impute_fixed, impute_zero, and impute_factor. notably these do not implement “scoped variants” which were previously implemented - for example, impute_fixed_if etc. This is in favour of using the new across workflow within dplyr, and it is easier to maintain. #261digit argument to miss_var_summary to help display %missing data correctly when there is a very small fraction of missingness. #284impute_mode - resolves #213.geom_miss_point() works with shape argument #290all_complete, which was implemented as !anyNA(x) but should be all(complete.cases(x)).any_na() (and any_miss()) and any_complete(). Rework examples to demonstrate workflow for finding complete variables.shadow_long not working when gathering variables of mixed type. Fix involves specifying a value transform, which defaults to character. #314Date, POSIXct and POSIXlt methods for impute_below() - #158gg_miss_fct where it used a deprecated function from forcats - #342cli::cli_abort and cli::cli_warn instead of stop and warn (#326)expect_snapshot instead of expect_error (#326)shadow_shift - #193miss_case_cumsum() and miss_var_cumsum() - #257Version 1.0.0 of naniar is to signify that this release is associated with the publication of the associated JSS paper, doi:10.18637/jss.v105.i07. There are also a few small changes that have been implemented in this release, which are described below.
There is still a lot to do in naniar, and this release does not signify that there are no changes upcoming, more so to establish that this is a stable release, and that any changes upcoming will go through a more formal deprecation process and so on.
tidyr::gather with tidyr::pivot_longer - resolves #301set_n_miss and set_prop_miss functions - resolved #298gg_miss_var() where a warning appears to due change in how to remove legend #288.vctrs and cli - which are both free dependencies as they are used within the already used tidyverse already.mcar_test() for Little’s (1988) statistical test for missing completely at random (MCAR) data. The null hypothesis in this test is that the data is MCAR, and the test statistic is a chi-squared value. Given a high statistic value and low p-value, we can conclude data are not missing completely at random. Thanks to Andrew Heiss for the PR.common_na_strings gains "#N/A".miss_var_span() (#270) where the number of missings + number of complete values added up to more than the number of rows in the data. This was due to the remainder not being used when calculating the number of complete values.recode_shadow() (#272) where adding the same special missing value in two subsequent operations fails.replace_with_na when columns provided that don’t exist (see #160). Thank you to michael-dewar for their help with this.nabular() and bind_shadow(). In doing so removes the functions, as_shadow(), is_shadow(), is_nabular(), new_nabular(), new_shadow(). These were mostly used internally and it is not expected that users would have used this functions. If these were used, please file an issue and I can implement them again.miss_var_summary(), miss_var_table(), and prop_miss_var(), resulting in a 3-10x speedup.tibble and subsequent downstream impacts on simputation.miss_var_prop()complete_var_prop()miss_var_pct()complete_var_pct()miss_case_prop()complete_case_prop()miss_case_pct()complete_case_pct()Instead use: prop_miss_var(), prop_complete_var(), pct_miss_var(), pct_complete_var(), prop_miss_case(), prop_complete_case(), pct_miss_case(), pct_complete_case(). (see 242)
replace_to_na() was made defunct, please use replace_with_na() instead. (see 242)miss_var_cumsum and miss_case_cumsum are now exportedmap_dfc instead of map_dfrowSums(is.na(x)), which was 3 times faster.gg_miss_fct() where warning is given for non explicit NA values - see 241.tibble() not data_frame()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).WORDLIST for spelling thanks to usethis::use_spell_check()@seealso bug (#228) (@sfirke)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.
new_tibblenew_tibble #220 - Thanks to Kirill Müller.rlang #218 - thanks for Lionel Henry.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 variantsany_shade() returns a logical TRUE or FALSE depending on if there are any shade valuesnabular() 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).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. #186Added gg_miss_upset to replace workflow of:
data %>%
as_shadow_upset() %>%
UpSetR::upset()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.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.shadow might be changed to nabble or something similar.add_label_shadow() and add_label_missings() gain arguments so you can only label according to the missingness / shadowy-ness of given variables.which_are_shadow(), to tell you which values are shadows.long_shadow(), which converts data in shadow/nabular form into a long format suitable for plotting. Related to #165miss_scan_countgg_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.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.| 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)miss_scan_count actually return’d something.gg_miss_var(airquality) now prints the ggplot - a typo meant that this did not print the plotThis is a patch release that removes tidyselect from the package Imports, as it is unnecessary. Fixes #174
all_miss() / all_na() equivalent to all(is.na(x))any_complete() equivalent to all(complete.cases(x))any_miss() equivalent to anyNA(x)common_na_numbers and finalised common_na_strings - to provide a list of commonly used NA values #168miss_var_which, to lists the variable names with missingsAdded as_shadow_upset which gets the data into a format suitable for plotting as an UpSetR plot:
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.
complete_{case/var}_{pct/prop}, which complement miss_{var/case}_{pct/prop} #150Added 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.
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
gg_miss_case and gg_miss_var to lorikeet purple (from ochRe package: https://github.com/ropenscilabs/ochRe)gg_miss_case
order_cases = TRUE.show_pct option to be consistent with gg_miss_var #153gg_miss_which is rotated 90 degrees so it is easier to read variable namesgg_miss_fct uses a minimal theme and tilts the axis labels #118.is_na and are_na from rlang.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”.
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.
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 variablesreplace_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: “doi: 10.1136/bmjopen-2014-007450” and Barnett et al. 2017: “doi: 10.1136/bmjopen-2017-017284”
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)
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.gg_miss_case to be clearer and less cluttered ( #117), also added n order_cases option to order by cases.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.oceanbuoys now is numeric type for year, latitude, and longitude, previously it was factor. See related issueshadow_shift when there are Inf or -Inf values (see #117)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.
naniar”naniar onto CRAN, updates to naniar will happen reasonably regularly after this approximately every 1-2 monthsnaniarmiss_case_cumsum / miss_var_cumsum / replace_to_nagg_var_cumsum & gg_case_cumsumgroup_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()label_missing* to label_miss to be more consistent with the rest of naniarpct and prop helpers (#78)miss_df_pct - this was literally the same as pct_miss or prop_miss.gg_miss_var gets a show_pct argument to show the percentage of missing values (Thanks Jennifer for the helpful feedback! :))miss_var_summary & miss_case_summary now have consistent output (one was ordered by n_missing, not the other).miss_case_pctenquo_x is now xreplace_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.add_* functions handle bare unqouted names where appropriate as per #61add_* familygeom_missing_point() to geom_miss_point(), to keep consistent with the rest of the functions in naniar.brfss and tao as per #59add_label_missings()add_label_shadow()cast_shadow()cast_shadow_shift()cast_shadow_shift_label()
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 suffixcast_shadow() - acts like bind_shadow() but allows for specifying which columns to addshadow_shift now has a method for factors - powered by forcats::fct_explicit_na() #3
is_na function to label_natidy-miss-[topic]gg_missing_* is changed to gg_miss_* to fit with other syntaxmiss_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.pedestrian - contains hourly counts of pedestriansmiss_ts_run(): return the number of missings / complete in a single runmiss_ts_summary(): return the number of missings in a given time periodgg_miss_ts(): plot the number of missings in a given time periodnaniar 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.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.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()
miss_* = I want to explore missing valuesmiss_case_* = I want to explore missing casesmiss_case_pct = I want to find the percentage of cases containing a missing valuemiss_case_summary = I want to find the number / percentage of missings in each casemiss_case_table = I want a tabulation of the number / percentage of cases missingThis 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”).
n_complete is a complement to n_miss, and counts the number of complete values in a vector, matrix, or dataframe.shadow_shift now handles cases where there is only 1 complete value in a vector.testthat.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
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 “doi: 10.1136/bmjopen-2014-007450”
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
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.
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.
geom_missing_point() now works in a way that we expect! Thanks to Miles McBain for working out how to get this to work.percent_missing_df returns the percentage of missing data for a data.framepercent_missing_var the percentage of variables that contain missing valuespercent_missing_case the percentage of cases that contain missing values.table_missing_var table of missing information for variablestable_missing_case table of missing information for casessummary_missing_var summary of missing information for variables (counts, percentages)summary_missing_case summary of missing information for variables (counts, percentages)