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{rtables} Advanced Usage

Gabriel Becker

2024-09-20

NOTE

This vignette is currently under development. Any code or prose which appears in a version of this vignette on the main branch of the repository will work/be correct, but they likely are not in their final form.

Initialization

library(rtables)

advanced_usage.R

Control splitting with provided function (limited customization)

rtables provides an array of functions to control the splitting logic without creating an entirely new split functions. By default split_*_by facets data based on categorical variable.

d1 <- subset(ex_adsl, AGE < 25)
d1$AGE <- as.factor(d1$AGE)
lyt1 <- basic_table() %>%
  split_cols_by("AGE") %>%
  analyze("SEX")

build_table(lyt1, d1)
##                    20   21   23   24
## ————————————————————————————————————
## F                  0    2    4    5 
## M                  1    1    2    3 
## U                  0    0    0    0 
## UNDIFFERENTIATED   0    0    0    0

advanced_usage.R

For continuous variables, the split_*_by_cutfun can be leveraged to create categories and the corresponding faceting, when the break points are dependent from the data.

sd_cutfun <- function(x) {
  cutpoints <- c(
    min(x),
    mean(x) - sd(x),
    mean(x) + sd(x),
    max(x)
  )

  names(cutpoints) <- c("", "Low", "Medium", "High")
  cutpoints
}

lyt1 <- basic_table() %>%
  split_cols_by_cutfun("AGE", cutfun = sd_cutfun) %>%
  analyze("SEX")

build_table(lyt1, ex_adsl)
##                    Low   Medium   High
## ——————————————————————————————————————
## F                  36     165      21 
## M                  21     115      30 
## U                   1      8       0  
## UNDIFFERENTIATED    0      1       2

advanced_usage.R

Alternatively, split_*_by_cuts can be used when breakpoints are predefined and split_*_by_quartiles when the data should be faceted by quantile.

lyt1 <- basic_table() %>%
  split_cols_by_cuts(
    "AGE",
    cuts = c(0, 30, 60, 100),
    cutlabels = c("0-30 y.o.", "30-60 y.o.", "60-100 y.o.")
  ) %>%
  analyze("SEX")

build_table(lyt1, ex_adsl)
##                    0-30 y.o.   30-60 y.o.   60-100 y.o.
## ———————————————————————————————————————————————————————
## F                     71          150            1     
## M                     48          116            2     
## U                      2           7             0     
## UNDIFFERENTIATED       1           2             0

advanced_usage.R

Custom Split Functions

Adding an Overall Column Only When The Split Would Already Define 2+ Facets

Our custom split functions can do anything, including conditionally applying one or more other existing custom split functions.

Here we define a function constructor which accepts the variable name we want to check, and then return a custom split function that has the behavior you want using functions provided by rtables for both cases:

picky_splitter <- function(var) {
  function(df, spl, vals, labels, trim) {
    orig_vals <- vals
    if (is.null(vals)) {
      vec <- df[[var]]
      vals <- if (is.factor(vec)) levels(vec) else unique(vec)
    }
    if (length(vals) == 1) {
      do_base_split(spl = spl, df = df, vals = vals, labels = labels, trim = trim)
    } else {
      add_overall_level(
        "Overall",
        label = "All Obs", first = FALSE
      )(df = df, spl = spl, vals = orig_vals, trim = trim)
    }
  }
}


d1 <- subset(ex_adsl, ARM == "A: Drug X")
d1$ARM <- factor(d1$ARM)

lyt1 <- basic_table() %>%
  split_cols_by("ARM", split_fun = picky_splitter("ARM")) %>%
  analyze("AGE")

advanced_usage.R

This gives us the desired behavior in both the one column corner case:

build_table(lyt1, d1)
##        A: Drug X
## ————————————————
## Mean     33.77

advanced_usage.R

and the standard multi-column case:

build_table(lyt1, ex_adsl)
##        A: Drug X   B: Placebo   C: Combination   All Obs
## ————————————————————————————————————————————————————————
## Mean     33.77       35.43          35.43         34.88

advanced_usage.R

Notice we use add_overall_level which is itself a function constructor, and then immediately call the constructed function in the more-than-one-columns case.

Leveraging .spl_context

What Is .spl_context?

.spl_context (see ?spl_context) is a mechanism by which the rtables tabulation machinery gives custom split, analysis or content (row-group summary) functions information about the overarching facet-structure the splits or cells they generate will reside in.

In particular .spl_context ensures that your functions know (and thus do computations based on) the following types of information:

Different Formats For Different Values Within A Row-Split

dta_test <- data.frame(
  USUBJID = rep(1:6, each = 3),
  PARAMCD = rep("lab", 6 * 3),
  AVISIT = rep(paste0("V", 1:3), 6),
  ARM = rep(LETTERS[1:3], rep(6, 3)),
  AVAL = c(9:1, rep(NA, 9)),
  CHG = c(1:9, rep(NA, 9))
)

my_afun <- function(x, .spl_context) {
  n <- sum(!is.na(x))
  meanval <- mean(x, na.rm = TRUE)
  sdval <- sd(x, na.rm = TRUE)

  ## get the split value of the most recent parent
  ## (row) split above this analyze
  val <- .spl_context[nrow(.spl_context), "value"]
  ## do a silly thing to decide the different format precisiosn
  ## your real logic would go here
  valnum <- min(2L, as.integer(gsub("[^[:digit:]]*", "", val)))
  fstringpt <- paste0("xx.", strrep("x", valnum))
  fmt_mnsd <- sprintf("%s (%s)", fstringpt, fstringpt)
  in_rows(
    n = n,
    "Mean, SD" = c(meanval, sdval),
    .formats = c(n = "xx", "Mean, SD" = fmt_mnsd)
  )
}

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("AVISIT") %>%
  split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
  analyze_colvars(my_afun)

build_table(lyt, dta_test)
##                          A                         B                 C     
##                 AVAL           CHG         AVAL         CHG      AVAL   CHG
## ———————————————————————————————————————————————————————————————————————————
## V1                                                                         
##   n               2             2            1           1        0      0 
##   Mean, SD    7.5 (2.1)     2.5 (2.1)    3.0 (NA)    7.0 (NA)     NA    NA 
## V2                                                                         
##   n               2             2            1           1        0      0 
##   Mean, SD   6.50 (2.12)   3.50 (2.12)   2.00 (NA)   8.00 (NA)    NA    NA 
## V3                                                                         
##   n               2             2            1           1        0      0 
##   Mean, SD   5.50 (2.12)   4.50 (2.12)   1.00 (NA)   9.00 (NA)    NA    NA

advanced_usage.R

Simulating ‘Baseline Comparison’ In Row Space

my_afun <- function(x, .var, .spl_context) {
  n <- sum(!is.na(x))
  meanval <- mean(x, na.rm = TRUE)
  sdval <- sd(x, na.rm = TRUE)

  ## get the split value of the most recent parent
  ## (row) split above this analyze
  val <- .spl_context[nrow(.spl_context), "value"]
  ## we show it if its not a CHG within V1
  show_it <- val != "V1" || .var != "CHG"
  ## do a silly thing to decide the different format precisiosn
  ## your real logic would go here
  valnum <- min(2L, as.integer(gsub("[^[:digit:]]*", "", val)))
  fstringpt <- paste0("xx.", strrep("x", valnum))
  fmt_mnsd <- if (show_it) sprintf("%s (%s)", fstringpt, fstringpt) else "xx"
  in_rows(
    n = if (show_it) n, ## NULL otherwise
    "Mean, SD" = if (show_it) c(meanval, sdval), ## NULL otherwise
    .formats = c(n = "xx", "Mean, SD" = fmt_mnsd)
  )
}

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("AVISIT") %>%
  split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
  analyze_colvars(my_afun)

build_table(lyt, dta_test)
##                          A                         B                 C     
##                 AVAL           CHG         AVAL         CHG      AVAL   CHG
## ———————————————————————————————————————————————————————————————————————————
## V1                                                                         
##   n               2                          1                    0        
##   Mean, SD    7.5 (2.1)                  3.0 (NA)                 NA       
## V2                                                                         
##   n               2             2            1           1        0      0 
##   Mean, SD   6.50 (2.12)   3.50 (2.12)   2.00 (NA)   8.00 (NA)    NA    NA 
## V3                                                                         
##   n               2             2            1           1        0      0 
##   Mean, SD   5.50 (2.12)   4.50 (2.12)   1.00 (NA)   9.00 (NA)    NA    NA

advanced_usage.R

We can further simulate the formal modeling of reference row(s) using the extra_args machinery

my_afun <- function(x, .var, ref_rowgroup, .spl_context) {
  n <- sum(!is.na(x))
  meanval <- mean(x, na.rm = TRUE)
  sdval <- sd(x, na.rm = TRUE)

  ## get the split value of the most recent parent
  ## (row) split above this analyze
  val <- .spl_context[nrow(.spl_context), "value"]
  ## we show it if its not a CHG within V1
  show_it <- val != ref_rowgroup || .var != "CHG"
  fmt_mnsd <- if (show_it) "xx.x (xx.x)" else "xx"
  in_rows(
    n = if (show_it) n, ## NULL otherwise
    "Mean, SD" = if (show_it) c(meanval, sdval), ## NULL otherwise
    .formats = c(n = "xx", "Mean, SD" = fmt_mnsd)
  )
}

lyt2 <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("AVISIT") %>%
  split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
  analyze_colvars(my_afun, extra_args = list(ref_rowgroup = "V1"))

build_table(lyt2, dta_test)
##                        A                      B                C     
##                AVAL         CHG        AVAL       CHG      AVAL   CHG
## —————————————————————————————————————————————————————————————————————
## V1                                                                   
##   n              2                      1                   0        
##   Mean, SD   7.5 (2.1)               3.0 (NA)               NA       
## V2                                                                   
##   n              2           2          1          1        0      0 
##   Mean, SD   6.5 (2.1)   3.5 (2.1)   2.0 (NA)   8.0 (NA)    NA    NA 
## V3                                                                   
##   n              2           2          1          1        0      0 
##   Mean, SD   5.5 (2.1)   4.5 (2.1)   1.0 (NA)   9.0 (NA)    NA    NA

advanced_usage.R

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They may not be fully stable and should be used with caution. We make no claims about them.
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