Reporting tables with R

The rtables R package was designed to create and display complex tables with R. The cells in an rtable may contain any high-dimensional data structure which can then be displayed with cell-specific formatting instructions. Currently, rtables can be outputted in ascii and html.

Note: we have completely refactored the rtables package which is officially released on CRAN in December 2020. With this significant change please familiarize yourself with the new framework by reading the package vignettes.

rtables is developed and copy written by F. Hoffmann-La Roche and it is released open source under Apache License Version 2.

rtables development is driven by the need to create regulatory ready tables for health authority review. Some of the key requirements for this undertaking are listed below:

  • cell values and their visualization separate (i.e. no string based tables)
    • values need to be programmatically accessible in their non-rounded state for cross-checking
  • multiple values displayed within a cell
  • flexible tabulation framework
  • flexible formatting (cell spans, rounding, alignment, etc.)
  • multiple output formats (html, ascii, latex, pdf, xml)
  • flexible pagination
  • distinguish between name and label in the data structure to work with CDISC standards
  • title, footnotes, cell cell/row/column references

Note that the current state of rtables does not fulfill all of those requirements, however, rtables is still under active development and we are working on adding the missing features.

Installation

rtables is now available on CRAN and you can install the latest released version with:

install.packages("rtables")

or you can install the latest stable version directly from GitHub with:

devtool::install_github("Roche/rtables")

To install a frozen pre-release version of rtables based on the new Layouting and Tabulation API as presented at user!2020 and JSM2020 run the following command in R:

devtools::install_github("roche/rtables", ref="v0.3.3")

To install the latest development version of the new test version of rtables run

devtools::install_github("roche/rtables", ref = "gabe_tabletree_work")

Usage

We first begin with a demographic table alike example and then show the creation of a more complex table.

library(rtables)
#> Loading required package: magrittr

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  analyze(c("AGE", "BMRKR1", "BMRKR2"), function(x, ...) {
    if (is.numeric(x)) {
      in_rows(
        "Mean (sd)" = c(mean(x), sd(x)),
        "Median" = median(x),
        "Min - Max" = range(x),
        .formats = c("xx.xx (xx.xx)", "xx.xx", "xx.xx - xx.xx")
      )
    } else if (is.factor(x) || is.character(x)) {
      in_rows(.list = list_wrap_x(table)(x))
    } else {
      stop("type not supproted")
    }
  })

build_table(lyt, ex_adsl)
#>                A: Drug X      B: Placebo    C: Combination
#> ----------------------------------------------------------
#> AGE                                                       
#>   Mean (sd)   33.77 (6.55)   35.43 (7.9)     35.43 (7.72) 
#>   Median           33             35              35      
#>   Min - Max     21 - 50        21 - 62         20 - 69    
#> BMRKR1                                                    
#>   Mean (sd)   5.97 (3.55)     5.7 (3.31)     5.62 (3.49)  
#>   Median          5.39           4.81            4.61     
#>   Min - Max   0.41 - 17.67   0.65 - 14.24    0.17 - 21.39 
#> BMRKR2                                                    
#>   LOW              50             45              40      
#>   MEDIUM           37             56              42      
#>   HIGH             47             33              50
library(rtables)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

## for simplicity grab non-sparse subset
ADSL = ex_adsl %>% filter(RACE %in% levels(RACE)[1:3])

biomarker_ave = function(x, ...) {
     val = if(length(x) > 0) round(mean(x), 2) else "no data"
     in_rows(
        "Biomarker 1 (mean)" = rcell(val)
     )
}

basic_table() %>%
  split_cols_by("ARM") %>%
  split_cols_by("BMRKR2") %>%
  add_colcounts() %>%
  split_rows_by("RACE", split_fun = trim_levels_in_group("SEX")) %>%
  split_rows_by("SEX") %>%
  summarize_row_groups() %>%
  analyze("BMRKR1", biomarker_ave) %>%
  build_table(ADSL)
#>                                                          A: Drug X                            B: Placebo                           C: Combination           
#>                                                LOW        MEDIUM        HIGH         LOW         MEDIUM       HIGH         LOW         MEDIUM        HIGH   
#>                                               (N=45)      (N=35)       (N=46)       (N=42)       (N=48)      (N=31)       (N=40)       (N=39)       (N=47)  
#> ------------------------------------------------------------------------------------------------------------------------------------------------------------
#> ASIAN                                                                                                                                                       
#>   F                                         13 (28.9%)   9 (25.7%)   19 (41.3%)   9 (21.4%)    18 (37.5%)    9 (29%)    13 (32.5%)   9 (23.1%)    17 (36.2%)
#>     Biomarker 1 (mean)                         5.23        6.17         5.38         5.64         5.55        4.33         5.46         5.48         5.19   
#>   M                                         8 (17.8%)     7 (20%)    10 (21.7%)   12 (28.6%)   10 (20.8%)   8 (25.8%)   5 (12.5%)    11 (28.2%)    16 (34%) 
#>     Biomarker 1 (mean)                         6.77        6.06         5.54         4.9          4.98        6.81         6.53         5.47         4.98   
#>   U                                          1 (2.2%)    1 (2.9%)      0 (0%)       0 (0%)       0 (0%)     1 (3.2%)      0 (0%)      1 (2.6%)     1 (2.1%) 
#>     Biomarker 1 (mean)                         4.68         7.7       no data      no data      no data       6.97       no data       11.93         9.01   
#> BLACK OR AFRICAN AMERICAN                                                                                                                                   
#>   F                                         6 (13.3%)    3 (8.6%)    9 (19.6%)    6 (14.3%)    8 (16.7%)    2 (6.5%)    7 (17.5%)    4 (10.3%)     3 (6.4%) 
#>     Biomarker 1 (mean)                         5.01         7.2         6.79         6.15         5.26        8.57         5.72         5.76         4.58   
#>   M                                         5 (11.1%)    5 (14.3%)    2 (4.3%)     3 (7.1%)    5 (10.4%)    4 (12.9%)    4 (10%)     5 (12.8%)    5 (10.6%) 
#>     Biomarker 1 (mean)                         6.92        5.82        11.66         4.46         6.14        8.47         6.16         5.25         4.83   
#>   U                                           0 (0%)      0 (0%)       0 (0%)       0 (0%)       0 (0%)      0 (0%)      1 (2.5%)     1 (2.6%)      0 (0%)  
#>     Biomarker 1 (mean)                       no data      no data     no data      no data      no data      no data       2.79         9.82       no data  
#>   UNDIFFERENTIATED                           1 (2.2%)     0 (0%)       0 (0%)       0 (0%)       0 (0%)      0 (0%)       2 (5%)       0 (0%)       0 (0%)  
#>     Biomarker 1 (mean)                         9.48       no data     no data      no data      no data      no data       6.46       no data      no data  
#> WHITE                                                                                                                                                       
#>   F                                         6 (13.3%)     7 (20%)     4 (8.7%)    5 (11.9%)    6 (12.5%)    6 (19.4%)    6 (15%)      3 (7.7%)     2 (4.3%) 
#>     Biomarker 1 (mean)                         4.43        7.83         4.52         6.42         5.07        7.83         6.71         5.87         10.7   
#>   M                                          4 (8.9%)    3 (8.6%)     2 (4.3%)    6 (14.3%)     1 (2.1%)    1 (3.2%)      2 (5%)     5 (12.8%)     3 (6.4%) 
#>     Biomarker 1 (mean)                         5.81        7.23         1.39         4.72         4.58        12.87        2.3          5.1          5.98   
#>   U                                          1 (2.2%)     0 (0%)       0 (0%)      1 (2.4%)      0 (0%)      0 (0%)       0 (0%)       0 (0%)       0 (0%)  
#>     Biomarker 1 (mean)                         3.94       no data     no data        3.77       no data      no data     no data      no data      no data  
#> AMERICAN INDIAN OR ALASKA NATIVE                                                                                                                            
#> MULTIPLE                                                                                                                                                    
#> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER                                                                                                                   
#> OTHER                                                                                                                                                       
#> UNKNOWN

Acknowledgements

We would like to thank everyone who has made rtables a better project by providing feedback and improving examples & vignettes. The following list of contributors is alphabetical:

Maximo Carreras, Francois Collins, Saibah Chohan, Tadeusz Lewandowski, Nick Paszty, Nina Qi, Jana Stoilova, Heng Wang, Godwin Yung

Presentations

New (Current) Layouting and Tabulation Framework (v.0.3+)

v0.1.0 and previous