# Tabulation Concepts

#### Adrian Waddell

#### 6/12/2020

## Introduction

In this vignette we will introduce some theory behind using layouts for table creation. Much of the theory also holds true when using

For this vignette we will use the following packages:

    library(dplyr)
    library(tibble)
    library(rtables)

The data we use is the following one 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

    # A tibble: 100 x 6
       c1    c2    r1    r2         x     y
       <fct> <chr> <fct> <chr>  <dbl> <dbl>
     1 B     b2    U     u3    -0.626     1
     2 A     a3    V     v2     0.184    -4
     3 B     b1    V     v2    -0.836    -2
     4 B     b3    V     v2     1.60     -2
     5 B     b1    U     u1     0.330     1
     6 C     c1    U     u3    -0.820     3
     7 A     a3    U     u3     0.487    -1
     8 B     b1    U     u3     0.738     1
     9 C     c3    V     v2     0.576     0
    10 C     c3    U     u2    -0.305     3
    # … with 90 more rows

## 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

    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") %>%
      build_table(df)

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

                   A      B      C
    --------------------------------
    foo label
      foo label    0      0      0
    bar label
      bar label   1.87   4.37   4.64
    zoo label
      zoo label   0.05   0.13   0.18

of 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 set subset of the dataset is passed to `afun`. So,

    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") %>%
      build_table(df)

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

                 A      B      C
    ------------------------------
    foo label
      foo        0      0      0
    bar label
      bar       1.87   4.37   4.64
    zoo label
      zoo       0.05   0.13   0.18

Note that it is also possible that a function returns multiple rows with `in_rows`

    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") %>%
      build_table(df)

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

                           A       B      C
    -----------------------------------------
    foo label
      row 1              0.05    0.13    0.18
      row 2              0.985   0.815   0.89
    bar label
      more rows 1          0      0.2    0.3
      even more rows 1    1.2    1.15    1.16

which is how we recommend to 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. `tibble`s. 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)

    [1] "17 x 6"

    foo(df_VA)

    [1] "9 x 6"

    foo(df_WA)

    [1] "14 x 6"

    foo(df_UB)

    [1] "13 x 6"

    foo(df_VB)

    [1] "15 x 6"

    foo(df_WB)

    [1] "11 x 6"

    foo(df_UC)

    [1] "10 x 6"

    foo(df_VC)

    [1] "5 x 6"

    foo(df_WC)

    [1] "11 x 6"

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
    )

         [,1]     [,2]     [,3]
    [1,] "17 x 6" "13 x 6" "10 x 6"
    [2,] "9 x 6"  "15 x 6" "5 x 6"
    [3,] "14 x 6" "11 x 6" "11 x 6"

In `rtables` this type of tabulation is done with `layouts`

    basic_table() %>%
      split_cols_by("c1") %>%
      split_rows_by("r1") %>%
      analyze("x", foo) %>%
      build_table(df)

              A        B        C
    --------------------------------
    U
      foo   17 x 6   13 x 6   10 x 6
    V
      foo   9 x 6    15 x 6   5 x 6
    W
      foo   14 x 6   6 x 6    11 x 6

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

    basic_table() %>%
      split_cols_by("c1") %>%
      split_rows_by("r1") %>%
      summarize_row_groups(cfun = foo, format = "xx") %>%
      build_table(df)

         A        B        C
    ---------------------------
       17 x 6   13 x 6   10 x 6
       9 x 6    15 x 6   5 x 6
       14 x 6   6 x 6    11 x 6

but now the row labels disapered. This is because `cfun` needs to define it’s row label. So let’s redefine `foo`:

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

    basic_table() %>%
      split_cols_by("c1") %>%
      split_rows_by("r1") %>%
      summarize_row_groups(cfun = foo) %>%
      build_table(df)

          A        B        C
    ----------------------------
    U   17 x 6   13 x 6   10 x 6
    V   9 x 6    15 x 6   5 x 6
    W   14 x 6   6 x 6    11 x 6

### Calculating the Mean

For lets calculate the mean of `df$y` for pattern I.

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

    basic_table() %>%
      split_cols_by("c1") %>%
      split_rows_by("r1") %>%
      summarize_row_groups(cfun = foo) %>%
      build_table(df)

        A    B    C
    ----------------
    U   -1   1    3
    V   -4   -2   0
    W   -7   -5   -3

Note that `foo` has the variable information hard encoded in the function body. Let’s try some alternatives returning to `analyze`:

    basic_table() %>%
      split_cols_by("c1") %>%
      split_rows_by("r1") %>%
      analyze("y", afun = mean) %>%
      build_table(df)

             A    B    C
    ---------------------
    U
      mean   -1   1    3
    V
      mean   -4   -2   0
    W
      mean   -7   -5   -3

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

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

          A    B    C
    ------------------
    U
      y   -1   1    3
    V
      y   -4   -2   0
    W
      y   -7   -5   -3

which is in contrast to:

    basic_table() %>%
      split_cols_by("c1") %>%
      split_rows_by("r1") %>%
      analyze("y", afun = function(x) mean(x)) %>%
      build_table(df)

          A    B    C
    ------------------
    U
      y   -1   1    3
    V
      y   -4   -2   0
    W
      y   -7   -5   -3

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")

    # A tibble: 2 x 6
      c1    c2    r1    r2        x     y
      <fct> <chr> <fct> <chr> <dbl> <dbl>
    1 A     a2    U     u1    1.12     -1
    2 A     a1    U     u1    0.594    -1

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

    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))
      }) %>%
      build_table(df)

                        A       B       C
    ---------------------------------------
    U
      mean x for u3   -0.04   0.36    -0.25
      mean x for u1   0.86    0.32     NaN
      mean x for u2   -0.28   0.38    0.08
    V
      mean x for v2   0.01    0.55     0.6
      mean x for v3   -0.03   -0.3    1.06
      mean x for v1   0.56    -0.27   -0.54
    W
      mean x for w1   -0.58   0.42    0.67
      mean x for w3   0.56    0.69    -0.39
      mean x for w2   -1.99   -0.1    0.53

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"))
    }

    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") %>%
      build_table(df)

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in min(x): no non-missing arguments to min; returning Inf

    Warning in max(x): no non-missing arguments to max; returning -Inf

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

                          A              B              C
    -----------------------------------------------------------
    U
      u3
        mean (sd)   -0.04 (1.18)    0.36 (1.41)    -0.25 (0.72)
        range        -1.8 - 1.47    -1.28 - 2.4    -0.82 - 0.56
      u1
        mean (sd)    0.86 (0.38)    0.32 (0.51)      NaN (NA)
        range        0.59 - 1.12    -0.48 - 0.94    Inf - -Inf
      u2
        mean (sd)   -0.28 (0.96)    0.38 (0.67)    0.08 (0.91)
        range       -1.52 - 1.43    -0.39 - 0.82   -0.93 - 1.51
    V
      v2
        mean (sd)    0.01 (0.25)    0.55 (1.14)     0.6 (0.03)
        range       -0.16 - 0.18    -0.84 - 1.6    0.58 - 0.62
      v3
        mean (sd)   -0.03 (0.37)    -0.3 (0.36)     1.06 (NA)
        range       -0.41 - 0.33    -0.62 - 0.03   1.06 - 1.06
      v1
        mean (sd)    0.56 (1.1)     -0.27 (0.73)   -0.54 (1.18)
        range       -0.16 - 2.17    -1.22 - 0.59   -1.38 - 0.29
    W
      w1
        mean (sd)   -0.58 (0.85)     0.42 (NA)     0.67 (0.39)
        range       -1.25 - 0.61    0.42 - 0.42    0.37 - 1.21
      w3
        mean (sd)    0.56 (0.85)     0.69 (NA)     -0.39 (1.68)
        range       -0.71 - 1.98    0.69 - 0.69    -2.21 - 1.1
      w2
        mean (sd)    -1.99 (NA)     -0.1 (0.47)     0.53 (0.6)
        range       -1.99 - -1.99   -0.61 - 0.39   -0.1 - 1.16

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 row 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)"))
    }

    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") %>%
      build_table(df)

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in min(x): no non-missing arguments to min; returning Inf

    Warning in max(x): no non-missing arguments to max; returning -Inf

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

                          A              B              C
    -----------------------------------------------------------
    U
      u3 (n)              6              5              3
        mean (sd)   -0.04 (1.18)    0.36 (1.41)    -0.25 (0.72)
        range        -1.8 - 1.47    -1.28 - 2.4    -0.82 - 0.56
      u1 (n)              2              5              0
        mean (sd)    0.86 (0.38)    0.32 (0.51)      NaN (NA)
        range        0.59 - 1.12    -0.48 - 0.94    Inf - -Inf
      u2 (n)              9              3              7
        mean (sd)   -0.28 (0.96)    0.38 (0.67)    0.08 (0.91)
        range       -1.52 - 1.43    -0.39 - 0.82   -0.93 - 1.51
    V
      v2 (n)              2              4              2
        mean (sd)    0.01 (0.25)    0.55 (1.14)     0.6 (0.03)
        range       -0.16 - 0.18    -0.84 - 1.6    0.58 - 0.62
      v3 (n)              3              4              1
        mean (sd)   -0.03 (0.37)    -0.3 (0.36)     1.06 (NA)
        range       -0.41 - 0.33    -0.62 - 0.03   1.06 - 1.06
      v1 (n)              4              7              2
        mean (sd)    0.56 (1.1)     -0.27 (0.73)   -0.54 (1.18)
        range       -0.16 - 2.17    -1.22 - 0.59   -1.38 - 0.29
    W
      w1 (n)              4              1              4
        mean (sd)   -0.58 (0.85)     0.42 (NA)     0.67 (0.39)
        range       -1.25 - 0.61    0.42 - 0.42    0.37 - 1.21
      w3 (n)              9              1              3
        mean (sd)    0.56 (0.85)     0.69 (NA)     -0.39 (1.68)
        range       -0.71 - 1.98    0.69 - 0.69    -2.21 - 1.1
      w2 (n)              1              4              4
        mean (sd)    -1.99 (NA)     -0.1 (0.47)     0.53 (0.6)
        range       -1.99 - -1.99   -0.61 - 0.39   -0.1 - 1.16

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

    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") %>%
      build_table(df)

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in min(x): no non-missing arguments to min; returning Inf

    Warning in max(x): no non-missing arguments to max; returning -Inf

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

    Warning in .make_split_kids(spl = spl, df = df, lvl = lvl, splvec = splvec, :
    Non-unique sibling analysis table names. Using Labels instead. Use the
    table_names argument to analyze to avoid this when analyzing the same variable
    multiple times.

                          A              B              C
    -----------------------------------------------------------
    U (n)                17              13             10
      u3 (n)              6              5              3
        mean (sd)   -0.04 (1.18)    0.36 (1.41)    -0.25 (0.72)
        range        -1.8 - 1.47    -1.28 - 2.4    -0.82 - 0.56
      u1 (n)              2              5              0
        mean (sd)    0.86 (0.38)    0.32 (0.51)      NaN (NA)
        range        0.59 - 1.12    -0.48 - 0.94    Inf - -Inf
      u2 (n)              9              3              7
        mean (sd)   -0.28 (0.96)    0.38 (0.67)    0.08 (0.91)
        range       -1.52 - 1.43    -0.39 - 0.82   -0.93 - 1.51
    V (n)                 9              15             5
      v2 (n)              2              4              2
        mean (sd)    0.01 (0.25)    0.55 (1.14)     0.6 (0.03)
        range       -0.16 - 0.18    -0.84 - 1.6    0.58 - 0.62
      v3 (n)              3              4              1
        mean (sd)   -0.03 (0.37)    -0.3 (0.36)     1.06 (NA)
        range       -0.41 - 0.33    -0.62 - 0.03   1.06 - 1.06
      v1 (n)              4              7              2
        mean (sd)    0.56 (1.1)     -0.27 (0.73)   -0.54 (1.18)
        range       -0.16 - 2.17    -1.22 - 0.59   -1.38 - 0.29
    W (n)                14              6              11
      w1 (n)              4              1              4
        mean (sd)   -0.58 (0.85)     0.42 (NA)     0.67 (0.39)
        range       -1.25 - 0.61    0.42 - 0.42    0.37 - 1.21
      w3 (n)              9              1              3
        mean (sd)    0.56 (0.85)     0.69 (NA)     -0.39 (1.68)
        range       -0.71 - 1.98    0.69 - 0.69    -2.21 - 1.1
      w2 (n)              1              4              4
        mean (sd)    -1.99 (NA)     -0.1 (0.47)     0.53 (0.6)
        range       -1.99 - -1.99   -0.61 - 0.39   -0.1 - 1.16

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 `***` then :

                       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(<>)

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`.