# Introduction to rtables

#### Gabriel Becker and Adrian Waddell

#### 2022-05-20

## Introduction

The `rtables` R package provides a framework to create, tabulate and output tables in `R`. Most of the design requirements for `rtables` have their origin in studying tables that are commonly used to report analyses from clinical trials; however, we were careful to keep `rtables` a general purpose toolkit.

There are a number of other table frameworks available in `R` such as gt from RStudio, xtable, tableone, and tables to name a few. There is a number of reasons to implement `rtables` (yet another tables R package):

- output tables in ASCII to text files
- `rtables` has two powerful tabulation frameworks: `rtabulate` and the layouting based tabulation framework
- table view (ASCII, HTML, etc.) is separate from the data model. Hence, one always has access to the non-rounded/non-formatted numbers.
- pagination to meet the health authority submission requirements
- cell, row, column, table reference system (to be implemented)
- title footnotes (to be implemented)
- path based access to cell content which will be useful for automated content generation

In the remainder of this vignette, we give a short introduction into `rtables` and tabulating a table. The content is based on the useR 2020 presentation from Gabriel Becker.

The packages used for this vignette are `rtables` and `dplyr`:

    library(rtables)
    library(dplyr)

## Data

The data used in this vignette is a made up using random number generators. The data content is relatively simple: one row per imaginary person and one column per measurement: study arm, the country of origin, gender, handedness, age, and weight.

    n <- 400

    set.seed(1)

    df <- tibble(
      arm = factor(sample(c("Arm A", "Arm B"), n, replace = TRUE), levels = c("Arm A", "Arm B")),
      country = factor(sample(c("CAN", "USA"), n, replace = TRUE, prob = c(.55, .45)), levels = c("CAN", "USA")),
      gender = factor(sample(c("Female", "Male"), n, replace = TRUE), levels = c("Female", "Male")),
      handed = factor(sample(c("Left", "Right"), n, prob = c(.6, .4), replace = TRUE), levels = c("Left", "Right")),
      age = rchisq(n, 30) + 10
    ) %>% mutate(
      weight = 35 * rnorm(n, sd = .5) + ifelse(gender == "Female", 140, 180)
    )

    head(df)

    # A tibble: 6 × 6
      arm   country gender handed   age weight
      <fct> <fct>   <fct>  <fct>  <dbl>  <dbl>
    1 Arm A USA     Female Left    31.3   139.
    2 Arm B CAN     Female Right   50.5   116.
    3 Arm A USA     Male   Right   32.4   186.
    4 Arm A USA     Male   Right   34.6   169.
    5 Arm B USA     Female Right   43.0   160.
    6 Arm A USA     Female Right   43.2   126.

Note that we use factors variables so that the level order is represented in the row or column order when we tabulate the information of `df` below.

## Building an Table

The aim of this vignette is to build the following table step by step:

                        Arm A                     Arm B
                 Female        Male        Female        Male
                 (N=96)      (N=105)       (N=92)      (N=107)
    ————————————————————————————————————————————————————————————
    CAN        45 (46.9%)   64 (61.0%)   46 (50.0%)   62 (57.9%)
      Left     32 (33.3%)   42 (40.0%)   26 (28.3%)   37 (34.6%)
        mean      38.9         40.4         40.3         37.7
      Right    13 (13.5%)   22 (21.0%)   20 (21.7%)   25 (23.4%)
        mean      36.6         40.2         40.2         40.6
    USA        51 (53.1%)   41 (39.0%)   46 (50.0%)   45 (42.1%)
      Left     34 (35.4%)   19 (18.1%)   25 (27.2%)   25 (23.4%)
        mean      40.4         39.7         39.2         40.1
      Right    17 (17.7%)   22 (21.0%)   21 (22.8%)   20 (18.7%)
        mean      36.9         39.8         38.5         39.0

## Starting Simple

In `rtables` a basic table is defined to have 0 rows and one column representing all data. Analyzing a variable is one way of adding a row:

    l <- basic_table() %>%
      analyze("age", mean, format = "xx.x")

    build_table(l, df)

           all obs
    ——————————————
    mean    39.4

### Layout Instructions

In the code above we first described the table and assigned that description to a variable `l`. We then built the table using the actual data with `build_table`. The description of a table is called a table layout. `basic_table` is the start of every table layout and contains the information that we have one column representing all data. The `analyze` instruction adds to the layout that the `age` variable should be analyzed with the `mean` analysis function and the result should be rounded to `1` decimal place.

Hence, a layout is “pre-data”, that is, it’s a description of how to build a table once we get data. We can look at the layout isolated:

    l

    A Pre-data Table Layout

    Column-Split Structure:
     ()

    Row-Split Structure:
    age (** analysis **)

The general layouting instructions are summarized below:

- `basic_table` is a layout representing a table with zero rows and one column
- Nested splitting
  - in row space: `split_rows_by`, `split_rows_by_multivar`, `split_rows_by_cuts`, `split_rows_by_cutfun`, `split_rows_by_quartiles`
  - in column space: `split_cols_by`, `split_cols_by_cuts`, `split_cols_by_cutfun`, `split_cols_by_quartiles`
- Summarizing Groups: `summarize_row_groups`
- Analyzing Variables: `analyze`, `analyze_against_baseline`, `analyze_colvars`, `analyze_row_groups`

using those functions it is possible to create a wide variety of tables as we will show in this document.

### Adding Column Structure

We will now add more structure to the columns by adding a column split based on the factor variable `arm`:

    l <- basic_table() %>%
      split_cols_by("arm") %>%
      analyze("age", afun = mean, format = "xx.x")

    build_table(l, df)

           Arm A   Arm B
    ————————————————————
    mean   39.5    39.4

The resulting table has one column per factor level of `arm`. So the data represented by the first column is `df[df$arm == "ARM A", ]`. Hence, the `split_cols_by` partitions the data among the columns by default.

Column splitting can be done in a recursive/nested manner by adding sequential `split_cols_by` layout instruction. It’s also possible to add a non-nested split. Here we splitting each arm further by the gender:

    l <- basic_table() %>%
      split_cols_by("arm") %>%
      split_cols_by("gender") %>%
      analyze("age", afun = mean, format = "xx.x")

    build_table(l, df)

               Arm A           Arm B
           Female   Male   Female   Male
    ————————————————————————————————————
    mean    38.8    40.1    39.6    39.2

The first column represents the data in `df` where `df$arm == "A" & df$gender == "Female"` and the second column the data in `df` where `df$arm == "A" & df$gender == "Male"`, and so on.

### Adding Row Structure

So far, we have created layouts with analysis and column splitting instructions, i.e. `analyze` and `split_cols_by`, respectively. This resulted with a table with multiple columns and one data row. We will add more row structure by stratifying the mean analysis by country (i.e. adding a split in the row space):

    l <- basic_table() %>%
      split_cols_by("arm") %>%
      split_cols_by("gender") %>%
      split_rows_by("country") %>%
      analyze("age", afun = mean, format = "xx.x")

    build_table(l, df)

                 Arm A           Arm B
             Female   Male   Female   Male
    ——————————————————————————————————————
    CAN
      mean    38.2    40.3    40.3    38.9
    USA
      mean    39.2    39.7    38.9    39.6

In this table the data used to derive the first data cell (average of age of female canadians in Arm A) is where `df$country == "CAN" & df$arm == "Arm A" & df$gender == "Female"`. This cell value can also be calculated manually:

    mean(df$age[df$country == "CAN" & df$arm == "Arm A" & df$gender == "Female"])

    [1] 38.22447

### Adding Group Information

When adding row splits we get by default label rows for each split level, for example `CAN` and `USA` in the table above. Besides the column space subsetting, we have now further subsetted the data for each cell. It is often useful when defining a row splitting to display information about each row group. In `rtables` this is referred to as content information, i.e. `mean` on row 2 is a descendant of `CAN` (visible via the indenting, though the table has an underlying tree structure that is not of importance for this vignette). In order to add content information and turn the `CAN` label row into a content row the `summarize_row_groups` function is required. By default, the count (`nrows`) and percentage of data relative to the column associated data is calculated:

    l <- basic_table() %>%
      split_cols_by("arm") %>%
      split_cols_by("gender") %>%
      split_rows_by("country") %>%
      summarize_row_groups() %>%
      analyze("age", afun = mean, format = "xx.x")

    build_table(l, df)

                      Arm A                     Arm B
               Female        Male        Female        Male
    ——————————————————————————————————————————————————————————
    CAN      45 (46.9%)   64 (61.0%)   46 (50.0%)   62 (57.9%)
      mean      38.2         40.3         40.3         38.9
    USA      51 (53.1%)   41 (39.0%)   46 (50.0%)   45 (42.1%)
      mean      39.2         39.7         38.9         39.6

The relative percentage for average age of female Canadians is calculated as follows:

    df_cell <- subset(df, df$country == "CAN" & df$arm == "Arm A" & df$gender == "Female")
    df_col_1 <- subset(df, df$arm == "Arm A" & df$gender == "Female")

    c(count = nrow(df_cell), percentage = nrow(df_cell)/nrow(df_col_1))

         count percentage
      45.00000    0.46875

so the group percentages per row split sum up to 1 for each column.

We can further split the row space by dividing each country by handedness:

    l <- basic_table() %>%
      split_cols_by("arm") %>%
      split_cols_by("gender") %>%
      split_rows_by("country") %>%
      summarize_row_groups() %>%
      split_rows_by("handed") %>%
      analyze("age", afun = mean, format = "xx.x")

    build_table(l, df)

                        Arm A                     Arm B
                 Female        Male        Female        Male
    ————————————————————————————————————————————————————————————
    CAN        45 (46.9%)   64 (61.0%)   46 (50.0%)   62 (57.9%)
      Left
        mean      38.9         40.4         40.3         37.7
      Right
        mean      36.6         40.2         40.2         40.6
    USA        51 (53.1%)   41 (39.0%)   46 (50.0%)   45 (42.1%)
      Left
        mean      40.4         39.7         39.2         40.1
      Right
        mean      36.9         39.8         38.5         39.0

Next, we further add a count and percentage summary for handedness within each country:

    l <- basic_table() %>%
      split_cols_by("arm") %>%
      split_cols_by("gender") %>%
      split_rows_by("country") %>%
      summarize_row_groups() %>%
      split_rows_by("handed") %>%
      summarize_row_groups() %>%
      analyze("age", afun = mean, format = "xx.x")

    build_table(l, df)

                        Arm A                     Arm B
                 Female        Male        Female        Male
    ————————————————————————————————————————————————————————————
    CAN        45 (46.9%)   64 (61.0%)   46 (50.0%)   62 (57.9%)
      Left     32 (33.3%)   42 (40.0%)   26 (28.3%)   37 (34.6%)
        mean      38.9         40.4         40.3         37.7
      Right    13 (13.5%)   22 (21.0%)   20 (21.7%)   25 (23.4%)
        mean      36.6         40.2         40.2         40.6
    USA        51 (53.1%)   41 (39.0%)   46 (50.0%)   45 (42.1%)
      Left     34 (35.4%)   19 (18.1%)   25 (27.2%)   25 (23.4%)
        mean      40.4         39.7         39.2         40.1
      Right    17 (17.7%)   22 (21.0%)   21 (22.8%)   20 (18.7%)
        mean      36.9         39.8         38.5         39.0

## Summary

In this vignette you have learned:

- every cell has an associated subset of data
  - this means that much of tabulation has to do with splitting/subsetting data
- tables can be described pre-data using layouts
- tables are a form of visualization of data

The other vignettes in the `rtables` package will provide more detailed information about the `rtables` package. We recommend that you continue with the `tabulation_dplyr` vignette which compares the information derived by the table in this vignette using `dplyr`.