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@njtierney njtierney released this 11 Sep 01:08
0.4.0
0d88a27

New Feature

  • Add custom label support for missings and not missings with functions add_label_missings and add_label_shadow() and add_any_miss(). So you can now do `add_label_missings(data, missing = "custom_missing_label", complete = "custom_complete_label")

  • impute_median() and scoped variants

  • any_shade() returns a logical TRUE or FALSE depending on if there are any shade values

  • nabular() an alias for bind_shadow() to tie the nabular term into the work.

  • is_nabular() checks if input is nabular.

  • geom_miss_point() now gains the arguments from shadow_shift()/impute_below() for altering the amount of jitter and proportion below (prop_below).

  • Added two new vignettes, "Exploring Imputed Values", and "Special Missing Values"

  • miss_var_summary and miss_case_summary now no longer provide the
    cumulative sum of missingness in the summaries - this summary can be added back
    to the data with the option add_cumsum = TRUE. #186

  • Added gg_miss_upset to replace workflow of: 
    data %>% 
  as_shadow_upset() %>%
  UpSetR::upset()

Major Change

  • recode_shadow now works! This function allows you to recode your missing
    values into special missing values. These special missing values are stored in
    the shadow part of the dataframe, which ends in _NA.
  • implemented shade where appropriate throughout naniar, and also added
    verifiers, is_shade, are_shade, which_are_shade, and removed which_are_shadow.
  • as_shadow and bind_shadow now return data of class shadow. This will
    feed into recode_shadow methods for flexibly adding new types of missing data.
  • Note that in the future shadow might be changed to nabble or something similar.

Minor feature

  • Functions add_label_shadow() and add_label_missings() gain arguments so you can only label according to the missingness / shadowy-ness of given variables.
  • new function which_are_shadow(), to tell you which values are shadows.
  • new function long_shadow(), which converts data in shadow/nabular form into a long format suitable for plotting. Related to #165
  • Added tests for miss_scan_count

Minor Changes

  • gg_miss_upset gets a better default presentation by ordering by the largest
    intersections, and also an improved error message when data with only 1 or no
    variables have missing values.
  • shadow_shift gains a more informative error message when it doesn't know the class.
  • Changed common_na_string to include escape characters for "?", "", "." so
    that if they are used in replacement or searching functions they don't return
    the wildcard results from the characters "?", "    ", and ".".
  • miss_case_table and miss_var_table now has final column names pct_vars,
    and pct_cases instead of pct_miss - fixes #178.

Breaking Changes

  • Deprecated old names of the scalar missingness summaries, in favour of a more
    consistent syntax #171. The old the and new are:
old_names new_names
miss_case_pct pct_miss_case
miss_case_prop prop_miss_case
miss_var_pct pct_miss_var
miss_var_prop prop_miss_var
complete_case_pct pct_complete_case
complete_case_prop prop_complete_case
complete_var_pct pct_complete_var
complete_var_prop prop_complete_var

These old names will be made defunct in 0.5.0, and removed completely in 0.6.0.

  • impute_below has changed to be an alias of shadow_shift - that is it operates on a single vector. impute_below_all operates on all columns in a dataframe (as specified in #159)

Bug fix

  • Ensured that miss_scan_count actually return'd something.
  • gg_miss_var(airquality) now prints the ggplot - a typo meant that this did not print the plot
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