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Format Precedence and NA Handling

Wojciech Wójciak and Gabriel Becker

2024-09-23

Source: vignettes/format_precedence.Rmd
format_precedence.Rmd

Formats Precedence

Users of the rtables package can specify the format in which the numbers in the reporting tables are printed. Formatting functionality is provided by the formatters R package. See formatters::list_valid_format_labels() for a list of all available formats. The format can be specified by the user in a few different places. It may happen that, for a single table layout, the format is specified in more than one place. In such a case, the final format that will be applied depends on format precedence rules defined by rtables. In this vignette, we describe the basic rules of rtables format precedence.

The examples shown in this vignette utilize the example ADSL dataset, a demographic table that summarizes the variables content for different population subsets (encoded in the columns).

library(rtables)
ADSL <- ex_adsl

Note that all ex_* data which is currently attached to the rtables package is provided by the formatters package and was created using the publicly available random.cdisc.data R package.

Format Precedence and Inheritance Rules

The format in which numbers are printed can be specified by the user in a few different places. In the context of precedence, it is important which level of the split hierarchy formats are specified at. In general, there are two such levels: the cell level and the so-called parent table level. The concept of the cell and the parent table results from the way in which the rtables package stores resulting tables. It models the resulting tables as hierarchical, tree-like objects with the cells (as leaves) containing multiple values. Particularly noteworthy in this context is the fact that the actual table splitting occurs in a row-dominant way (even if column splitting is present in the layout). rtables provides user-end function table_structure() that prints the structure of a given table object.

For a simple illustration, consider the following example:

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX") %>%
  analyze(vars = "AGE", afun = mean)

adsl_analyzed <- build_table(lyt, ADSL)
adsl_analyzed
#                       A: Drug X          B: Placebo       C: Combination 
# —————————————————————————————————————————————————————————————————————————
# F                                                                        
#   mean             32.7594936708861   34.1168831168831   35.1969696969697
# M                                                                        
#   mean             35.5686274509804   37.4363636363636   35.3833333333333
# U                                                                        
#   mean             31.6666666666667          31               35.25      
# UNDIFFERENTIATED                                                         
#   mean                    28                 NA                 45
table_structure(adsl_analyzed)
# [TableTree] SEX
#  [TableTree] F
#   [ElementaryTable] AGE (1 x 3)
#  [TableTree] M
#   [ElementaryTable] AGE (1 x 3)
#  [TableTree] U
#   [ElementaryTable] AGE (1 x 3)
#  [TableTree] UNDIFFERENTIATED
#   [ElementaryTable] AGE (1 x 3)

In this table, there are 4 sub-tables under the SEX table. These are: F, M, U, and UNDIFFERENTIATED. Each of these sub-tables has one sub-table AGE. For example, for the first AGE sub-table, its parent table is F.

The concept of hierarchical, tree-like representations of resulting tables translates directly to format precedence and inheritance rules. As a general principle, the format being finally applied for the cell is the one that is the most specific, that is, the one which is the closest to the cell in a given path in the tree. Hence, the precedence-inheritance chain looks like the following:

parent_table -> parent_table -> ... -> parent_table -> cell

In such a chain, the outermost parent_table is the least specific place to specify the format, while the cell is the most specific one. In cases where the format is specified by the user in more than one place, the one which is most specific will be applied in the cell. If no specific format has been selected by the user for the split, then the default format will be applied. The default format is "xx" and it yields the same formatting as the as.character() function. In the following sections of this vignette, we will illustrate the format precedence rules with a few examples.

Standard Format

Below is a simple layout that does not explicitly set a format for the output of the analysis function. In such a case, the default format is applied.

lyt0 <- basic_table() %>%
  split_cols_by("ARM") %>%
  analyze(vars = "AGE", afun = mean)

build_table(lyt0, ADSL)
#           A: Drug X          B: Placebo       C: Combination 
# —————————————————————————————————————————————————————————————
# mean   33.7686567164179   35.4328358208955   35.4318181818182

Cell Format

The format of a cell can be explicitly specified via the rcell() or in_rows() functions. The former is essentially a collection of data objects while the latter is a collection of rcell() objects. As previously mentioned, this is the most specific place where the format can be specified by the user.

lyt1 <- basic_table() %>%
  split_cols_by("ARM") %>%
  analyze(vars = "AGE", afun = function(x) {
    rcell(mean(x), format = "xx.xx", label = "Mean")
  })

build_table(lyt1, ADSL)
#        A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————
# Mean     33.77       35.43          35.43
lyt1a <- basic_table() %>%
  split_cols_by("ARM") %>%
  analyze(vars = "AGE", afun = function(x) {
    in_rows(
      "Mean" = rcell(mean(x)),
      .formats = "xx.xx"
    )
  })

build_table(lyt1a, ADSL)
#        A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————
# Mean     33.77       35.43          35.43

If the format is specified in both of these places at the same time, the one specified via in_rows() takes highest precedence. Technically, in this case, the format defined in rcell() will simply be overwritten by the one defined in in_rows(). This is because the format specified in in_rows() is applied to the cells not the rows (overriding the previously specified cell-specific values), which indicates that the precedence rules described above are still in place.

lyt2 <- basic_table() %>%
  split_cols_by("ARM") %>%
  analyze(vars = "AGE", afun = function(x) {
    in_rows(
      "Mean" = rcell(mean(x), format = "xx.xxx"),
      .formats = "xx.xx"
    )
  })

build_table(lyt2, ADSL)
#        A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————
# Mean     33.77       35.43          35.43

Parent Table Format and Inheritance

In addition to the cell level, the format can be specified at the parent table level. If no format has been set by the user for a cell, the most specific format for that cell is the one defined at its innermost parent table split (if any).

lyt3 <- basic_table() %>%
  split_cols_by("ARM") %>%
  analyze(vars = "AGE", mean, format = "xx.x")

build_table(lyt3, ADSL)
#        A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————
# mean     33.8         35.4           35.4

If the cell format is also specified for a cell, then the parent table format is ignored for this cell since the cell format is more specific and therefore takes precedence.

lyt4 <- basic_table() %>%
  split_cols_by("ARM") %>%
  analyze(
    vars = "AGE", afun = function(x) {
      rcell(mean(x), format = "xx.xx", label = "Mean")
    },
    format = "xx.x"
  )

build_table(lyt4, ADSL)
#        A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————
# Mean     33.77       35.43          35.43
lyt4a <- basic_table() %>%
  split_cols_by("ARM") %>%
  analyze(
    vars = "AGE", afun = function(x) {
      in_rows(
        "Mean" = rcell(mean(x)),
        "SD" = rcell(sd(x)),
        .formats = "xx.xx"
      )
    },
    format = "xx.x"
  )

build_table(lyt4a, ADSL)
#        A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————
# Mean     33.77       35.43          35.43     
# SD       6.55         7.90           7.72

In the following, slightly more complicated, example, we can observe partial inheritance. That is, only SD cells inherit the parent table’s format while the Mean cells do not.

lyt5 <- basic_table() %>%
  split_cols_by("ARM") %>%
  analyze(
    vars = "AGE", afun = function(x) {
      in_rows(
        "Mean" = rcell(mean(x), format = "xx.xx"),
        "SD" = rcell(sd(x))
      )
    },
    format = "xx.x"
  )

build_table(lyt5, ADSL)
#        A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————
# Mean     33.77       35.43          35.43     
# SD        6.6         7.9            7.7

NA Handling

Consider the following layout and the resulting table created:

lyt6 <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX") %>%
  analyze(vars = "AGE", afun = mean, format = "xx.xx")

build_table(lyt6, ADSL)
#                    A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————————————
# F                                                         
#   mean               32.76       34.12          35.20     
# M                                                         
#   mean               35.57       37.44          35.38     
# U                                                         
#   mean               31.67       31.00          35.25     
# UNDIFFERENTIATED                                          
#   mean               28.00         NA           45.00

In the output the cell corresponding to the UNDIFFERENTIATED level of SEX and the B: Placebo level of ARM is displayed as NA. This occurs because there were no non-NA values under this facet that could be used to compute the mean. rtables allows the user to specify a string to display when cell values are NA. Similar to formats for numbers, the user can specify a string to replace NA with the parameter format_na_str or .format_na_str. This can be specified at the cell or parent table level. NA string precedence and inheritance rules are the same as those for number format precedence, described in the previous section of this vignette. We will illustrate this with a few examples.

Replacing NA Values at the Cell Level

At the cell level, it is possible to replace NA values with a custom string by means of the format_na_str parameter in rcell() or .format_na_str parameter in in_rows().

lyt7 <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX") %>%
  analyze(vars = "AGE", afun = function(x) {
    rcell(mean(x), format = "xx.xx", label = "Mean", format_na_str = "<missing>")
  })

build_table(lyt7, ADSL)
#                    A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————————————
# F                                                         
#   Mean               32.76       34.12          35.20     
# M                                                         
#   Mean               35.57       37.44          35.38     
# U                                                         
#   Mean               31.67       31.00          35.25     
# UNDIFFERENTIATED                                          
#   Mean               28.00     <missing>        45.00
lyt7a <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX") %>%
  analyze(vars = "AGE", afun = function(x) {
    in_rows(
      "Mean" = rcell(mean(x), format = "xx.xx"),
      .format_na_strs = "<MISSING>"
    )
  })

build_table(lyt7a, ADSL)
#                    A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————————————
# F                                                         
#   Mean               32.76       34.12          35.20     
# M                                                         
#   Mean               35.57       37.44          35.38     
# U                                                         
#   Mean               31.67       31.00          35.25     
# UNDIFFERENTIATED                                          
#   Mean               28.00     <MISSING>        45.00

If the NA string is specified in both of these places at the same time, the one specified with in_rows() takes precedence. Technically, in this case the NA replacement string defined in rcell() will simply be overwritten by the one defined in in_rows(). This is because the NA string specified in in_rows() is applied to the cells, not the rows (overriding the previously specified cell specific values), which means that the precedence rules described above are still in place.

lyt8 <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX") %>%
  analyze(vars = "AGE", afun = function(x) {
    in_rows(
      "Mean" = rcell(mean(x), format = "xx.xx", format_na_str = "<missing>"),
      .format_na_strs = "<MISSING>"
    )
  })

build_table(lyt8, ADSL)
#                    A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————————————
# F                                                         
#   Mean               32.76       34.12          35.20     
# M                                                         
#   Mean               35.57       37.44          35.38     
# U                                                         
#   Mean               31.67       31.00          35.25     
# UNDIFFERENTIATED                                          
#   Mean               28.00     <MISSING>        45.00

Parent Table Replacement of NA Values and Inheritance Principles

In addition to the cell level, the string replacement for NA values can be specified at the parent table level. If no replacement string has been specified by the user for a cell, the most specific NA string for that cell is the one defined at its innermost parent table split (if any).

lyt9 <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX") %>%
  analyze(vars = "AGE", mean, format = "xx.xx", na_str = "not available")

build_table(lyt9, ADSL)
#                    A: Drug X    B: Placebo     C: Combination
# —————————————————————————————————————————————————————————————
# F                                                            
#   mean               32.76         34.12           35.20     
# M                                                            
#   mean               35.57         37.44           35.38     
# U                                                            
#   mean               31.67         31.00           35.25     
# UNDIFFERENTIATED                                             
#   mean               28.00     not available       45.00

If an NA value replacement string was also specified at the cell level, then the one set at the parent table level is ignored for this cell as the cell level format is more specific and therefore takes precedence.

lyt10 <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX") %>%
  analyze(
    vars = "AGE", afun = function(x) {
      rcell(mean(x), format = "xx.xx", label = "Mean", format_na_str = "<missing>")
    },
    na_str = "not available"
  )

build_table(lyt10, ADSL)
#                    A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————————————
# F                                                         
#   Mean               32.76       34.12          35.20     
# M                                                         
#   Mean               35.57       37.44          35.38     
# U                                                         
#   Mean               31.67       31.00          35.25     
# UNDIFFERENTIATED                                          
#   Mean               28.00     <missing>        45.00
lyt10a <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX") %>%
  analyze(
    vars = "AGE", afun = function(x) {
      in_rows(
        "Mean" = rcell(mean(x)),
        "SD" = rcell(sd(x)),
        .formats = "xx.xx",
        .format_na_strs = "<missing>"
      )
    },
    na_str = "not available"
  )

build_table(lyt10a, ADSL)
#                    A: Drug X   B: Placebo   C: Combination
# ——————————————————————————————————————————————————————————
# F                                                         
#   Mean               32.76       34.12          35.20     
#   SD                 6.09         7.06           7.43     
# M                                                         
#   Mean               35.57       37.44          35.38     
#   SD                 7.08         8.69           8.24     
# U                                                         
#   Mean               31.67       31.00          35.25     
#   SD                 3.21         5.66           3.10     
# UNDIFFERENTIATED                                          
#   Mean               28.00     <missing>        45.00     
#   SD               <missing>   <missing>         1.41

In the following, slightly more complicated example, we can observe partial inheritance of NA strings. That is, only SD cells inherit the parent table’s NA string, while the Mean cells do not.

lyt11 <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX") %>%
  analyze(
    vars = "AGE", afun = function(x) {
      in_rows(
        "Mean" = rcell(mean(x), format_na_str = "<missing>"),
        "SD" = rcell(sd(x))
      )
    },
    format = "xx.xx",
    na_str = "not available"
  )

build_table(lyt11, ADSL)
#                      A: Drug X      B: Placebo     C: Combination
# —————————————————————————————————————————————————————————————————
# F                                                                
#   Mean                 32.76           34.12           35.20     
#   SD                   6.09            7.06             7.43     
# M                                                                
#   Mean                 35.57           37.44           35.38     
#   SD                   7.08            8.69             8.24     
# U                                                                
#   Mean                 31.67           31.00           35.25     
#   SD                   3.21            5.66             3.10     
# UNDIFFERENTIATED                                                 
#   Mean                 28.00         <missing>         45.00     
#   SD               not available   not available        1.41