visdat 0.5.1 (2018/07/02) “The Northern Lights Moonwalker” Unreleased

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 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

visdat 0.1.0 (2017/07/03) (“JOSS”) 2017-07-11

  • lightweight CRAN submission - will only contain functions vis_dat and vis_miss

visdat (2017/07/03) Unreleased

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.

visdat (2017/02/26) Unreleased

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

visdat (2017/01/09) Unreleased

Minor Improvements

  • Added testing for visualisations with vdiffr. Code coverage is now at 99%
  • Fixed up suggestions from goodpractice::gp()
  • Submitted to rOpenSci onboarding
  • written and submitted to JOSS

visdat (2017/01/08) Unreleased

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, 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.