yolandazzz13])This vignette demonstrates how to use predefined templates to create new R scripts for generating tables.The core idea is to provide a set of standard templates for common statistical tables, such as those for demography (t_dm_slide). Users can then modify them to suit their specific datasets and analysis needs.
Before generating a template, you can view all supported table templates using the list_all_templates() function:
To get started, first navigate to your autoslider.core repo directory, make sure you have installed the autoslider.core package:
setwd("~/path/to/autoslider.core")
devtools::load_all()
list_all_templates()This will return a character vector of supported templates (e.g., t_dm_slide, g_ae_slide, etc.), each corresponding to a table type stored in the R/ folder of the package.
Use the use_template() function to generate a template script that you can customize. For example:
use_template(template = "t_dm_slide", function_name = "demography")
This will create a file with the demography template at ./programs/R/demography.R, whose contents are displayed below.
library(dplyr)
library(autoslider.core)
demography <- function(adsl,
arm = "TRT01P",
vars = c("AGE", "SEX", "RACE"),
stats = c("median", "range", "count_fraction"),
split_by_study = FALSE,
side_by_side = NULL) {
if (is.null(side_by_side)) {
extra <- NULL
} else {
extra <- c("COUNTRY")
}
for (v in c(vars, extra)) {
assert_that(has_name(adsl, v))
}
adsl1 <- adsl %>%
select(all_of(c("STUDYID", "USUBJID", arm, vars, extra)))
if (!is.null(side_by_side)) {
adsl1$lvl <- "Global"
}
lyt <- build_table_header(adsl1, arm,
split_by_study = split_by_study,
side_by_side = side_by_side
)
lyt <- lyt %>%
analyze_vars(
na.rm = TRUE,
.stats = stats,
denom = "n",
vars = vars,
.formats = c(mean_sd = "xx.xx (xx.xx)", median = "xx.xx"),
var_labels = formatters::var_labels(adsl1)[vars]
)
result <- lyt_to_side_by_side(lyt, adsl1, side_by_side)
if (is.null(side_by_side)) {
# adding "N" attribute
arm <- col_paths(result)[[1]][1]
n_r <- data.frame(
ARM = toupper(names(result@col_info)),
N = col_counts(result) %>% as.numeric()
) %>%
`colnames<-`(c(paste(arm), "N")) %>%
dplyr::arrange(get(arm))
attr(result, "N") <- n_r
}
result@main_title <- "Demographic slide"
result
}Processing our data with the generated t_dm_slide script would look like：
filters::load_filters(filters, overwrite = TRUE)
# read data
data <- list(
"adsl" = eg_adsl %>%
mutate(
FASFL = SAFFL, # add FASFL for illustrative purpose for t_pop_slide
# DISTRTFL is needed for t_ds_slide but is missing in example data
DISTRTFL = sample(c("Y", "N"), size = length(TRT01A), replace = TRUE, prob = c(.1, .9))
) %>%
preprocess_t_ds(), # this preproccessing is required by one of the autoslider.core functions
"adae" = eg_adae,
"adtte" = eg_adtte,
"adrs" = eg_adrs,
"adlb" = eg_adlb
)
demography(data$adsl)
#> Demographic slide
#>
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————
#> A: Drug X B: Placebo C: Combination All Patients
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————
#> Age
#> Median 33.00 35.00 35.00 34.00
#> Min - Max 21.0 - 50.0 21.0 - 62.0 20.0 - 69.0 20.0 - 69.0
#> Sex
#> F 79 (59%) 82 (61.2%) 70 (53%) 231 (57.8%)
#> M 55 (41%) 52 (38.8%) 62 (47%) 169 (42.2%)
#> Race
#> ASIAN 68 (50.7%) 67 (50%) 73 (55.3%) 208 (52%)
#> BLACK OR AFRICAN AMERICAN 31 (23.1%) 28 (20.9%) 32 (24.2%) 91 (22.8%)
#> WHITE 27 (20.1%) 26 (19.4%) 21 (15.9%) 74 (18.5%)
#> AMERICAN INDIAN OR ALASKA NATIVE 8 (6%) 11 (8.2%) 6 (4.5%) 25 (6.2%)
#> MULTIPLE 0 1 (0.7%) 0 1 (0.2%)
#> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 1 (0.7%) 0 1 (0.2%)
#> OTHER 0 0 0 0
#> UNKNOWN 0 0 0 0Following this process, you have successfully generated a reusable demography function that processes clinical data, and convert it into a standard demographic table, including statistical insights like range, median, and count fractions to variables AGE, SEX, and RACE.
Naturally, you may want to customize the script to cater to the dataset characteristics or study objectives. To create multiple versions from the same template for specific analytics requirements, we have made it possible to do so by passing in different function_name. For example:
use_template(template = "l_ae_slide", function_name = "l_ae_slide_001")
# which generates an Adverse Events listing at ./programs/R/l_ae_slide_001.R
use_template(template = "l_ae_slide", function_name = "l_ae_slide_v2")
# which generates an Adverse Events listing at ./programs/R/l_ae_slide_v2.RThe template argument must match one of the entries in list_all_templates().
If a file with the same name already exists, it will not be overwritten unless you set overwrite = TRUE.
In interactive mode, the generated file will open in your R editor for immediate editing.
Feel free to modify the template script to suit your dataset or statistical needs. For instance, you can:
Add or remove table columns
Customize labels and formatting
Modify the summary statistics