Generate Rows Analyzing Variables Across Columns

Usage

analyze(
  lyt,
  vars,
  afun = simple_analysis,
  var_labels = vars,
  table_names = vars,
  format = NULL,
  na_str = NA_character_,
  nested = TRUE,
  inclNAs = FALSE,
  extra_args = list(),
  show_labels = c("default", "visible", "hidden"),
  indent_mod = 0L,
  section_div = NA_character_
)

Arguments

lyt

layout object pre-data used for tabulation

vars

character vector. Multiple variable names.

afun

function. Analysis function, must take x or df as its first parameter. Can optionally take other parameters which will be populated by the tabulation framework. See Details in analyze.

var_labels

character. Variable labels for 1 or more variables

table_names

character. Names for the tables representing each atomic analysis. Defaults to var.

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.

na_str

character(1). String that should be displayed when the value of x is missing. Defaults to "NA".

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.

inclNAs

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

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.

show_labels

character(1). Should the variable labels for corresponding to the variable(s) in vars be visible in the resulting table.

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.

section_div

character(1). String which should be repeated as a section divider after each group defined by this split instruction, or NA_character_ (the default) for no section divider.

Value

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

Details

When non-NULL format is used to specify formats for all generated rows, and can be a character vector, a function, or a list of functions. It will be repped out to the number of rows once this is known during the tabulation process, but will be overridden by formats specified within rcell calls in afun.

The analysis function (afun) should take as its first parameter either x or df. Which of these the function accepts changes the behavior when tabulation is performed.

• If afun's first parameter is x, it will receive the corresponding subset vector of data from the relevant column (from var here) of the raw data being used to build the table.

• If afun's first parameter is df, it will receive the corresponding subset data.frame (i.e. all columns) of the raw data being tabulated

In addition to differentiation on the first argument, the analysis function can optionally accept a number of other parameters which, if and only if present in the formals will be passed to the function by the tabulation machinery. These are listed and described in additional_fun_params.

Note

None of the arguments described in the Details section can be overridden via extra_args or when calling make_afun. .N_col and .N_total can be overridden via the col_counts argument to build_table. Alternative values for the others must be calculated within afun based on a combination of extra arguments and the unmodified values provided by the tabulation framework.

Author

Gabriel Becker

Examples

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  analyze("AGE", afun = list_wrap_x(summary), format = "xx.xx")
lyt
#> A Pre-data Table Layout
#> 
#> Column-Split Structure:
#> ARM (lvls) 
#> 
#> Row-Split Structure:
#> AGE (** analysis **) 
#> 

tbl <- build_table(lyt, DM)
tbl
#>           A: Drug X   B: Placebo   C: Combination
#> —————————————————————————————————————————————————
#> Min.        20.00       21.00          22.00     
#> 1st Qu.     29.00       29.00          30.00     
#> Median      33.00       32.00          33.00     
#> Mean        34.91       33.02          34.57     
#> 3rd Qu.     39.00       37.00          38.00     
#> Max.        60.00       55.00          53.00     

lyt2 <- basic_table() %>%
  split_cols_by("Species") %>%
  analyze(head(names(iris), -1), afun = function(x) {
    list(
      "mean / sd" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"),
      "range" = rcell(diff(range(x)), format = "xx.xx")
    )
  })
lyt2
#> A Pre-data Table Layout
#> 
#> Column-Split Structure:
#> Species (lvls) 
#> 
#> Row-Split Structure:
#> Sepal.Length:Sepal.Width:Petal.Length:Petal.Width (** multivar analysis **) 
#> 

tbl2 <- build_table(lyt2, iris)
tbl2
#>                  setosa      versicolor     virginica 
#> ——————————————————————————————————————————————————————
#> Sepal.Length                                          
#>   mean / sd    5.01 (0.35)   5.94 (0.52)   6.59 (0.64)
#>   range           1.50          2.10          3.00    
#> Sepal.Width                                           
#>   mean / sd    3.43 (0.38)   2.77 (0.31)   2.97 (0.32)
#>   range           2.10          1.40          1.60    
#> Petal.Length                                          
#>   mean / sd    1.46 (0.17)   4.26 (0.47)   5.55 (0.55)
#>   range           0.90          2.10          2.40    
#> Petal.Width                                           
#>   mean / sd    0.25 (0.11)   1.33 (0.20)   2.03 (0.27)
#>   range           0.50          0.80          1.10