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
all-is-miss-complete    Identify if all values are missing or complete
any-na                  Identify if there are any 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
group_by_fun            Group By Helper
impute_below            Impute data with values shifted 10 percent
                        below range.
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_mean             Impute the mean value into a vector with
                        missing values
impute_median           Impute the median value 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?
label_shadow            Label shadow values as missing or not missing
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
new_shade               Create a new shade factor
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
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_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_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'
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
stat_miss_point         stat_miss_point
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
unbinders               Unbind (remove) shadow from data, and vice
                        versa
update_shadow           Expand all shadow levels
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?
