# {rtables} Advanced Usage

#### Gabriel Becker

#### 2025-04-10

## 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)

## 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

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

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

## 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")

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

    build_table(lyt1, d1)

    ##        A: Drug X
    ## ————————————————
    ## Mean     33.77

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

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

### 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

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