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Add rows according to levels of a variable

Source: R/colby_constructors.R
split_rows_by.Rd

Add rows according to levels of a variable

Usage

split_rows_by(
  lyt,
  var,
  labels_var = var,
  split_label = var,
  split_fun = NULL,
  format = NULL,
  na_str = NA_character_,
  nested = TRUE,
  child_labels = c("default", "visible", "hidden"),
  label_pos = "hidden",
  indent_mod = 0L,
  page_by = FALSE,
  page_prefix = split_label,
  section_div = NA_character_
)

Arguments

lyt

(PreDataTableLayouts)
layout object pre-data used for tabulation.

var

(string)
variable name.

labels_var

(string)
name of variable containing labels to be displayed for the values of var.

split_label

(string)
label to be associated with the table generated by the split. Not to be confused with labels assigned to each child (which are based on the data and type of split during tabulation).

split_fun

(function or NULL)
custom splitting function. See custom_split_funs.

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.

child_labels

(string)
the display behavior for the labels (i.e. label rows) of the children of this split. Accepts "default", "visible", and "hidden". Defaults to "default" which flags the label row as visible only if the child has 0 content rows.

label_pos

(string)
location where the variable label should be displayed. Accepts "hidden" (default for non-analyze row splits), "visible", "topleft", and "default" (for analyze splits only). For analyze calls, "default" indicates that the variable should be visible if and only if multiple variables are analyzed at the same level of nesting.

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.

page_by

(flag)
whether pagination should be forced between different children resulting from this split. An error will occur if the selected split does not contain at least one value that is not NA.

page_prefix

(string)
prefix to be appended with the split value when forcing pagination between the children of a split/table.

section_div

(string)
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().

Note

If var is a factor with empty unobserved levels and labels_var is specified, it must also be a factor with the same number of levels as var. Currently the error that occurs when this is not the case is not very informative, but that will change in the future.

Custom Splitting Function Details

User-defined custom split functions can perform any type of computation on the incoming data provided that they meet the requirements for generating "splits" of the incoming data based on the split object.

Split functions are functions that accept:

df

a data.frame of incoming data to be split.

spl

a Split object. This is largely an internal detail custom functions will not need to worry about, but obj_name(spl), for example, will give the name of the split as it will appear in paths in the resulting table.

vals

any pre-calculated values. If given non-NULL values, the values returned should match these. Should be NULL in most cases and can usually be ignored.

labels

any pre-calculated value labels. Same as above for values.

trim

if TRUE, resulting splits that are empty are removed.

(optional) .spl_context

a data.frame describing previously performed splits which collectively arrived at df.

The function must then output a named list with the following elements:

values

the vector of all values corresponding to the splits of df.

datasplit

a list of data.frames representing the groupings of the actual observations from df.

labels

a character vector giving a string label for each value listed in the values element above.

(optional) extras

if present, extra arguments are to be passed to summary and analysis functions whenever they are executed on the corresponding element of datasplit or a subset thereof.

One way to generate custom splitting functions is to wrap existing split functions and modify either the incoming data before they are called or their outputs.

Author

Gabriel Becker

Examples

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("RACE", split_fun = drop_split_levels) %>%
  analyze("AGE", mean, var_labels = "Age", format = "xx.xx")

tbl <- build_table(lyt, DM)
tbl
#>                             A: Drug X   B: Placebo   C: Combination
#> ———————————————————————————————————————————————————————————————————
#> ASIAN                                                              
#>   mean                        34.20       32.68          34.63     
#> BLACK OR AFRICAN AMERICAN                                          
#>   mean                        34.68       31.71          34.00     
#> WHITE                                                              
#>   mean                        39.36       36.93          35.11     

lyt2 <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("RACE") %>%
  analyze("AGE", mean, var_labels = "Age", format = "xx.xx")

tbl2 <- build_table(lyt2, DM)
tbl2
#>                                             A: Drug X   B: Placebo   C: Combination
#> ———————————————————————————————————————————————————————————————————————————————————
#> ASIAN                                                                              
#>   mean                                        34.20       32.68          34.63     
#> BLACK OR AFRICAN AMERICAN                                                          
#>   mean                                        34.68       31.71          34.00     
#> WHITE                                                                              
#>   mean                                        39.36       36.93          35.11     
#> AMERICAN INDIAN OR ALASKA NATIVE                                                   
#>   mean                                         NA           NA             NA      
#> MULTIPLE                                                                           
#>   mean                                         NA           NA             NA      
#> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER                                          
#>   mean                                         NA           NA             NA      
#> OTHER                                                                              
#>   mean                                         NA           NA             NA      
#> UNKNOWN                                                                            
#>   mean                                         NA           NA             NA      

lyt3 <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_cols_by("SEX") %>%
  summarize_row_groups(label_fstr = "Overall (N)") %>%
  split_rows_by("RACE",
    split_label = "Ethnicity", labels_var = "ethn_lab",
    split_fun = drop_split_levels
  ) %>%
  summarize_row_groups("RACE", label_fstr = "%s (n)") %>%
  analyze("AGE", var_labels = "Age", afun = mean, format = "xx.xx")

lyt3
#> A Pre-data Table Layout
#> 
#> Column-Split Structure:
#> ARM (lvls) -> SEX (lvls) 
#> 
#> Row-Split Structure:
#> RACE (lvls) -> AGE (** analysis **) 
#> 

library(dplyr)

DM2 <- DM %>%
  filter(SEX %in% c("M", "F")) %>%
  mutate(
    SEX = droplevels(SEX),
    gender_lab = c(
      "F" = "Female", "M" = "Male",
      "U" = "Unknown",
      "UNDIFFERENTIATED" = "Undifferentiated"
    )[SEX],
    ethn_lab = c(
      "ASIAN" = "Asian",
      "BLACK OR AFRICAN AMERICAN" = "Black or African American",
      "WHITE" = "White",
      "AMERICAN INDIAN OR ALASKA NATIVE" = "American Indian or Alaska Native",
      "MULTIPLE" = "Multiple",
      "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER" =
        "Native Hawaiian or Other Pacific Islander",
      "OTHER" = "Other", "UNKNOWN" = "Unknown"
    )[RACE]
  )

tbl3 <- build_table(lyt3, DM2)
tbl3
#>                                           A: Drug X                  B: Placebo                C: Combination      
#>                                        F             M             F             M             F             M     
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Overall (N)                       70 (100.0%)   51 (100.0%)   56 (100.0%)   50 (100.0%)   61 (100.0%)   68 (100.0%)
#>   Asian (n)                       44 (62.9%)    35 (68.6%)    37 (66.1%)    31 (62.0%)    40 (65.6%)    44 (64.7%) 
#>     mean                             33.55         35.03         34.00         31.10         34.90         34.39   
#>   Black or African American (n)   18 (25.7%)    10 (19.6%)    12 (21.4%)    12 (24.0%)    13 (21.3%)    14 (20.6%) 
#>     mean                             33.17         37.40         30.58         32.83         33.85         34.14   
#>   White (n)                        8 (11.4%)     6 (11.8%)     7 (12.5%)     7 (14.0%)     8 (13.1%)    10 (14.7%) 
#>     mean                             35.88         44.00         38.57         35.29         36.50         34.00