(), a function that uses the unexported function collectorGuess from readr. Changes in version 0.0.1.9000 ========================= New Features - vis_miss() and vis_dat actually run