afun/cfun)It is possible to add specific parameters to afun and cfun, in analyze
and summarize_row_groups respectively. These parameters grant access to
relevant information like the row split structure (see spl_context) and the
predefined baseline (.ref_group).
We list and describe here all the parameters that can be added to a custom analysis function:
column-wise N (column count) for the full column being tabulated within
overall N (all observation count, defined as sum of column counts) for the tabulation
row-wise N (row group count) for the group of observations being analyzed (i.e. with no column-based subsetting)
data.frame for observations in the row group being analyzed (i.e. with no column-based subsetting)
variable that is analyzed
data.frame or vector of subset corresponding to the
ref_group column including subsetting defined by row-splitting.
Optional and only required/meaningful if a ref_group column has been
defined
data.frame or vector of subset corresponding to the
ref_group column without subsetting defined by row-splitting. Optional
and only required/meaningful if a ref_group column has been defined
boolean indicates if calculation is done for cells within the reference column
data.frame, each row gives information about a
previous/'ancestor' split state. See spl_context
data.frame, i.e. the alt_count_df after
row splitting. It can be used with .all_col_exprs and .spl_context
information to retrieve current faceting, but for alt_count_df.
It can be an empty table if all the entries are filtered out.
data.frame, .alt_df_row but filtered by columns expression.
This data present the same faceting of main data df. This also filters
NAs out if related parameters are set to (e.g. inclNAs in analyze).
Similarly to .alt_df_row, it can be an empty table if all the entries
are filtered out.
list of expressions. Each of them represents a different column splitting.
vector of integers. Each of them represents the global
count for each column. It differs if alt_counts_df is used
(see build_table).