Below is a step-by-step guide demonstrating how to use {rtables.officer} for creating and exporting a clinical trial results table.
library(tern)
library(dplyr)
library(rtables.officer)
# Load example datasets
adsl <- formatters::ex_adae
adlb <- formatters::ex_adlb
# Convert character variables to factors and handle missing levels
adsl <- df_explicit_na(adsl)
adlb <- df_explicit_na(adlb)
# Create a temporary file for the output
tf <- tempfile(fileext = ".docx")adlb_f <- adlb %>%
dplyr::filter(
PARAM %in% c("Alanine Aminotransferase Measurement", "C-Reactive Protein Measurement") &
!(ACTARM == "B: Placebo" & AVISIT == "WEEK 1 DAY 8") &
AVISIT != "SCREENING"
)afun <- function(x, .var, .spl_context, ...) {
n_fun <- sum(!is.na(x), na.rm = TRUE)
mean_sd_fun <- if (n_fun == 0) c(NA, NA) else c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))
median_fun <- if (n_fun == 0) NA else median(x, na.rm = TRUE)
min_max_fun <- if (n_fun == 0) c(NA, NA) else c(min(x), max(x))
is_chg <- .var == "CHG"
is_baseline <- .spl_context$value[which(.spl_context$split == "AVISIT")] == "BASELINE"
if (is_baseline && is_chg) n_fun <- mean_sd_fun <- median_fun <- min_max_fun <- NULL
in_rows(
"n" = n_fun,
"Mean (SD)" = mean_sd_fun,
"Median" = median_fun,
"Min - Max" = min_max_fun,
.formats = list("n" = "xx", "Mean (SD)" = "xx.xx (xx.xx)", "Median" = "xx.xx", "Min - Max" = "xx.xx - xx.xx"),
.format_na_strs = list("n" = "NE", "Mean (SD)" = "NE (NE)", "Median" = "NE", "Min - Max" = "NE - NE")
)
}lyt <- basic_table() %>%
split_cols_by("ACTARM", show_colcounts = TRUE, split_fun = keep_split_levels(levels(adlb_f$ACTARM)[c(1, 2)])) %>%
split_rows_by("PARAM",
split_fun = drop_split_levels, label_pos = "topleft",
split_label = obj_label(adlb_f$PARAM), page_by = TRUE
) %>%
split_rows_by("AVISIT",
split_fun = drop_split_levels, label_pos = "topleft",
split_label = obj_label(adlb_f$AVISIT)
) %>%
split_cols_by_multivar(
vars = c("AVAL", "CHG"),
varlabels = c("Value at Visit", "Change from Baseline")
) %>%
analyze_colvars(afun = afun)result <- build_table(lyt, adlb_f)
result
# A: Drug X B: Placebo
# Parameter (N=1608) (N=1340)
# Analysis Visit Value at Visit Change from Baseline Value at Visit Change from Baseline
# ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
# Alanine Aminotransferase Measurement
# BASELINE
# n 134 134
# Mean (SD) 49.57 (8.32) 50.31 (8.34)
# Median 49.57 50.15
# Min - Max 23.97 - 70.85 26.22 - 79.12
# WEEK 1 DAY 8
# n 134 134 0 0
# Mean (SD) 48.60 (7.96) -0.97 (11.70) NE (NE) NE (NE)
# Median 48.42 -1.02 NE NE
# Min - Max 27.71 - 64.64 -25.05 - 39.39 NE - NE NE - NE
# WEEK 2 DAY 15
# n 134 134 134 134
# Mean (SD) 49.41 (8.47) -0.16 (12.01) 50.25 (8.53) -0.06 (12.53)
# Median 48.34 0.08 49.97 -0.74
# Min - Max 24.34 - 71.06 -26.31 - 34.35 24.38 - 71.07 -36.75 - 28.78
# WEEK 3 DAY 22
# n 134 134 134 134
# Mean (SD) 50.26 (7.51) 0.69 (11.52) 49.67 (7.74) -0.63 (11.21)
# Median 50.09 0.90 49.75 -0.36
# Min - Max 33.04 - 68.98 -32.07 - 32.51 33.71 - 66.49 -37.04 - 30.00
# WEEK 4 DAY 29
# n 134 134 134 134
# Mean (SD) 50.70 (9.17) 1.13 (13.01) 49.26 (8.69) -1.04 (12.59)
# Median 49.53 0.71 48.30 -2.37
# Min - Max 29.75 - 78.99 -34.00 - 37.59 32.99 - 73.97 -33.16 - 34.47
# WEEK 5 DAY 36
# n 134 134 134 134
# Mean (SD) 50.84 (7.85) 1.26 (12.11) 49.72 (8.45) -0.59 (12.63)
# Median 51.44 1.85 50.23 1.46
# Min - Max 31.89 - 70.34 -32.06 - 32.18 30.64 - 68.11 -38.08 - 25.27
# C-Reactive Protein Measurement
# BASELINE
# n 134 134
# Mean (SD) 48.95 (9.44) 50.08 (7.87)
# Median 49.55 50.12
# Min - Max 28.96 - 75.23 29.91 - 73.35
# WEEK 1 DAY 8
# n 134 134 0 0
# Mean (SD) 51.86 (8.08) 2.91 (12.48) NE (NE) NE (NE)
# Median 51.23 2.94 NE NE
# Min - Max 28.47 - 72.90 -28.23 - 41.19 NE - NE NE - NE
# WEEK 2 DAY 15
# n 134 134 134 134
# Mean (SD) 49.75 (8.27) 0.80 (12.55) 50.87 (7.42) 0.79 (10.69)
# Median 50.44 0.48 51.51 0.82
# Min - Max 29.02 - 69.08 -34.08 - 30.43 30.70 - 67.70 -22.15 - 27.16
# WEEK 3 DAY 22
# n 134 134 134 134
# Mean (SD) 50.06 (8.32) 1.12 (12.36) 49.27 (7.52) -0.81 (10.62)
# Median 49.93 3.14 48.78 0.15
# Min - Max 26.70 - 69.37 -36.68 - 31.78 30.70 - 67.76 -30.71 - 29.92
# WEEK 4 DAY 29
# n 134 134 134 134
# Mean (SD) 51.61 (8.13) 2.66 (12.22) 49.47 (8.21) -0.61 (11.10)
# Median 52.26 3.17 48.81 -1.27
# Min - Max 31.45 - 71.21 -31.34 - 41.61 27.07 - 67.32 -24.90 - 23.50
# WEEK 5 DAY 36
# n 134 134 134 134
# Mean (SD) 49.71 (8.76) 0.76 (13.36) 50.83 (7.59) 0.75 (11.28)
# Median 49.22 -0.25 50.25 0.46
# Min - Max 25.97 - 69.62 -39.98 - 33.63 33.32 - 70.27 -25.59 - 33.12Assign titles and footers:
main_title(result) <- "Alanine Aminotransferase Measurement"
subtitles(result) <- c("This is a subtitle.", "This is another subtitle.")
main_footer(result) <- "This is a demo table for illustration purpose."
prov_footer(result) <- "Program: demo_poc_docx.R\nDate: 2024-11-06\nVersion: 0.0.1\n"flextable and Export to Word Convert the
table to a flextable object and export it to a Word
document:flx_res <- tt_to_flextable(result)
export_as_docx(flx_res,
file = tf,
section_properties = section_properties_default(orientation = "landscape")
)
flx_resYou can further customize your tables, such as setting column widths, handling pagination, and more.
cw <- propose_column_widths(result)
cw <- cw / sum(cw)
cw <- c(0.6, 0.1, 0.1, 0.1, 0.1)
spd <- section_properties_default(orientation = "landscape")
fin_cw <- cw * spd$page_size$width / 2 / sum(cw)
flex_tbl <- tt_to_flextable(result,
total_page_width = spd$page_size$width / 2,
counts_in_newline = TRUE,
autofit_to_page = FALSE,
bold_titles = TRUE,
colwidths = cw
)
export_as_docx(flex_tbl, file = tf)
# Warning in FUN(X[[i]], ...): The total table width does not match the page
# width. The column widths will be resized to fit the page. Please consider
# modifying the parameter total_page_width in tt_to_flextable().
flex_tblsection_div = <chr>)tbl <- basic_table() %>%
split_cols_by("ACTARM") %>%
split_rows_by("PARAM", split_fun = drop_split_levels, section_div = "-") %>%
split_rows_by("AVISIT", split_fun = drop_split_levels, section_div = " ") %>%
split_cols_by_multivar(
vars = c("AVAL", "CHG"), varlabels = c("Value at Visit", "Change from Baseline")
) %>%
analyze_colvars(afun = afun) %>%
build_table(adlb_f)
flx_res <- tt_to_flextable(tbl)
export_as_docx(flx_res, file = tf)
flx_res