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naniar

Data Structures, Summaries, and Visualisations for Missing Data

Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. 'naniar' provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of 'ggplot2' and tidy data. The work is fully discussed at Tierney & Cook (2018) .

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naniar provides principled, tidy ways to summarise, visualise, and manipulate missing data with minimal deviations from the workflows in ggplot2 and tidy data. It does this by providing:

  • Shadow matrices, a tidy data structure for missing data:
    • bind_shadow() and nabular()
  • Shorthand summaries for missing data:
    • n_miss() and n_complete()
    • pct_miss()and pct_complete()
  • Numerical summaries of missing data in variables and cases:
    • miss_var_summary() and miss_var_table()
    • miss_case_summary(), miss_case_table()
  • Visualisation for missing data:
    • geom_miss_point()
    • gg_miss_var()
    • gg_miss_case()
    • gg_miss_fct()

For more details on the workflow and theory underpinning naniar, read the vignette Getting started with naniar.

For a short primer on the data visualisation available in naniar, read the vignette Gallery of Missing Data Visualisations.

Installation

You can install naniar from CRAN:

install.packages("naniar")

Or you can install the development version on github using remotes:

# install.packages("remotes")
remotes::install_github("njtierney/naniar")

A short overview of naniar

Visualising missing data might sound a little strange - how do you visualise something that is not there? One approach to visualising missing data comes from ggobi and manet, which replaces NA values with values 10% lower than the minimum value in that variable. This visualisation is provided with the geom_miss_point() ggplot2 geom

  • which we illustrate by exploring the relationship between Ozone and Solar radiation from the airquality dataset.
 
library(ggplot2)
 
ggplot(data = airquality,
       aes(x = Ozone,
           y = Solar.R)) +
  geom_point()
#> Warning: Removed 42 rows containing missing values (geom_point).

ggplot2 does not handle these missing values, and we get a warning message about the missing values.

We can instead use geom_miss_point() to display the missing data

 
library(naniar)
 
ggplot(data = airquality,
       aes(x = Ozone,
           y = Solar.R)) +
  geom_miss_point()

geom_miss_point() has shifted the missing values to now be 10% below the minimum value. The missing values are a different colour so that missingness becomes pre-attentive. As it is a ggplot2 geom, it supports features like faceting and other ggplot features.

 
p1 <-
ggplot(data = airquality,
       aes(x = Ozone,
           y = Solar.R)) + 
  geom_miss_point() + 
  facet_wrap(~Month, ncol = 2) + 
  theme(legend.position = "bottom")
 
p1

Data Structures

naniar provides a data structure for working with missing data, the shadow matrix (Swayne and Buja, 1998). The shadow matrix is the same dimension as the data, and consists of binary indicators of missingness of data values, where missing is represented as “NA”, and not missing is represented as “!NA”, and variable names are kep the same, with the added suffix “_NA" to the variables.

 
head(airquality)
#>   Ozone Solar.R Wind Temp Month Day
#> 1    41     190  7.4   67     5   1
#> 2    36     118  8.0   72     5   2
#> 3    12     149 12.6   74     5   3
#> 4    18     313 11.5   62     5   4
#> 5    NA      NA 14.3   56     5   5
#> 6    28      NA 14.9   66     5   6
 
as_shadow(airquality)
#> # A tibble: 153 x 6
#>    Ozone_NA Solar.R_NA Wind_NA Temp_NA Month_NA Day_NA
#>    <fct>    <fct>      <fct>   <fct>   <fct>    <fct> 
#>  1 !NA      !NA        !NA     !NA     !NA      !NA   
#>  2 !NA      !NA        !NA     !NA     !NA      !NA   
#>  3 !NA      !NA        !NA     !NA     !NA      !NA   
#>  4 !NA      !NA        !NA     !NA     !NA      !NA   
#>  5 NA       NA         !NA     !NA     !NA      !NA   
#>  6 !NA      NA         !NA     !NA     !NA      !NA   
#>  7 !NA      !NA        !NA     !NA     !NA      !NA   
#>  8 !NA      !NA        !NA     !NA     !NA      !NA   
#>  9 !NA      !NA        !NA     !NA     !NA      !NA   
#> 10 NA       !NA        !NA     !NA     !NA      !NA   
#> # … with 143 more rows

Binding the shadow data to the data you help keep better track of the missing values. This format is called “nabular”, a portmanteau of NA and tabular. You can bind the shadow to the data using bind_shadow or nabular:

bind_shadow(airquality)
#> # A tibble: 153 x 12
#>    Ozone Solar.R  Wind  Temp Month   Day Ozone_NA Solar.R_NA Wind_NA
#>    <int>   <int> <dbl> <int> <int> <int> <fct>    <fct>      <fct>  
#>  1    41     190   7.4    67     5     1 !NA      !NA        !NA    
#>  2    36     118   8      72     5     2 !NA      !NA        !NA    
#>  3    12     149  12.6    74     5     3 !NA      !NA        !NA    
#>  4    18     313  11.5    62     5     4 !NA      !NA        !NA    
#>  5    NA      NA  14.3    56     5     5 NA       NA         !NA    
#>  6    28      NA  14.9    66     5     6 !NA      NA         !NA    
#>  7    23     299   8.6    65     5     7 !NA      !NA        !NA    
#>  8    19      99  13.8    59     5     8 !NA      !NA        !NA    
#>  9     8      19  20.1    61     5     9 !NA      !NA        !NA    
#> 10    NA     194   8.6    69     5    10 NA       !NA        !NA    
#> # … with 143 more rows, and 3 more variables: Temp_NA <fct>,
#> #   Month_NA <fct>, Day_NA <fct>
nabular(airquality)
#> # A tibble: 153 x 12
#>    Ozone Solar.R  Wind  Temp Month   Day Ozone_NA Solar.R_NA Wind_NA
#>    <int>   <int> <dbl> <int> <int> <int> <fct>    <fct>      <fct>  
#>  1    41     190   7.4    67     5     1 !NA      !NA        !NA    
#>  2    36     118   8      72     5     2 !NA      !NA        !NA    
#>  3    12     149  12.6    74     5     3 !NA      !NA        !NA    
#>  4    18     313  11.5    62     5     4 !NA      !NA        !NA    
#>  5    NA      NA  14.3    56     5     5 NA       NA         !NA    
#>  6    28      NA  14.9    66     5     6 !NA      NA         !NA    
#>  7    23     299   8.6    65     5     7 !NA      !NA        !NA    
#>  8    19      99  13.8    59     5     8 !NA      !NA        !NA    
#>  9     8      19  20.1    61     5     9 !NA      !NA        !NA    
#> 10    NA     194   8.6    69     5    10 NA       !NA        !NA    
#> # … with 143 more rows, and 3 more variables: Temp_NA <fct>,
#> #   Month_NA <fct>, Day_NA <fct>

Using the nabular format helps you manage where missing values are in your dataset and make it easy to do visualisations where you split by missingness:

 
airquality %>%
  bind_shadow() %>%
  ggplot(aes(x = Temp,
             fill = Ozone_NA)) + 
  geom_density(alpha = 0.5)

And even visualise imputations

 
airquality %>%
  bind_shadow() %>%
  simputation::impute_lm(Ozone ~ Temp + Solar.R) %>%
  ggplot(aes(x = Solar.R,
             y = Ozone,
             colour = Ozone_NA)) + 
  geom_point()
#> Warning: Removed 7 rows containing missing values (geom_point).

Or perform an upset plot - to plot of the combinations of missingness across cases, using the gg_miss_upset function

 
gg_miss_upset(airquality)

naniar does this while following consistent principles that are easy to read, thanks to the tools of the tidyverse.

naniar also provides handy visualations for each variable:

 
gg_miss_var(airquality)

Or the number of missings in a given variable at a repeating span

gg_miss_span(pedestrian,
             var = hourly_counts,
             span_every = 1500)

You can read about all of the visualisations in naniar in the vignette Gallery of missing data visualisations using naniar.

naniar also provides handy helpers for calculating the number, proportion, and percentage of missing and complete observations:

n_miss(airquality)
#> [1] 44
n_complete(airquality)
#> [1] 874
prop_miss(airquality)
#> [1] 0.04793028
prop_complete(airquality)
#> [1] 0.9520697
pct_miss(airquality)
#> [1] 4.793028
pct_complete(airquality)
#> [1] 95.20697

Numerical summaries for missing data

naniar provides numerical summaries of missing data, that follow a consistent rule that uses a syntax begining with miss_. Summaries focussing on variables or a single selected variable, start with miss_var_, and summaries for cases (the initial collected row order of the data), they start with miss_case_. All of these functions that return dataframes also work with dplyr’s group_by().

For example, we can look at the number and percent of missings in each case and variable with miss_var_summary(), and miss_case_summary(), which both return output ordered by the number of missing values.

 
miss_var_summary(airquality)
#> # A tibble: 6 x 3
#>   variable n_miss pct_miss
#>   <chr>     <int>    <dbl>
#> 1 Ozone        37    24.2 
#> 2 Solar.R       7     4.58
#> 3 Wind          0     0   
#> 4 Temp          0     0   
#> 5 Month         0     0   
#> 6 Day           0     0
miss_case_summary(airquality)
#> # A tibble: 153 x 3
#>     case n_miss pct_miss
#>    <int>  <int>    <dbl>
#>  1     5      2     33.3
#>  2    27      2     33.3
#>  3     6      1     16.7
#>  4    10      1     16.7
#>  5    11      1     16.7
#>  6    25      1     16.7
#>  7    26      1     16.7
#>  8    32      1     16.7
#>  9    33      1     16.7
#> 10    34      1     16.7
#> # … with 143 more rows

You could also group_by() to work out the number of missings in each variable across the levels within it.

 
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
airquality %>%
  group_by(Month) %>%
  miss_var_summary()
#> # A tibble: 25 x 4
#>    Month variable n_miss pct_miss
#>    <int> <chr>     <int>    <dbl>
#>  1     5 Ozone         5     16.1
#>  2     5 Solar.R       4     12.9
#>  3     5 Wind          0      0  
#>  4     5 Temp          0      0  
#>  5     5 Day           0      0  
#>  6     6 Ozone        21     70  
#>  7     6 Solar.R       0      0  
#>  8     6 Wind          0      0  
#>  9     6 Temp          0      0  
#> 10     6 Day           0      0  
#> # … with 15 more rows

You can read more about all of these functions in the vignette “Getting Started with naniar”.

Contributions

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Future Work

  • Extend the geom_miss_* family to include categorical variables, Bivariate plots: scatterplots, density overlays
  • SQL translation for databases
  • Big Data tools (sparklyr, sparklingwater)
  • Work well with other imputation engines / processes
  • Provide tools for assessing goodness of fit for classical approaches of MCAR, MAR, and MNAR (graphical inference from nullabor package)

Acknowledgements

Firstly, thanks to Di Cook for giving the initial inspiration for the package and laying down the rich theory and literature that the work in naniar is built upon. Naming credit (once again!) goes to Miles McBain. Among various other things, Miles also worked out how to overload the missing data and make it work as a geom. Thanks also to Colin Fay for helping me understand tidy evaluation and for features such as replace_to_na, miss_*_cumsum, and more.

A note on the name

naniar was previously named ggmissing and initially provided a ggplot geom and some other visualisations. ggmissing was changed to naniar to reflect the fact that this package is going to be bigger in scope, and is not just related to ggplot2. Specifically, the package is designed to provide a suite of tools for generating visualisations of missing values and imputations, manipulate, and summarise missing data.

Well, I think it is useful to think of missing values in data being like this other dimension, perhaps like C.S. Lewis’s Narnia - a different world, hidden away. You go inside, and sometimes it seems like you’ve spent no time in there but time has passed very quickly, or the opposite. Also, NAniar = na in r, and if you so desire, naniar may sound like “noneoya” in an nz/aussie accent. Full credit to @MilesMcbain for the name, and @Hadley for the rearranged spelling.

News

naniar 0.4.2 (2018/12/27)

Improvements

  • The geom_miss_point() ggplot2 layer can now be converted into an interactive web-based version by the ggplotly() function in the plotly package. In order for this to work, naniar now exports the geom2trace.GeomMissPoint() function (users should never need to call geom2trace.GeomMissPoint() directly -- ggplotly() calls it for you).
  • adds WORDLIST for spelling thanks to usethis::use_spell_check()
  • fix documentation @seealso bug (#228) (@sfirke)

Dependency fixes

  • Thanks to a PR (#223) from @romainfrancois:

    • This fixes two problems that were identified as part of reverse dependency checks of dplyr 0.8.0 release candidate. https://github.com/tidyverse/dplyr/blob/revdep_dplyr_0_8_0_RC/revdep/problems.md#naniar

    • n() must be imported or prefixed like any other function. In the PR, I've changed 1:n() to dplyr::row_number() as naniar seems to prefix all dplyr functions.

    • update_shadow was only restoring the class attributes, changed so that it restores all attributes, this was causing problems when data was a grouped_df. This likely was a problem before too, but dplyr 0.8.0 is stricter about what is a grouped data frame.

naniar 0.4.1 (2018/12/14)

Minor Changes

  • pkgdown updates: update favicon and logo, set up for gh-pages deployment
  • use a scalar integer in new_tibble

naniar 0.4.1 (2018/11/20) "Aslan's Song"

Minor Change

naniar 0.4.0 (2018/09/10) "An Unexpected Meeting"

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

naniar 0.3.1 (2018/06/10) "Strawberry's Adventure"

Minor Change

This is a patch release that removes tidyselect from the package Imports, as it is unnecessary. Fixes #174

naniar 0.3.0 (2018/06/06) "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".

naniar 0.2.0 (2018/02/08) ("The First Joke and Other Matters")

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

New Features

  • Speed improvements. Thanks to the help, contributions, and discussion with Romain François and Jim Hester, naniar now has greatly improved speed for calculating the missingness in each row. These speedups should continue to improve in future releases.

  • New "scoped variants" of replace_with_na, thankyou to Colin Fay for his work on this:

    • replace_with_na_all replaces all NAs across the dataframe that meet a specified condition (using the syntax ~.x == -99)
    • replace_with_na_at replaces all NAs across for specified variables
    • replace_with_na_if replaces all NAs for those variables that satisfy some predicate function (e.g., is.character)

  • added which_na - replacement for which(is.na(x))

  • miss_scan_count. This makes it easier for users to search for particular occurrences of these values across their variables. #119

  • n_miss_row calculates the number of missing values in each row, returning a vector. There are also 3 other functions which are similar in spirit: n_complete_row, prop_miss_row, and prop_complete_row, which return a vector of the number of complete obserations, the proportion of missings in a row, and the proportion of complete obserations in a row

  • add_miss_cluster is a new function that calculates a cluster of missingness for each row, using hclust. This can be useful in exploratory modelling of missingness, similar to Tierney et al 2015. and Barnett et al. 2017

  • Now exported where_na - a function that returns the positions of NA values. For a dataframe it returns a matrix of row and col positions of NAs, and for a vector it returns a vector of positions of NAs. (#105)

Minor changes

  • Updated the vignette "Gallery of Missing Data Visualisations" to include the facet features and order_cases.
  • bind_shadow gains a only_miss argument. When set to FALSE (the default) it will bind a dataframe with all of the variables duplicated with their shadow. Setting this to TRUE will bind variables only those variables that contain missing values.
  • Cleaned up the visualisation of gg_miss_case to be clearer and less cluttered ( #117), also added n order_cases option to order by cases.
  • Added a facet argument to gg_miss_var, gg_miss_case, and gg_miss_span. This makes it easier for users to visualise these plots across the values of another variable. In the future I will consider adding facet to the other shorthand plotting function, but at the moment these seemed to be the ones that would benefit the most from this feature.

Bug fix

  • oceanbuoys now is numeric type for year, latitude, and longitude, previously it was factor. See related issue
  • Improved handling of shadow_shift when there are Inf or -Inf values (see #117)

Breaking change

  • Deprecated replace_to_na, with replace_with_na, as it is a more natural phrase ("replace coffee to tea" vs "replace coffee with tea"). This will be made defunct in the next version.

  • cast_shadow no longer works when called as cast_shadow(data). This action used to return all variables, and then shadow variables for the variables that only contained missing values. This was inconsistent with the use of cast_shadow(data, var1, var2). A new option has been added to bind_shadow that controls this - discussed below. See more details at issue 65.

  • Change behaviour of cast_shadow so that the default option is to return only the variables that contain missings. This is different to bind_shadow, which binds a complete shadow matrix to the dataframe. A way to think about this is that the shadow is only cast on variables that contain missing values, whereas a bind is binding a complete shadow to the data. This may change in the future to be the default option for bind_shadow.

Minor Changes

  • Update vignettes to have floating menu and better figure size.
  • minor changes to graphics in gg_miss_fct - change legend title from "Percent Missing" to "% Miss".

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 (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.

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.
install.packages("naniar")

0.6.1 by Nicholas Tierney, 5 months ago

https://github.com/njtierney/naniar

Report a bug at https://github.com/njtierney/naniar/issues

Browse source code at https://github.com/cran/naniar

Authors: Nicholas Tierney [aut, cre] , Di Cook [aut] , Miles McBain [aut] , Colin Fay [aut] , Mitchell O'Hara-Wild [ctb] , Jim Hester [ctb] , Luke Smith [ctb] , Andrew Heiss [ctb]

Documentation:   PDF Manual  

Task views: Missing Data

MIT + file LICENSE license

Imports dplyr, ggplot2, purrr, tidyr, tibble, norm, magrittr, stats, visdat, rlang, forcats, viridis, glue, UpSetR

Suggests knitr, rmarkdown, testthat, rpart, rpart.plot, covr, gridExtra, wakefield, vdiffr, here, simputation, imputeTS, gdtools, Hmisc, spelling

Imported by povcalnetR, squashinformr, suddengains.

Suggested by causalCmprsk.

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