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)Version 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_df
rowSums(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_tibble
new_tibble
#220 - Thanks to Kirill Müller.rlang
#218 - thanks for Lionel Henry.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
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_count
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.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
=========================
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.
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:
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.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".
=========================
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 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
onto CRAN, updates to naniar
will
happen reasonably regularly after this approximately every 1-2 months=========================
naniar
miss_case_cumsum
/ miss_var_cumsum
/ replace_to_na
gg_var_cumsum
& gg_case_cumsum
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()
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_pct
enquo_x
is now x
=========================
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.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 #59=========================
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
is_na
function to label_na
tidy-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)