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 html, and
pdf, as well Power Point (via conversion to
flextable objects). rtf support is in
development and will be in a future release.
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:
CDISC standardsrtables currently covers virtually all of these
requirements, and further advances remain under active development.
rtables is available on CRAN and you can install the
latest released version with:
install.packages("rtables")or you can install the latest development version directly from GitHub with:
# install.packages("pak")
pak::pak("insightsengineering/rtables")Packaged releases (both those on CRAN and those between official CRAN releases) can be found in the releases list.
To understand how to use this package, please refer to the Introduction
to rtables article, which provides multiple examples of
code implementation.
We first demonstrate with a demographic table-like example and then show the creation of a more complex table.
library(rtables)
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 supported")
}
})
build_table(lyt, ex_adsl)
#> A: Drug X B: Placebo C: Combination
#> ————————————————————————————————————————————————————————————
#> AGE
#> Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72)
#> Median 33.00 35.00 35.00
#> Min - Max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00
#> BMRKR1
#> Mean (sd) 5.97 (3.55) 5.70 (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)
## 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(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
split_cols_by("BMRKR2") %>%
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 (29.0%) 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 (20.0%) 10 (21.7%) 12 (28.6%) 10 (20.8%) 8 (25.8%) 5 (12.5%) 11 (28.2%) 16 (34.0%)
#> 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%) 0 (0.0%) 1 (3.2%) 0 (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 (10.0%) 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%) 0 (0.0%) 0 (0.0%) 1 (2.5%) 1 (2.6%) 0 (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 (0.0%) 0 (0.0%) 2 (5.0%) 0 (0.0%) 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 (20.0%) 4 (8.7%) 5 (11.9%) 6 (12.5%) 6 (19.4%) 6 (15.0%) 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 (5.0%) 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 (0.0%) 1 (2.4%) 0 (0.0%) 0 (0.0%) 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 dataWe 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