naniar

Add a column describing presence of any missing values

Add a column describing if there are any missings in the dataset

Add a column describing whether there is a shadow

Add a column that tells us which "missingness cluster" a row belongs to

Add column containing number of missing data values

Add column containing proportion of missing data values

Add a shadow column to dataframe

Add a shadow shifted column to a dataset

Add a counter variable for a span of dataframe

Identify if all values are missing or complete

Identify if there are any missing or complete values

Helper function to determine whether there are any missings

Create shadows

Convert data into shadow format for doing an upset plot

Bind a shadow dataframe to original data

Add a shadow column to a dataset

Add a shadow and a shadow_shift column to a dataset

Add a shadow column and a shadow shifted column to a dataset

Common number values for NA

Common string values for NA

Key drawing functions

Long form representation of a shadow matrix

geom_miss_point

Plot the number of missings per case (row)

Plot of cumulative sum of missing for cases

Plot the number of missings for each variable, broken down by a factor

Plot the number of missings in a given repeating span

Plot the pattern of missingness using an upset plot.

Plot the number of missings for each variable

Plot of cumulative sum of missing value for each variable

Plot which variables contain a missing value

Group By Helper

Impute data with values shifted 10 percent below range.

Impute data with values shifted 10 percent below range.

Scoped variants of impute_below

Scoped variants of impute_below

Impute the mean value into a vector with missing values

Impute the median value into a vector with missing values

Detect if this is a shade

Label a missing from one column

label_miss_2d

Is there a missing value in the row of a dataframe?

Label shadow values as missing or not missing

Proportion of variables containing missings or complete values

Summarise the missingness in each case

Summarise the missingness in each case

Tabulate missings in cases.

Proportions of missings in data, variables, and cases.

Search and present different kinds of missing values

Collate summary measures from naniar into one tibble

Cumulative sum of the number of missings in each variable

Find the number of missing and complete values in a single run

Summarise the number of missings for a given repeating span on a variable

Summarise the missingness in each variable

Tabulate the missings in the variables

Which variables contain missing values?

The number of variables with complete values

The number of variables or cases with missing values

Return the number of complete values

Return a vector of the number of complete values in each row

Return the number of missing values

Return a vector of the number of missing values in each row

Convert data into nabular form by binding shade to it

naniar

naniar-ggproto

Create a new shade factor

West Pacific Tropical Atmosphere Ocean Data, 1993 & 1997.

Percentage of cases that contain a missing or complete values.

Percentage of variables containing missings or complete values

Return the percent of complete values

Return the percent of missing values

Pedestrian count information around Melbourne for 2016

Plotly helpers (Convert a geom to a "basic" geom.)

Proportion of cases that contain a missing or complete values.

Proportion of variables containing missings or complete values

Return the proportion of complete values

Return a vector of the proportion of missing values in each row

Return the proportion of missing values

Return a vector of the proportion of missing values in each row

Add special missing values to the shadow matrix

Objects exported from other packages

Replace values with missings

Replace values with missings

Replace all values with NA where a certain condition is met

Replace specified variables with NA where a certain condition is met

Replace values with NA based on some condition, for variables that meet some predicate

The Behavioral Risk Factor Surveillance System (BRFSS) Survey Data, 2009.

Scoped variants of impute_mean

Scoped variants of impute_median

Create new levels of missing

Expand and relevel a shadow column with a new suffix

Reshape shadow data into a long format

Shift missing values to facilitate missing data exploration/visualisation

Shift (impute) numeric values for graphical exploration

stat_miss_point

Test if input is a data.frame

Test if the input is Missing

Test if the input is NULL

Unbind (remove) shadow from data, and vice versa

Expand all shadow levels

check the levels of many things

Split a call into two components with a useful verb name

Which rows and cols contain missings?

Which variables are shades?

Which elements contain missings?