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:
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.
rtables is now available on CRAN and you can install the latest released version with:
or you can install the latest stable version directly from GitHub with:
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:
To install the latest development version of the new test version of rtables run
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 50library(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
#> UNKNOWNWe 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
baselR November 2017, this presentation was written for version v0.0.1