Digory and his Uncle Are Both in Trouble

New Features

• Added all_miss() / all_na() equivalent to all(is.na(x))

• Added any_complete() equivalent to all(complete.cases(x))

• Added any_miss() equivalent to anyNA(x)

• Added common_na_numbers and finalised common_na_strings - to provide a
  list of commonly used NA values
  #168

• Added miss_var_which, to lists the variable names with missings

• Added as_shadow_upset which gets the data into a format suitable for
  plotting as an UpSetR plot:

  airquality %>%
    as_shadow_upset() %>%
    UpSetR::upset()

• Added some imputation functions to assist with exploring missingness
  structure and visualisation:

  • impute_below Perfoms as for shadow_shift, but performs on all columns.
    This means that it imputes missing values 10% below the range of the
    data (powered by shadow_shift), to facilitate graphical exloration of
    the data. Closes #145
    There are also scoped variants that work for specific named columns:
    impute_below_at, and for columns that satisfy some predicate function:
    impute_below_if.
  • impute_mean, imputes the mean value, and scoped variants
    impute_mean_at, and impute_mean_if.

• impute_below and shadow_shift gain arguments prop_below and jitter
  to control the degree of shift, and also the extent of jitter.

• Added complete_{case/var}_{pct/prop}, which complement
  miss_{var/case}_{pct/prop}
  #150

• Added unbind_shadow and unbind_data as helpers to remove shadow columns
  from data, and data from shadows, respectively.

• Added is_shadow and are_shadow to determine if something contains a
  shadow column. simimlar to rlang::is_na and rland::are_na, is_shadow
  this returns a logical vector of length 1, and are_shadow returns a logical
  vector of length of the number of names of a data.frame. This might be
  revisited at a later point (see any_shade in add_label_shadow).

• Aesthetics now map as expected in geom_miss_point(). This means you can write
  things like geom_miss_point(aes(colour = Month)) and it works appropriately.
  Fixed by Luke Smith in Pull request
  #144, fixing
  #137.

Minor Changes

• miss_var_summary and miss_case_summary now return use order = TRUE by
  default, so cases and variables with the most missings are presented in
  descending order. Fixes #163

• Changes for Visualisation:

  • Changed the default colours used in gg_miss_case and gg_miss_var to
    lorikeet purple (from ochRe package: https://github.com/ropenscilabs/ochRe)
  • gg_miss_case
    • The y axis label is now ...
    • Default presentation is with order_cases = TRUE.
    • Gains a show_pct option to be consistent with gg_miss_var
      #153
  • gg_miss_which is rotated 90 degrees so it is easier to read variable names
  • gg_miss_fct uses a minimal theme and tilts the axis labels
    #118.

• imported is_na and are_na from rlang.

• Added common_na_strings, a list of common NA values
  #168.

• Added some detail on alternative methods for replacing with NA in the
  vignette "replacing values with NA".

CRAN 0.1.0 Release "The Founding of naniar"

"The Founding of naniar the first version on CRAN! The name is taken from Chapter 9 of The Magician's Nephew. Below is the updated NEWS file

naniar 0.1.0 (2017/08/09) "The Founding of naniar"

=========================

• This is the first release of naniar onto CRAN, updates to naniar will happen reasonably regularly after this approximately every 1-2 months

naniar 0.0.9.9995 (2017/08/07)

=========================

Name change

• After careful consideration, I have changed back to naniar

Major Change

• three new functions : miss_case_cumsum / miss_var_cumsum / replace_to_na
• two new visualisations : gg_var_cumsum & gg_case_cumsum

New Feature

• group_by is now respected by the following functions:
  • miss_case_cumsum()
  • miss_case_summary()
  • miss_case_table()
  • miss_prop_summary()
  • miss_var_cumsum()
  • miss_var_run()
  • miss_var_span()
  • miss_var_summary()
  • miss_var_table()

Minor changes

• Reviewed documentation for all functions and improved wording, grammar, and
  style.
• Converted roxygen to roxygen markdown
• updated vignettes and readme
• added a new vignette "naniar-visualisation", to give a quick overview of the visualisations provided with naniar.
• changed label_missing* to label_miss to be more consistent with the rest
  of naniar
• Add pct and prop helpers (#78)
• removed miss_df_pct - this was literally the same as pct_miss or prop_miss.
• break larger files into smaller, more manageable files (#83)
• gg_miss_var gets a show_pct argument to show the percentage of missing values (Thanks Jennifer for the helpful feedback! :))

Minor changes

• miss_var_summary & miss_case_summary now have consistent output (one was ordered by n_missing, not the other).
• prevent error in miss_case_pct
• enquo_x is now x (as adviced by Hadley)
• Now has ByteCompile to TRUE
• add Colin to auth

narnia 0.0.9.9400 (2017/07/24)

=========================

new features

• replace_to_na is a complement to tidyr::replace_na and replaces a specified
  value from a variable to NA.
• gg_miss_fct returns a heatmap of the number of missings per variable for
  each level of a factor. This feature was very kindly contributed by
  Colin Fay.
• gg_miss_ functions now return a ggplot object, which behave as such.
  gg_miss_ basic themes can be overriden with ggplot functions. This fix
  was very kindly contributed by Colin Fay.
• removed defunct functions as per #63
• made add_* functions handle bare unqouted names where appropriate as per #61
• added tests for the add_* family
• got the svgs generated from vdiffr, thanks @karawoo!

breaking changes

• changed geom_missing_point() to geom_miss_point(), to keep consistent with the rest of the functions in naniar.

narnia 0.0.8.9100 (2017/06/23)

=========================

new features

• updated datasets brfss and tao as per #59

narnia 0.0.7.9992 (2017/06/22)

=========================

new features

• add_label_missings()

• add_label_shadow()

• cast_shadow()

• cast_shadow_shift()

• cast_shadow_shift_label()

• added github issue / contribution / pull request guides

• ts generic functions are now miss_var_span and miss_var_run, and gg_miss_span and work on data.frame's, as opposed to just ts objects.

• add_shadow_shift() adds a column of shadow_shifted values to the current dataframe, adding "_shift" as a suffix

• cast_shadow() - acts like bind_shadow() but allows for specifying which columns to add

• shadow_shift now has a method for factors - powered by forcats::fct_explicit_na() #3

bug fixes

• shadow_shift.numeric works when there is no variance (#37)

name changes

• changed is_na function to label_na
• renamed most files to have tidy-miss-[topic]
• gg_missing_* is changed to gg_miss_* to fit with other syntax

Removed functions

• Removed old functions miss_cat, shadow_df and shadow_cat, as they are no longer needed, and have been superceded by label_missing_2d, as_shadow, and is_na.

minor changes

• drastically reduced the size of the pedestrian dataset, consider 4 sensor locations, just for 2016.

New features

• New dataset, pedestrian - contains hourly counts of pedestrians
• First pass at time series missing data summaries and plots:
  • miss_ts_run(): return the number of missings / complete in a single run
  • miss_ts_summary(): return the number of missings in a given time period
  • gg_miss_ts(): plot the number of missings in a given time period

Name changes

• renamed package from naniar to narnia - I had to explain the spelling a few times when I was introducing the package and I realised that I should change the name. Fortunately it isn't on CRAN yet.

naniar 0.0.6.9100 (2017/03/21)

=========================

• Added prop_miss and the complement prop_complete. Where n_miss returns the number of missing values, prop_miss returns the proportion of missing values. Likewise, prop_complete returns the proportion of complete values.

Defunct functions

• As stated in 0.0.5.9000, to address Issue #38, I am moving towards the format miss_type_value/fun, because it makes more sense to me when tabbing through functions.

The left hand side functions have been made defunct in favour of the right hand side.
- percent_missing_case() --> miss_case_pct()
- percent_missing_var() --> miss_var_pct()
- percent_missing_df() --> miss_df_pct()
- summary_missing_case() --> miss_case_summary()
- summary_missing_var() --> miss_var_summary()
- table_missing_case() --> miss_case_table()
- table_missing_var() --> miss_var_table()

naniar 0.0.5.9000 (2016/01/08)

=========================

Deprecated functions

• To address Issue #38, I am moving towards the format miss_type_value/fun, because it makes more sense to me when tabbing through functions.
• miss_* = I want to explore missing values
• miss_case_* = I want to explore missing cases
• miss_case_pct = I want to find the percentage of cases containing a missing value
• miss_case_summary = I want to find the number / percentage of missings in each case
  miss_case_table = I want a tabulation of the number / percentage of cases missing

This is more consistent and easier to reason with.

Thus, I have renamed the following functions:
- percent_missing_case() --> miss_case_pct()
- percent_missing_var() --> miss_var_pct()
- percent_missing_df() --> miss_df_pct()
- summary_missing_case() --> miss_case_summary()
- summary_missing_var() --> miss_var_summary()
- table_missing_case() --> miss_case_table()
- table_missing_var() --> miss_var_table()

These will be made defunct in the next release, 0.0.6.9000 ("The Wood Between Worlds").

naniar 0.0.4.9000 (2016/12/31)

=========================

New features

• n_complete is a complement to n_miss, and counts the number of complete values in a vector, matrix, or dataframe.

Bug fixes

• shadow_shift now handles cases where there is only 1 complete value in a vector.

Other changes

• added much more comprehensive testing with testthat.

naniar 0.0.3.9901 (2016/12/18)

=========================

After a burst of effort on this package I have done some refactoring and thought hard about where this package is going to go. This meant that I had to make the decision to rename the package from ggmissing to naniar. The name may strike you as strange but it reflects the fact that there are many changes happening, and that we will be working on creating a nice utopia (like Narnia by CS Lewis) that helps us make it easier to work with missing data

New Features (under development)

• add_n_miss and add_prop_miss are helpers that add columns to a dataframe containing the number and proportion of missing values. An example has been provided to use decision trees to explore missing data structure as in Tierney et al

• geom_miss_point() now supports transparency, thanks to @seasmith (Luke Smith)

• more shadows. These are mainly around bind_shadow and gather_shadow, which are helper functions to assist with creating

Bug fixes

• geom_missing_point() broke after the new release of ggplot2 2.2.0, but this is now fixed by ensuring that it inherits from GeomPoint, rather than just a new Geom. Thanks to Mitchell O'hara-Wild for his help with this.

• missing data summaries table_missing_var and table_missing_case also now return more sensible numbers and variable names. It is possible these function names will change in the future, as these are kind of verbose.

• semantic versioning was incorrectly entered in the DESCRIPTION file as 0.2.9000, so I changed it to 0.0.2.9000, and then to 0.0.3.9000 now to indicate the new changes, hopefully this won't come back to bite me later. I think I accidentally did this with visdat at some point as well. Live and learn.

Other changes

• gathered related functions into single R files rather than leaving them in
  their own.

• correctly imported the %>% operator from magrittr, and removed a lot of chaff around @importFrom - really don't need to use @importFrom that often.

ggmissing 0.0.2.9000 (2016/07/29)

=========================

New Feature (und...

The Wrong Door

naniar 0.0.4.9000 (2016/12/31)

New features

• n_complete is a complement to n_miss, and counts the number of complete values in a vector, matrix, or dataframe.

Bug fixes

• shadow_shift now handles cases where there is only 1 complete value in a vector.

Other changes

• added much more comprehensive testing with testthat.

naniar 0.0.3.9901 (2016/12/18)

New features

• add_n_miss and add_prop_miss are helpers that add columns to a dataframe containing the number and proportion of missing values. An example has been provided to use decision trees to explore missing data structure as in Tierney et al
• geom_miss_point() now supports transparency, thanks to @seasmith (Luke Smith)

naniar 0.0.3.9000 (2016/12/18)

After a burst of effort on this package I have done some refactoring and thought hard about where this package is going to go. This meant that I had to make the decision to rename the package from ggmissing to naniar. The name may strike you as strange but it reflects the fact that there are many changes happening, and that we will be working on creating a nice utopia (like Narnia by CS Lewis) that helps us make it easier to work with missing data

New Features (under development)

• more shadows. These are mainly around bind_shadow and gather_shadow, which are helper functions to assist with creating

Bug fixes

• geom_missing_point() broke after the new release of ggplot2 2.2.0, but this is now fixed by ensuring that it inherits from GeomPoint, rather than just a new Geom. Thanks to Mitchell O'hara-Wild for his help with this.
• missing data summaries table_missing_var and table_missing_case also now return more sensible numbers and variable names. It is possible these function names will change in the future, as these are kind of verbose.
• semantic versioning was incorrectly entered in the DESCRIPTION file as 0.2.9000, so I changed it to 0.0.2.9000, and then to 0.0.3.9000 now to indicate the new changes, hopefully this won't come back to bite me later. I think I accidentally did this with visdat at some point as well. Live and learn.

Other changes

• gathered related functions into single R files rather than leaving them in
  their own.
• correctly imported the %>% operator from magrittr, and removed a lot of chaff around @importFrom - really don't need to use @importFrom that often.

ggmissing 0.0.2.9000 (2016/07/29)

New Feature (under development)

• geom_missing_point() now works in a way that we expect! Thanks to Miles McBain for working out how to get this to work.

ggmissing 0.0.1.9000 (2016/07/29)

New Feature (under development)

• tidy summaries for missing data:
  • percent_missing_df returns the percentage of missing data for a data.frame
  • percent_missing_var the percentage of variables that contain missing values
  • percent_missing_case the percentage of cases that contain missing values.
  • table_missing_var table of missing information for variables
  • table_missing_case table of missing information for cases
  • summary_missing_var summary of missing information for variables (counts, percentages)
  • summary_missing_case summary of missing information for variables (counts, percentages)
• gg_missing_col: plot the missingness in each variable
• gg_missing_row: plot the missingness in each case
• gg_missing_which: plot which columns contain missing data.