Data Structures, Summaries, and Visualisations for Missing Data

Documentation for package ‘naniar’ version 1.1.0

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naniar

-- A --

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
all_complete	Identify if there are any or all missing or complete values
all_miss	Identify if there are any or all missing or complete values
all_na	Identify if there are any or all missing or complete values
any-all-na-complete	Identify if there are any or all missing or complete values
any_complete	Identify if there are any or all missing or complete values
any_miss	Identify if there are any or all missing or complete values
any_na	Identify if there are any or all missing or complete values
any_row_miss	Helper function to determine whether there are any missings
any_shade	Detect if this is a shade
are_shade	Detect if this is a shade
as_shadow	Create shadows
as_shadow_upset	Convert data into shadow format for doing an upset plot

-- B --

bind_shadow

Bind a shadow dataframe to original data

-- C --

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
common_na_numbers	Common number values for NA
common_na_strings	Common string values for NA
complete_case_pct	Proportion of variables containing missings or complete values
complete_case_prop	Proportion of variables containing missings or complete values
complete_var_pct	Proportion of variables containing missings or complete values
complete_var_prop	Proportion of variables containing missings or complete values

-- G --

gather_shadow	Long form representation of a shadow matrix
GeomMissPoint	naniar-ggproto
geom_miss_point	geom_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

-- I --

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_factor.character	Impute a factor value into a vector with missing values
impute_factor.default	Impute a factor value into a vector with missing values
impute_factor.factor	Impute a factor value into a vector with missing values
impute_factor.shade	Impute a factor value into a vector with missing values
impute_fixed	Impute a fixed value into a vector with missing values
impute_fixed.default	Impute a fixed value into a vector with missing values
impute_mean	Impute the mean value into a vector with missing values
impute_mean.default	Impute the mean value into a vector with missing values
impute_mean.factor	Impute the mean value into a vector with missing values
impute_mean_all	Scoped variants of 'impute_mean'
impute_mean_at	Scoped variants of 'impute_mean'
impute_mean_if	Scoped variants of 'impute_mean'
impute_median	Impute the median value into a vector with missing values
impute_median.default	Impute the median value into a vector with missing values
impute_median.factor	Impute the median value into a vector with missing values
impute_median_all	Scoped variants of 'impute_median'
impute_median_at	Scoped variants of 'impute_median'
impute_median_if	Scoped variants of 'impute_median'
impute_mode	Impute the mode value into a vector with missing values
impute_mode.default	Impute the mode value into a vector with missing values
impute_mode.factor	Impute the mode value into a vector with missing values
impute_mode.integer	Impute the mode value into a vector with missing values
impute_zero	Impute zero into a vector with missing values
is_shade	Detect if this is a shade

-- L --

label_missings	Is there a missing value in the row of a dataframe?
label_miss_1d	Label a missing from one column
label_miss_2d	label_miss_2d

-- M --

mcar_test	Little's missing completely at random (MCAR) test
miss-pct-prop-defunct	Proportion of variables containing missings or complete values
miss_case_cumsum	Summarise the missingness in each case
miss_case_pct	Proportion of variables containing missings or complete values
miss_case_prop	Proportion of variables containing missings or complete values
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_pct	Proportion of variables containing missings or complete values
miss_var_prop	Proportion of variables containing missings or complete values
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?

-- N --

n-var-case-complete	The number of variables with complete values
n-var-case-miss	The number of variables or cases with missing values
nabular	Convert data into nabular form by binding shade to it
naniar	naniar
naniar-ggproto	naniar-ggproto
n_case_complete	The number of variables with complete values
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
n_var_complete	The number of variables with complete values
n_var_miss	The number of variables or cases with missing values

-- O --

oceanbuoys

West Pacific Tropical Atmosphere Ocean Data, 1993 & 1997.

-- P --

pct-miss-complete-case	Percentage of cases that contain a missing or complete values.
pct-miss-complete-var	Percentage of variables containing missings or complete values
pct_complete	Return the percent of complete values
pct_complete_case	Percentage of cases that contain a missing or complete values.
pct_complete_var	Percentage of variables containing missings or complete values
pct_miss	Return the percent of missing values
pct_miss_case	Percentage of cases that contain a missing or complete values.
pct_miss_var	Percentage of variables containing missings or complete values
pedestrian	Pedestrian count information around Melbourne for 2016
prop-miss-complete-case	Proportion of cases that contain a missing or complete values.
prop-miss-complete-var	Proportion of variables containing missings or complete values
prop_complete	Return the proportion of complete values
prop_complete_case	Proportion of cases that contain a missing or complete values.
prop_complete_row	Return a vector of the proportion of missing values in each row
prop_complete_var	Proportion of variables containing missings or complete values
prop_miss	Return the proportion of missing values
prop_miss_case	Proportion of cases that contain a missing or complete values.
prop_miss_row	Return a vector of the proportion of missing values in each row
prop_miss_var	Proportion of variables containing missings or complete values

-- R --

recode_shadow	Add special missing values to the shadow matrix
recode_shadow.data.frame	Add special missing values to the shadow matrix
recode_shadow.grouped_df	Add special missing values to the shadow matrix
replace_na_with	Replace NA value with provided value
replace_to_na	Replace values with missings
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
riskfactors	The Behavioral Risk Factor Surveillance System (BRFSS) Survey Data, 2009.

-- S --

scoped-impute_mean	Scoped variants of 'impute_mean'
scoped-impute_median	Scoped variants of 'impute_median'
set-prop-n-miss	Set a proportion or number of missing values
set_n_miss	Set a proportion or number of missing values
set_prop_miss	Set a proportion or number of missing values
shade	Create new levels of missing
shadow_long	Reshape shadow data into a long format
shadow_shift	Shift missing values to facilitate missing data exploration/visualisation
StatMissPoint	naniar-ggproto
stat_miss_point	stat_miss_point

-- U --

unbinders	Unbind (remove) shadow from data, and vice versa
unbind_data	Unbind (remove) shadow from data, and vice versa
unbind_shadow	Unbind (remove) shadow from data, and vice versa

-- W --

where	Split a call into two components with a useful verb name
where_na	Which rows and cols contain missings?
which_are_shade	Which variables are shades?
which_na	Which elements contain missings?

-- misc --

.where

Split a call into two components with a useful verb name