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Custom Split Functions

Source: R/utils_split_funs.R
utils_split_funs.Rd

[Stable]

Collection of useful functions that are expanding on the core list of functions provided by rtables. See rtables::custom_split_funs and rtables::make_split_fun() for more information on how to make a custom split function. All these functions work with split_rows_by() argument split_fun to modify the way the split happens. For other split functions, consider consulting rtables::split_funcs.

Usage 

ref_group_position(position = "first")

level_order(order)

Arguments

position

(string or integer)
should it be "first" or "last" or in a specific position?

order

(character or integer)
vector of ordering indexes for the split facets.

Value

  • ref_group_position returns an utility function that puts the reference group as first, last or at a certain position and needs to be assigned to split_fun.

  • level_order returns an utility function that changes the original levels' order, depending on input order and split levels.

Functions

  • ref_group_position(): split function to place reference group facet at a specific position during post-processing stage.

  • level_order(): split function to change level order based on a integer vector or a character vector that represent the split variable's factor levels.

See also

rtables::make_split_fun()

Examples 

library(dplyr)

dat <- data.frame(
  x = factor(letters[1:5], levels = letters[5:1]),
  y = 1:5
)

# With rtables layout functions
basic_table() %>%
  split_cols_by("x", ref_group = "c", split_fun = ref_group_position("last")) %>%
  analyze("y") %>%
  build_table(dat)
#>         e      d      b      a      c  
#> ———————————————————————————————————————
#> Mean   5.00   4.00   2.00   1.00   3.00

# With tern layout funcitons
adtte_f <- tern_ex_adtte %>%
  filter(PARAMCD == "OS") %>%
  mutate(
    AVAL = day2month(AVAL),
    is_event = CNSR == 0
  )

basic_table() %>%
  split_cols_by(var = "ARMCD", ref_group = "ARM B", split_fun = ref_group_position("first")) %>%
  add_colcounts() %>%
  surv_time(
    vars = "AVAL",
    var_labels = "Survival Time (Months)",
    is_event = "is_event",
  ) %>%
  build_table(df = adtte_f)
#>                             ARM B          ARM A          ARM C    
#>                             (N=73)         (N=69)         (N=58)   
#> ———————————————————————————————————————————————————————————————————
#> Survival Time (Months)                                             
#>   Median                     23.9           32.0           20.8    
#>     95% CI               (18.3, 32.9)   (22.5, 49.3)   (12.9, 26.0)
#>   25% and 75%-ile         9.8, 42.0      17.4, 65.3     7.3, 37.1  
#>   Range                  0.1 to 154.1   0.3 to 155.5   0.6 to 80.7 

basic_table() %>%
  split_cols_by(var = "ARMCD", ref_group = "ARM B", split_fun = ref_group_position(2)) %>%
  add_colcounts() %>%
  surv_time(
    vars = "AVAL",
    var_labels = "Survival Time (Months)",
    is_event = "is_event",
  ) %>%
  build_table(df = adtte_f)
#>                             ARM A          ARM B          ARM C    
#>                             (N=69)         (N=73)         (N=58)   
#> ———————————————————————————————————————————————————————————————————
#> Survival Time (Months)                                             
#>   Median                     32.0           23.9           20.8    
#>     95% CI               (22.5, 49.3)   (18.3, 32.9)   (12.9, 26.0)
#>   25% and 75%-ile         17.4, 65.3     9.8, 42.0      7.3, 37.1  
#>   Range                  0.3 to 155.5   0.1 to 154.1   0.6 to 80.7 

# level_order --------
# Even if default would bring ref_group first, the original order puts it last
basic_table() %>%
  split_cols_by("Species", split_fun = level_order(c(1, 3, 2))) %>%
  analyze("Sepal.Length") %>%
  build_table(iris)
#>        setosa   virginica   versicolor
#> ——————————————————————————————————————
#> Mean    5.01      6.59         5.94   

# character vector
new_order <- level_order(levels(iris$Species)[c(1, 3, 2)])
basic_table() %>%
  split_cols_by("Species", ref_group = "virginica", split_fun = new_order) %>%
  analyze("Sepal.Length") %>%
  build_table(iris)
#>        setosa   virginica   versicolor
#> ——————————————————————————————————————
#> Mean    5.01      6.59         5.94