Introduction to {rtables}

Introduction

The rtables 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.

In this vignette, we give a short introduction into rtables and tabulating a table.

The content in this vignette is based on the following two resources:

The rtables useR 2020 presentation by Gabriel Becker
rtables - A Framework For Creating Complex Structured Reporting Tables Via Multi-Level Faceted Computations.

The packages used in this vignette are rtables and dplyr:

library(rtables)
library(dplyr)

Overview

To build a table using rtables two components are required: A layout constructed using rtables functions, and a data.frame of unaggregated data. These two elements are combined to build a table object. Table objects contain information about both the content and the structure of the table, as well as instructions on how this information should be processed to construct the table. After obtaining the table object, a formatted table can be printed in ASCII format, or exported to a variety of other formats (.txt, .pdf, .docx, etc.).

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

Building a 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     38.87        40.43        40.33        37.68   
#   Right    13 (13.5%)   22 (21.0%)   20 (21.7%)   25 (23.4%)
#     mean     36.64        40.19        40.16        40.65   
# 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     40.36        39.68        39.21        40.07   
#   Right    17 (17.7%)   22 (21.0%)   21 (22.8%)   20 (18.7%)
#     mean     36.94        39.80        38.53        39.02

Quick Start

The table above can be achieved via the qtable() function. If you are new to tabulation with the rtables layout framework, you can use this convenience wrapper to create many types of two-way frequency tables.

The purpose of qtable is to enable quick exploratory data analysis. See the exploratory_analysis vignette for more details.

Here is the code to recreate the table above:

qtable(df,
  row_vars = c("country", "handed"),
  col_vars = c("arm", "gender"),
  avar = "age",
  afun = mean,
  summarize_groups = TRUE,
  row_labels = "mean"
)

#                       Arm A                     Arm B         
#                Female        Male        Female        Male   
# age - mean     (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       38.87        40.43        40.33        37.68   
#   Right      13 (13.5%)   22 (21.0%)   20 (21.7%)   25 (23.4%)
#     mean       36.64        40.19        40.16        40.65   
# 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       40.36        39.68        39.21        40.07   
#   Right      17 (17.7%)   22 (21.0%)   21 (22.8%)   20 (18.7%)
#     mean       36.94        39.80        38.53        39.02

From the qtable function arguments above we can see many of the key concepts of the underlying rtables layout framework. The user needs to define:

Which variables should be used as facets in the row and/or column space?
Which variable should be used in the summary analysis?
Which function should be used as a summary?
Should the table include any marginal summaries?
Are any labels needed to clarify the table content?

In the sections below we will look at translating each of these questions to a set of features part of the rtables layout framework. Now let’s take a look at building the example table with a layout.

Layout Instructions

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:

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

tbl <- build_table(lyt, df)
tbl

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

In the code above we first described the table and assigned that description to a variable lyt. 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 in 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:

lyt

# 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_multivar(), split_cols_by_cuts(), split_cols_by_cutfun(), split_cols_by_quartiles()
Summarizing Groups: summarize_row_groups()
Analyzing Variables: analyze(), analyze_colvars()

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:

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

tbl <- build_table(lyt, df)
tbl

#        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:

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

tbl <- build_table(lyt, df)
tbl

#            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.

More information on column structure can be found in the col_counts vignette.

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):

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

tbl <- build_table(lyt, df)
tbl

#              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

Row structure can also be used to group the table into titled groups of pages during rendering. We do this via ‘page by splits’, which are declared via page_by = TRUE within a call to split_rows_by:

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

tbl <- build_table(lyt, df)
cat(export_as_txt(tbl, page_type = "letter", page_break = "\n\n~~~~~~ Page Break ~~~~~~\n\n"))

# 
# country: CAN
# 
# ————————————————————————————————————————
#                Arm A           Arm B    
#            Female   Male   Female   Male
# ————————————————————————————————————————
# Left                                    
#   mean      38.9    40.4    40.3    37.7
# Right                                   
#   mean      36.6    40.2    40.2    40.6
# 
# 
# ~~~~~~ Page Break ~~~~~~
# 
# 
# country: USA
# 
# ————————————————————————————————————————
#                Arm A           Arm B    
#            Female   Male   Female   Male
# ————————————————————————————————————————
# Left                                    
#   mean      40.4    39.7    39.2    40.1
# Right                                   
#   mean      36.9    39.8    38.5    39.0

We go into more detail on page-by splits and how to control the page-group specific titles in the Title and footer vignette.

Note that if you print or render a table without pagination, the page_by splits are currently rendered as normal row splits. This may change in future releases.

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:

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

tbl <- build_table(lyt, df)
tbl

#                   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:

lyt <- 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")

tbl <- build_table(lyt, df)
tbl

#                     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:

lyt <- 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")

tbl <- build_table(lyt, df)
tbl

#                     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

Comparing with Other Tabulation Frameworks

There are a number of other table frameworks available in R, including:

There are a number of reasons to choose rtables (yet another tables R package):

Output tables in ASCII to text files.
Table rendering (ASCII, HTML, etc.) is separate from the data model. Hence, one always has access to the non-rounded/non-formatted numbers.
Pagination in both horizontal and vertical directions to meet the health authority submission requirements.
Cell, row, column, and table reference system.
Titles, footers, and referential footnotes.
Path based access to cell content which is useful for automated content generation.

More in depth comparisons of the various tabulation frameworks can be found in the Overview of table R packages chapter of the Tables in Clinical Trials with R book compiled by the R Consortium Tables Working Group.

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 with 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.

Gabriel Becker and Adrian Waddell

2024-06-20