rtables (version 0.6.1)

analyze_colvars: Generate Rows Analyzing Different Variables Across Columns

Description

Generate Rows Analyzing Different Variables Across Columns

Usage

analyze_colvars(
  lyt,
  afun,
  format = NULL,
  nested = TRUE,
  extra_args = list(),
  indent_mod = 0L,
  inclNAs = FALSE
)

Value

A PreDataTableLayouts object suitable for passing to further layouting functions, and to build_table.

Arguments

lyt

layout object pre-data used for tabulation

afun

function or list. Function(s) to be used to calculate the values in each column. the list will be repped out as needed and matched by position with the columns during tabulation.

format

FormatSpec. Format associated with this split. Formats can be declared via strings ("xx.x") or function. In cases such as analyze calls, they can character vectors or lists of functions.

nested

boolean. Should this layout instruction be applied within the existing layout structure if possible (TRUE, the default) or as a new top-level element (`FALSE). Ignored if it would nest a split underneath analyses, which is not allowed.

extra_args

list. Extra arguments to be passed to the tabulation function. Element position in the list corresponds to the children of this split. Named elements in the child-specific lists are ignored if they do not match a formal argument of the tabulation function.

indent_mod

numeric. Modifier for the default indent position for the structure created by this function(subtable, content table, or row) and all of that structure's children. Defaults to 0, which corresponds to the unmodified default behavior.

inclNAs

boolean. Should observations with NA in the var variable(s) be included when performing this analysis. Defaults to FALSE

Author

Gabriel Becker

See Also

split_cols_by_multivar()

Examples

library(dplyr)
ANL <- DM %>% mutate(value = rnorm(n()), pctdiff = runif(n()))

## toy example where we take the mean of the first variable and the
## count of >.5 for the second.
colfuns <- list(function(x) rcell(mean(x), format = "xx.x"),
                function(x) rcell(sum(x > .5), format = "xx"))

lyt <- basic_table() %>%
    split_cols_by("ARM") %>%
    split_cols_by_multivar(c("value", "pctdiff")) %>%
    split_rows_by("RACE", split_label = "ethnicity",
                  split_fun = drop_split_levels) %>%
    summarize_row_groups() %>%
    analyze_colvars(afun = colfuns)
lyt

tbl <- build_table(lyt, ANL)
tbl

lyt2 <- basic_table() %>%
    split_cols_by("ARM") %>%
    split_cols_by_multivar(c("value", "pctdiff"),
                           varlabels = c("Measurement", "Pct Diff")) %>%
    split_rows_by("RACE", split_label = "ethnicity",
                  split_fun = drop_split_levels) %>%
    summarize_row_groups() %>%
    analyze_colvars(afun = mean, format = "xx.xx")

tbl2 <- build_table(lyt2, ANL)
tbl2