# Data Structures, Summaries, and Visualisations for Missing Data

## Documentation for package ‘naniar’ version 0.4.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

A B C G I L M N O P R S T U W misc

naniar-package | 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-is-miss-complete | Identify if all values are missing or complete
all_complete | Identify if all values are missing or complete
all_miss | Identify if all values are missing or complete
all_na | Identify if all values are missing or complete
all_row_complete | Helper function to determine whether all rows are complete
all_row_miss | Helper function to determine whether all rows are missing
any-na | Identify if there are any missing or complete values
any_complete | Identify if there are any missing or complete values
any_miss | Identify if there are any missing or complete values
any_na | Identify if there are any 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.data.frame | Create shadow data
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 | Percentage of cases that contain a missing or complete values.
complete_case_prop | Proportion of cases that contain a missing or complete values.
complete_var_pct | Percentage 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
group_by_fun | Group By Helper

## -- I --

impute_below | Impute data with values shifted 10% below range.
impute_below_all | Impute data with values shifted 10% below range.
impute_below_at | Scoped variants of 'impute_below'
impute_below_if | Scoped variants of 'impute_below'
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'
is_nabular | Test if input is or are shadow variables
is_shade | Detect if this is a shade
is_shadow | Test if input is or are shadow variables

## -- 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
label_shadow | Label shadow values as missing or not missing

## -- M --

miss-complete-case-pct | Percentage of cases that contain a missing or complete values.
miss-complete-case-prop | Proportion of cases that contain a missing or complete values.
miss-complete-var-pct | Percentage of variables containing missings or complete values
miss-complete-var-prop | Proportion of variables containing missings or complete values
miss_case_pct | Percentage of cases that contain a missing or complete values.
miss_case_prop | Proportion of cases that contain a missing 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_pct | Percentage 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
new_nabular | Create a new nabular format
new_shade | Create a new shade factor
new_shadow | Create a new shadow
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
plotly_helpers | Plotly helpers (Convert a geom to a "basic" geom.)
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
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'
shade | Create new levels of missing
shadow_expand_relevel | Expand and relevel a shadow column with a new suffix
shadow_long | Reshape shadow data into a long format
shadow_shift | Shift missing values to facilitate missing data exploration/visualisation
shadow_shift.numeric | Shift (impute) numeric values for graphical exploration
StatMissPoint | naniar-ggproto
stat_miss_point | stat_miss_point

## -- T --

test_if_dataframe | Test if input is a data.frame
test_if_missing | Test if the input is Missing
test_if_null | Test if the input is NULL
test_if_shadow | Test if input is a shadow
to_basic.GeomMissPoint | Plotly helpers (Convert a geom to a "basic" geom.)

## -- 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
update_shadow | Expand all shadow levels

## -- W --

what_levels | check the levels of many things
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