GeomMissPoint           naniar-ggproto
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
any-all-na-complete     Identify if there are any or all missing or
                        complete values
any_row_miss            Helper function to determine whether there are
                        any missings
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
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
gather_shadow           Long form representation of a shadow matrix
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
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
is_shade                Detect if this is a shade
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?
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_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?
n-var-case-complete     The number of variables with complete values
n-var-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
nabular                 Convert data into nabular form by binding shade
                        to it
naniar                  naniar
oceanbuoys              West Pacific Tropical Atmosphere Ocean Data,
                        1993 & 1997.
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_miss                Return the percent of missing 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_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
recode_shadow           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.
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
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
stat_miss_point         stat_miss_point
unbinders               Unbind (remove) shadow from data, and vice
                        versa
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?
