Generate rows analyzing different variables across columns

Usage

analyze_colvars(
  lyt,
  afun,
  parent_name = get_acolvar_name(lyt),
  format = NULL,
  na_str = NA_character_,
  nested = TRUE,
  at_sibling = NULL,
  extra_args = list(),
  indent_mod = 0L,
  inclNAs = FALSE
)

Arguments

lyt: (PreDataTableLayouts)
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. This functions accepts the same parameters as analyze() like afun and format. For further information see additional_fun_params.
parent_name: (character(1))
Name to assign to the table corresponding to the split or group of sibling analyses, for split_rows_by* and analyze* when analyzing more than one variable, respectively. Ignored when analyzing a single variable.
format: (string, function, or list)
format associated with this split. Formats can be declared via strings ("xx.x") or function. In cases such as analyze calls, they can be character vectors or lists of functions. See formatters::list_valid_format_labels() for a list of all available format strings.
na_str: (string)
string that should be displayed when the value of x is missing. Defaults to "NA".
nested: (logical)
whether this layout instruction should 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.
at_sibling: (character(1) or NULL)
If non-null, a preceding split or analyze to anchor this instruction to as a direct sibling. Cannot select an instruction that is downstream of a point where a previously used anchor (See Nesting Anchor Resolution for details).
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: (logical)
whether NA observations in the var variable(s) should be included when performing the analysis. Defaults to FALSE.

Value

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

Nesting Anchor Resolution

When nested is TRUE, at_sibling allows you to set a nesting anchor that your new split_rows_by* or analyze* directive should be placed as a sibling to. The lookup for this anchor occurs only in the currently active top-level nesting stack, meaning the directives that have occurred since the last split or analysis with nested == FALSE.

Furthermore, resolution occurs against the first element of each arm of a branching point caused by any previous uses of at_sibling but only descends into the last arm.

So for example if our previous layout was generated via:

lyt <- basic_table() |>
  split_rows_by("SEX") |>
  analyze("AGE") |>
  split_rows_by("BMRKR2", nested = FALSE) |>
  split_rows_by("RACE") |>
  analyze("AGE") |>
  split_rows_by("SEX", at_sibling = "RACE") |>
  analyze("BMRKR1")

The eligible anchor points would be "BMRKR2", "RACE", "SEX" and "BMRKR1". "AGE" is masked by the branching caused by anchoring our SEX split on RACE.

Finally, while at_sibling does support de-duplication of "<name>[i]" anchors, it does so within the set of available anchors, which can be counter-intuitive. It is strongly suggested that the parent_name and table_names argument(s) of split_rows_by* and analyze be used to prevent the need for this. at_sibling will resolve to table names overridden in this manner.

Author

Gabriel Becker

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
#> A Pre-data Table Layout
#> 
#> Column-Split Structure:
#> ARM (lvls) -> multivars (vars)
#> 
#> Row-Split Structure:
#> RACE (lvls) -> ac_value_pctdiff (** col-var **)
#> 
#> '->' indicates nesting, vertical stacks of '|' indicate anchoring/siblings.
#> '(<type>)' indicates split type, while '(** <type> **)' indicates an analyze instruction.
#> 

tbl <- build_table(lyt, ANL)
tbl
#>                                    A: Drug X                B: Placebo              C: Combination     
#>                               value       pctdiff       value       pctdiff       value       pctdiff  
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————
#> ASIAN                       79 (65.3%)   79 (65.3%)   68 (64.2%)   68 (64.2%)   84 (65.1%)   84 (65.1%)
#>                                0.0           32          0.2           28          0.1           42    
#> BLACK OR AFRICAN AMERICAN   28 (23.1%)   28 (23.1%)   24 (22.6%)   24 (22.6%)   27 (20.9%)   27 (20.9%)
#>                                -0.0          15          0.1           9           0.1           15    
#> WHITE                       14 (11.6%)   14 (11.6%)   14 (13.2%)   14 (13.2%)   18 (14.0%)   18 (14.0%)
#>                                -0.2          10          0.3           7           -0.3          12    

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
#>                                    A: Drug X                  B: Placebo               C: Combination     
#>                             Measurement    Pct Diff    Measurement    Pct Diff    Measurement    Pct Diff 
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————————
#> ASIAN                       79 (65.3%)    79 (65.3%)   68 (64.2%)    68 (64.2%)   84 (65.1%)    84 (65.1%)
#>   mean                         0.04          0.45         0.19          0.44         0.14          0.51   
#> BLACK OR AFRICAN AMERICAN   28 (23.1%)    28 (23.1%)   24 (22.6%)    24 (22.6%)   27 (20.9%)    27 (20.9%)
#>   mean                         -0.04         0.53         0.13          0.46         0.06          0.57   
#> WHITE                       14 (11.6%)    14 (11.6%)   14 (13.2%)    14 (13.2%)   18 (14.0%)    18 (14.0%)
#>   mean                         -0.19         0.61         0.33          0.48         -0.27         0.55