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rtables Advanced Usage

Gabriel Becker

2023-05-19

Source: vignettes/advanced_usage.Rmd
advanced_usage.Rmd

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

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