Tabulation Concepts
In rtables: Reporting Tables

suggested_dependent_pkgs <- c("dplyr")
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = all(vapply(
    suggested_dependent_pkgs,
    requireNamespace,
    logical(1),
    quietly = TRUE
  ))
)

knitr::opts_chunk$set(echo = TRUE)

knitr::opts_chunk$set(comment = "#")

```{css, echo=FALSE} .reveal .r code { white-space: pre; }

## Introduction

In this vignette we will introduce some theory behind using layouts
for table creation. Much of the theory also holds true when using
other table packages. For this vignette we will use the following
packages:

```r
library(dplyr)
library(tibble)
library(rtables)

The data we use is the following, created with random number generators:

add_subgroup <- function(x) paste0(tolower(x), sample(1:3, length(x), TRUE))

set.seed(1)

df <- tibble(
  x = rnorm(100),
  c1 = factor(sample(c("A", "B", "C"), 100, replace = TRUE), levels = c("A", "B", "C")),
  r1 = factor(sample(c("U", "V", "W"), 100, replace = TRUE), levels = c("U", "V", "W"))
) %>%
  mutate(
    c2 = add_subgroup(c1),
    r2 = add_subgroup(r1),
    y = as.numeric(2 * as.numeric(c1) - 3 * as.numeric(r1))
  ) %>%
  select(c1, c2, r1, r2, x, y)

df

Building A Table Row By Row

Let's look at a table that has 3 columns and 3 rows. Each row represents a different analysis (functions foo, bar, zoo that return an rcell() object):

                     A         B         C
------------------------------------------------
foo_label        foo(df_A)  foo(df_B)  foo(df_C)
bar_label        bar(df_A)  bar(df_B)  bar(df_C)
zoo_label        zoo(df_A)  zoo(df_B)  zoo(df_C)

The data passed to the analysis functions are a subset defined by the respective column and:

df_A <- df %>% filter(c1 == "A")
df_B <- df %>% filter(c1 == "B")
df_C <- df %>% filter(c1 == "C")

Let's do this on the concrete data with analyze():

foo <- prod
bar <- sum
zoo <- mean

lyt <- basic_table() %>%
  split_cols_by("c1") %>%
  analyze("x", function(df) foo(df$x), var_labels = "foo label", format = "xx.xx") %>%
  analyze("x", function(df) bar(df$x), var_labels = "bar label", format = "xx.xx") %>%
  analyze("x", function(df) zoo(df$x), var_labels = "zoo label", format = "xx.xx")

tbl <- build_table(lyt, df)
tbl

or if we wanted the x variable instead of the data frame:

                     A         B         C
------------------------------------------------
foo_label        foo(x_A)  foo(x_B)  foo(x_C)
bar_label        bar(x_A)  bar(x_B)  bar(x_C)
zoo_label        zoo(x_A)  zoo(x_B)  zoo(x_C)

where:

x_A <- df_A$x
x_B <- df_B$x
x_C <- df_C$x

The function passed to afun is evaluated using argument matching. If afun has an argument x the analysis variable specified in vars in analyze() is passed to the function, and if afun has an argument df then a subset of the dataset is passed to afun:

lyt2 <- basic_table() %>%
  split_cols_by("c1") %>%
  analyze("x", foo, var_labels = "foo label", format = "xx.xx") %>%
  analyze("x", bar, var_labels = "bar label", format = "xx.xx") %>%
  analyze("x", zoo, var_labels = "zoo label", format = "xx.xx")

tbl2 <- build_table(lyt2, df)
tbl2

Note that it is also possible that a function returns multiple rows with in_rows():

lyt3 <- basic_table() %>%
  split_cols_by("c1") %>%
  analyze("x", function(x) {
    in_rows(
      "row 1" = rcell(mean(x), format = "xx.xx"),
      "row 2" = rcell(sd(x), format = "xx.xxx")
    )
  }, var_labels = "foo label") %>%
  analyze("x", function(x) {
    in_rows(
      "more rows 1" = rcell(median(x), format = "xx.x"),
      "even more rows 1" = rcell(IQR(x), format = "xx.xx")
    )
  }, var_labels = "bar label", format = "xx.xx")

tbl3 <- build_table(lyt3, df)
tbl3

This is how we recommend you specify the row names explicitly.

Tabulation With Row Structure

Let's say we would like to create the following table:

            A         B         C
--------------------------------------
U        foo(df_UA)  foo(df_UB)  foo(df_UC)
V        foo(df_VA)  foo(df_VB)  foo(df_VC)
W        foo(df_WA)  foo(df_WB)  foo(df_WC)

where df_* are subsets of df as follows:

df_UA <- df %>% filter(r1 == "U", c1 == "A")
df_VA <- df %>% filter(r1 == "V", c1 == "A")
df_WA <- df %>% filter(r1 == "W", c1 == "A")
df_UB <- df %>% filter(r1 == "U", c1 == "B")
df_VB <- df %>% filter(r1 == "V", c1 == "B")
df_WB <- df %>% filter(r1 == "W", c1 == "C")
df_UC <- df %>% filter(r1 == "U", c1 == "C")
df_VC <- df %>% filter(r1 == "V", c1 == "C")
df_WC <- df %>% filter(r1 == "W", c1 == "C")

further note that df_* are of the same class as df, i.e. tibbles. Hence foo aggregates the subset of our data to a cell value.

Given a function foo (ignore the ... for now):

foo <- function(df, labelstr = "", ...) {
  paste(dim(df), collapse = " x ")
}

we can start calculating the cell values individually:

foo(df_UA)
foo(df_VA)
foo(df_WA)
foo(df_UB)
foo(df_VB)
foo(df_WB)
foo(df_UC)
foo(df_VC)
foo(df_WC)

Now we are still missing the table structure:

matrix(
  list(
    foo(df_UA),
    foo(df_VA),
    foo(df_WA),
    foo(df_UB),
    foo(df_VB),
    foo(df_WB),
    foo(df_UC),
    foo(df_VC),
    foo(df_WC)
  ),
  byrow = FALSE, ncol = 3
)

In rtables this type of tabulation is done with layouts:

lyt4 <- basic_table() %>%
  split_cols_by("c1") %>%
  split_rows_by("r1") %>%
  analyze("x", foo)

tbl4 <- build_table(lyt4, df)
tbl4

or if we would not want to see the foo label we would have to use:

lyt5 <- basic_table() %>%
  split_cols_by("c1") %>%
  split_rows_by("r1") %>%
  summarize_row_groups(cfun = foo, format = "xx")

tbl5 <- build_table(lyt5, df)
tbl5

but now the row labels have disappeared. This is because cfun needs to define its row label. So let's redefine foo:

foo <- function(df, labelstr) {
  rcell(paste(dim(df), collapse = " x "), format = "xx", label = labelstr)
}

lyt6 <- basic_table() %>%
  split_cols_by("c1") %>%
  split_rows_by("r1") %>%
  summarize_row_groups(cfun = foo)

tbl6 <- build_table(lyt6, df)
tbl6

Calculating the Mean

Now let's calculate the mean of df$y for pattern I:

foo <- function(df, labelstr) {
  rcell(mean(df$y), label = labelstr, format = "xx.xx")
}

lyt7 <- basic_table() %>%
  split_cols_by("c1") %>%
  split_rows_by("r1") %>%
  summarize_row_groups(cfun = foo)

tbl7 <- build_table(lyt7, df)
tbl7

Note that foo has the variable information hard-encoded in the function body. Let's try some alternatives returning to analyze():

lyt8 <- basic_table() %>%
  split_cols_by("c1") %>%
  split_rows_by("r1") %>%
  analyze("y", afun = mean)

tbl8 <- build_table(lyt8, df)
tbl8

Note that the subset of the y variable is passed as the x argument to mean(). We could also get the data.frame instead of the variable:

lyt9 <- basic_table() %>%
  split_cols_by("c1") %>%
  split_rows_by("r1") %>%
  analyze("y", afun = function(df) mean(df$y))

tbl9 <- build_table(lyt9, df)
tbl9

which is in contrast to:

lyt10 <- basic_table() %>%
  split_cols_by("c1") %>%
  split_rows_by("r1") %>%
  analyze("y", afun = function(x) mean(x))

tbl10 <- build_table(lyt10, df)
tbl10

where the function receives the subset of y.

Group Summaries

Pattern I is an interesting one as we can add more row structure (with further splits). Consider the following table:

            A         B         C
--------------------------------------
U
  u1     foo(<>)  foo(<>)  foo(<>)
  u2     foo(<>)  foo(<>)  foo(<>)
  u3     foo(<>)  foo(<>)  foo(<>)
V
  v1     foo(<>)  foo(<>)  foo(<>)
  v2     foo(<>)  foo(<>)  foo(<>)
  v3     foo(<>)  foo(<>)  foo(<>)
W
  w1     foo(<>)  foo(<>)  foo(<>)
  w2     foo(<>)  foo(<>)  foo(<>)
  w3     foo(<>)  foo(<>)  foo(<>)

where <> represents the data that is represented by the cell. So for the cell U > u1, A we would have the subset:

df %>%
  filter(r1 == "U", r2 == "u1", c1 == "A")

and so on. We can get this table as follows:

lyt11 <- basic_table() %>%
  split_cols_by("c1") %>%
  split_rows_by("r1") %>%
  split_rows_by("r2") %>%
  summarize_row_groups(cfun = function(df, labelstr) {
    rcell(mean(df$x), format = "xx.xx", label = paste("mean x for", labelstr))
  })

tbl11 <- build_table(lyt11, df)
tbl11

or, if we wanted to calculate two summaries per row split:

s_mean_sd <- function(x) {
  in_rows("mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"))
}

s_range <- function(x) {
  in_rows("range" = rcell(range(x), format = "xx.xx - xx.xx"))
}

lyt12 <- basic_table() %>%
  split_cols_by("c1") %>%
  split_rows_by("r1") %>%
  split_rows_by("r2") %>%
  analyze("x", s_mean_sd, show_labels = "hidden") %>%
  analyze("x", s_range, show_labels = "hidden")

tbl12 <- build_table(lyt12, df)
tbl12

Which has the following structure:

                   A              B              C
---------------------------------------------------------
U
  u1
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  u2
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  u3
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
V
  v1
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  v2
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  v3
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
W
  w1
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  w2
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  w3
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)

The rows U, u1, u2, ..., W, w1, w2, w3 are label rows and the other rows (with mean_sd and range) are data rows. Currently we do not have content rows in the table. Content rows summarize the data defined by their splitting (i.e. V > v1, B). So if we wanted to add content rows at the r2 split level then we would get:

                   A              B              C
---------------------------------------------------------
U
  u1          s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  u2          s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  u3          s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
V
  v1          s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  v2          s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  v3          s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
W
  w1          s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  w2          s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  w3          s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)

where s_cfun_2 is the content function and either returns one row via rcell() or multiple rows via in_rows(). The data represented by <> for the content rows is same data as for it's descendant, i.e. for the U > u1, A content row cell it is df %>% filter(r1 == "U", r2 == "u1", c1 == "A"). Note that content functions cfun operate only on data frames and not on vectors/variables so they must take the df argument. Further, a cfun must also have the labelstr argument which is the split level. This way, the cfun can define its own row name. In order to get the table above we can use the layout framework as follows:

s_mean_sd <- function(x) {
  in_rows("mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"))
}

s_range <- function(x) {
  in_rows("range" = rcell(range(x), format = "xx.xx - xx.xx"))
}

s_cfun_2 <- function(df, labelstr) {
  rcell(nrow(df), format = "xx", label = paste(labelstr, "(n)"))
}

lyt13 <- basic_table() %>%
  split_cols_by("c1") %>%
  split_rows_by("r1") %>%
  split_rows_by("r2") %>%
  summarize_row_groups(cfun = s_cfun_2) %>%
  analyze("x", s_mean_sd, show_labels = "hidden") %>%
  analyze("x", s_range, show_labels = "hidden")

tbl13 <- build_table(lyt13, df)
tbl13

In the same manner, if we want content rows for the r1 split we can do it at as follows:

lyt14 <- basic_table() %>%
  split_cols_by("c1") %>%
  split_rows_by("r1") %>%
  summarize_row_groups(cfun = s_cfun_2) %>%
  split_rows_by("r2") %>%
  summarize_row_groups(cfun = s_cfun_2) %>%
  analyze("x", s_mean_sd, show_labels = "hidden") %>%
  analyze("x", s_range, show_labels = "hidden")

tbl14 <- build_table(lyt14, df)
tbl14

In pagination, content rows and label rows get repeated if a page is split in a descendant of a content row. So, for example, if we were to split the following table at ***:

                   A              B              C
---------------------------------------------------------
U
  u1 (n)      s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
***
     range    s_range(<>)    s_range(<>)    s_range(<>)
  u2 (n)      s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)

Then we would get the following two tables:

                   A              B              C
---------------------------------------------------------
U
  u1 (n)      s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)

and

                   A              B              C
---------------------------------------------------------
U
  u1 (n)      s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)
  u2 (n)      s_cfun_2(<>)   s_cfun_2(<>)   s_cfun_2(<>)
     mean_sd  s_mean_sd(<>)  s_mean_sd(<>)  s_mean_sd(<>)
     range    s_range(<>)    s_range(<>)    s_range(<>)

Pattern III

Let's consider the following tabulation pattern:

                     A         B         C
------------------------------------------------
label 1        foo(x_A)  bar(x_B)  zoo(x_C)
label 2        foo(x_A)  bar(x_B)  zoo(x_C)
label 3        foo(x_A)  bar(x_B)  zoo(x_C)

We will discuss that in a future release of rtables.