Package index • naniar

Data structures for missing data Creation and Manipulation of Shadow Matrices
`as_shadow()`	Create shadows
`as_shadow_upset()`	Convert data into shadow format for doing an upset plot
`bind_shadow()`	Bind a shadow dataframe to original data
`nabular()`	Convert data into nabular form by binding shade to it
`gather_shadow()`	Long form representation of a shadow matrix
`shade()`	Create new levels of missing
`shadow_long()`	Reshape shadow data into a long format
`unbind_shadow()` `unbind_data()`	Unbind (remove) shadow from data, and vice versa
`shadow_shift()`	Shift missing values to facilitate missing data exploration/visualisation
Create special missing values Create special missing values so that they don’t get lost!
`recode_shadow()`	Add special missing values to the shadow matrix
Visualisation Visualise missing data
`geom_miss_point()`	geom_miss_point
`stat_miss_point()`	stat_miss_point
`gg_miss_case()`	Plot the number of missings per case (row)
`gg_miss_case_cumsum()`	Plot of cumulative sum of missing for cases
`gg_miss_fct()`	Plot the number of missings for each variable, broken down by a factor
`gg_miss_span()`	Plot the number of missings in a given repeating span
`gg_miss_upset()`	Plot the pattern of missingness using an upset plot.
`gg_miss_var()`	Plot the number of missings for each variable
`gg_miss_var_cumsum()`	Plot of cumulative sum of missing value for each variable
`gg_miss_which()`	Plot which variables contain a missing value
`reexports` `vis_miss` `%>%` `is_na` `are_na`	Objects exported from other packages
Numerical Summaries Provide tidy data frame summaries of missingness
`miss_var_prop()` `complete_var_prop()` `miss_var_pct()` `complete_var_pct()` `miss_case_prop()` `complete_case_prop()` `miss_case_pct()` `complete_case_pct()`	Proportion of variables containing missings or complete values
`miss_case_cumsum()`	Summarise the missingness in each case
`miss_case_summary()`	Summarise the missingness in each case
`miss_case_table()`	Tabulate missings in cases.
`miss_prop_summary()`	Proportions of missings in data, variables, and cases.
`miss_scan_count()`	Search and present different kinds of missing values
`miss_summary()`	Collate summary measures from naniar into one tibble
`miss_var_cumsum()`	Cumulative sum of the number of missings in each variable
`miss_var_run()`	Find the number of missing and complete values in a single run
`miss_var_span()`	Summarise the number of missings for a given repeating span on a variable
`miss_var_summary()`	Summarise the missingness in each variable
`miss_var_table()`	Tabulate the missings in the variables
`miss_var_which()`	Which variables contain missing values?
Handy helpers Handy helpers
`n_var_complete()` `n_case_complete()`	The number of variables with complete values
`n_var_miss()` `n_case_miss()`	The number of variables or cases with missing values
`n_complete()`	Return the number of complete values
`n_complete_row()`	Return a vector of the number of complete values in each row
`n_miss()`	Return the number of missing values
`n_miss_row()`	Return a vector of the number of missing values in each row
`prop_miss_case()` `prop_complete_case()`	Proportion of cases that contain a missing or complete values.
`prop_miss_var()` `prop_complete_var()`	Proportion of variables containing missings or complete values
`prop_complete()`	Return the proportion of complete values
`prop_complete_row()`	Return a vector of the proportion of missing values in each row
`prop_miss()`	Return the proportion of missing values
`prop_miss_row()`	Return a vector of the proportion of missing values in each row
`pct_miss_case()` `pct_complete_case()`	Percentage of cases that contain a missing or complete values.
`pct_miss_var()` `pct_complete_var()`	Percentage of variables containing missings or complete values
`pct_complete()`	Return the percent of complete values
`pct_miss()`	Return the percent of missing values
`any_na()` `any_miss()` `any_complete()` `all_na()` `all_miss()` `all_complete()`	Identify if there are any or all missing or complete values
`any_row_miss()`	Helper function to determine whether there are any missings
`is_shade()` `are_shade()` `any_shade()`	Detect if this is a shade
`which_are_shade()`	Which variables are shades?
`common_na_numbers`	Common number values for NA
`common_na_strings`	Common string values for NA
Add columns Add missing data summaries/tool columns
`add_any_miss()`	Add a column describing presence of any missing values
`add_label_missings()`	Add a column describing if there are any missings in the dataset
`add_label_shadow()`	Add a column describing whether there is a shadow
`add_miss_cluster()`	Add a column that tells us which "missingness cluster" a row belongs to
`add_n_miss()`	Add column containing number of missing data values
`add_prop_miss()`	Add column containing proportion of missing data values
`add_shadow()`	Add a shadow column to dataframe
`add_shadow_shift()`	Add a shadow shifted column to a dataset
`add_span_counter()`	Add a counter variable for a span of dataframe
Replacing values with and to NA Functions to help replace certain values with NA, which includes scoped variants (_at, _if, _all) that take formulas for flexible approachs
`replace_with_na()`	Replace values with missings
`replace_with_na_all()`	Replace all values with NA where a certain condition is met
`replace_with_na_at()`	Replace specified variables with NA where a certain condition is met
`replace_with_na_if()`	Replace values with NA based on some condition, for variables that meet some predicate
`replace_to_na()`	Replace values with missings
`replace_na_with()`	Replace NA value with provided value
Imputation helpers Simple imputation methods for exploring visualisation and missingness structure
`impute_below()`	Impute data with values shifted 10 percent below range.
`impute_below(<numeric>)`	Impute numeric values below a range for graphical exploration
`impute_below_all()`	Impute data with values shifted 10 percent below range.
`impute_below_at()`	Scoped variants of `impute_below`
`impute_below_if()`	Scoped variants of `impute_below`
`impute_factor()`	Impute a factor value into a vector with missing values
`impute_fixed()`	Impute a fixed value into a vector with missing values
`impute_mean()`	Impute the mean value into a vector with missing values
`impute_median()`	Impute the median value into a vector with missing values
`impute_mode()`	Impute the mode value into a vector with missing values
`impute_zero()`	Impute zero into a vector with missing values
`impute_mean_all()` `impute_mean_at()` `impute_mean_if()`	Scoped variants of `impute_mean`
`impute_median_all()` `impute_median_at()` `impute_median_if()`	Scoped variants of `impute_median`
`set_prop_miss()` `set_n_miss()`	Set a proportion or number of missing values
Package title details Details of the package naniar
`naniar-package` `naniar`	naniar
Cast Shadows Add shadow information to the dataframe while reducing it to the variables of interest
`cast_shadow()`	Add a shadow column to a dataset
`cast_shadow_shift()`	Add a shadow and a shadow_shift column to a dataset
`cast_shadow_shift_label()`	Add a shadow column and a shadow shifted column to a dataset
Misc helpers Misc helpers
`label_miss_1d()`	Label a missing from one column
`label_miss_2d()`	label_miss_2d
`label_missings()`	Is there a missing value in the row of a dataframe?
`where_na()`	Which rows and cols contain missings?
`which_na()`	Which elements contain missings?
`.where()`	Split a call into two components with a useful verb name
Data Sources For practice and example usecases in naniar
`oceanbuoys`	West Pacific Tropical Atmosphere Ocean Data, 1993 & 1997.
`pedestrian`	Pedestrian count information around Melbourne for 2016
`riskfactors`	The Behavioral Risk Factor Surveillance System (BRFSS) Survey Data, 2009.
Little’s MCAR test For performing Little’s MCAR test
`mcar_test()`	Little's missing completely at random (MCAR) test
ggplot2 extensions Custom ggplot geoms built to extend ggplot for missing values
`StatMissPoint`	naniar-ggproto

Reference

Data structures for missing data

Create special missing values

Visualisation

Numerical Summaries

Handy helpers

Add columns

Replacing values with and to NA

Imputation helpers

Package title details

Cast Shadows

Misc helpers

Data Sources

Little’s MCAR test

ggplot2 extensions