Data Structures, Summaries, and Visualisations for Missing Data
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 t...
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 there are any or all 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
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
Impute numeric values below a range for graphical exploration
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 a factor value into a vector with missing values
Impute a fixed value into a vector with missing values
Impute the mean value into a vector with missing values
Impute the median value into a vector with missing values
Impute the mode value into a vector with missing values
Impute zero 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?
Little's missing completely at random (MCAR) test
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 varia...
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-ggproto
naniar
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
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 NA value with provided value
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 mee...
Scoped variants of impute_mean
Scoped variants of impute_median
Set a proportion or number of missing values
Create new levels of missing
Reshape shadow data into a long format
Shift missing values to facilitate missing data exploration/visualisat...
stat_miss_point
Unbind (remove) shadow from data, and vice versa
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?
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 (2023) <doi:10.18637/jss.v105.i07>.
Useful links