tern
functionstern functionsEvery function in the tern package is designed to have a
certain structure that can cooperate well with every user’s need, while
maintaining a consistent and predictable behavior. This document will
guide you through an example function in the package, explaining the
purpose of many of its building blocks and how they can be used.
As we recently worked on it we will consider
summarize_change() as an example. This function is used to
calculate the change from a baseline value for a given variable. A
realistic example can be found in LBT03
from the TLG-catalog.
summarize_change() is the main function that is
available to the user. You can find lists of these functions in
?tern::analyze_functions. All of these are build around
rtables::analyze() function, which is the core analysis
function in rtables. All these wrapper functions call
specific analysis functions (always written as a_*) that
are meant to handle the statistic functions (always written as
s_*) and format the results with the
rtables::in_row() function. We can summarize this structure
as follows:
summarize_change() (1)->
a_change_from_baseline() (2)->
[s_change_from_baseline() +
rtables::in_row()]
The main questions that may arise are:
NA.Data set and library loading.
library(dplyr)
library(tern)
## Fabricate dataset
dta_test <- data.frame(
USUBJID = rep(1:6, each = 3),
AVISIT = rep(paste0("V", 1:3), 6),
ARM = rep(LETTERS[1:3], rep(6, 3)),
AVAL = c(9:1, rep(NA, 9))
) %>%
mutate(ABLFLL = AVISIT == "V1") %>%
group_by(USUBJID) %>%
mutate(
BLVAL = AVAL[ABLFLL],
CHG = AVAL - BLVAL
) %>%
ungroup()Classic use of summarize_change().
fix_layout <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("AVISIT")
# Dealing with NAs: na_rm = TRUE
fix_layout %>%
summarize_change("CHG", variables = list(value = "AVAL", baseline_flag = "ABLFLL")) %>%
build_table(dta_test) %>%
print()
#> A B C
#> ————————————————————————————————————————————————
#> V1
#> n 2 1 0
#> Mean (SD) 7.50 (2.12) 3.00 (NA) NA
#> Median 7.50 3.00 NA
#> Min - Max 6.00 - 9.00 3.00 - 3.00 NA
#> V2
#> n 2 1 0
#> Mean (SD) -1.00 (0.00) -1.00 (NA) NA
#> Median -1.00 -1.00 NA
#> Min - Max -1.00 - -1.00 -1.00 - -1.00 NA
#> V3
#> n 2 1 0
#> Mean (SD) -2.00 (0.00) -2.00 (NA) NA
#> Median -2.00 -2.00 NA
#> Min - Max -2.00 - -2.00 -2.00 - -2.00 NA
# Dealing with NAs: na_rm = FALSE
fix_layout %>%
summarize_change("CHG", variables = list(value = "AVAL", baseline_flag = "ABLFLL"), na_rm = FALSE) %>%
build_table(dta_test) %>%
print()
#> A B C
#> ————————————————————————————————————————————————
#> V1
#> n 2 1 0
#> Mean (SD) 7.50 (2.12) 3.00 (NA) NA
#> Median 7.50 3.00 NA
#> Min - Max 6.00 - 9.00 3.00 - 3.00 NA
#> V2
#> n 2 1 0
#> Mean (SD) -1.00 (0.00) -1.00 (NA) NA
#> Median -1.00 -1.00 NA
#> Min - Max -1.00 - -1.00 -1.00 - -1.00 NA
#> V3
#> n 2 1 0
#> Mean (SD) -2.00 (0.00) -2.00 (NA) NA
#> Median -2.00 -2.00 NA
#> Min - Max -2.00 - -2.00 -2.00 - -2.00 NA
# changing the NA string (it is done on all levels)
fix_layout %>%
summarize_change("CHG", variables = list(value = "AVAL", baseline_flag = "ABLFLL"), na_str = "my_na") %>%
build_table(dta_test) %>%
print()
#> A B C
#> ———————————————————————————————————————————————————
#> V1
#> n 2 1 0
#> Mean (SD) 7.50 (2.12) 3.00 (my_na) my_na
#> Median 7.50 3.00 my_na
#> Min - Max 6.00 - 9.00 3.00 - 3.00 my_na
#> V2
#> n 2 1 0
#> Mean (SD) -1.00 (0.00) -1.00 (my_na) my_na
#> Median -1.00 -1.00 my_na
#> Min - Max -1.00 - -1.00 -1.00 - -1.00 my_na
#> V3
#> n 2 1 0
#> Mean (SD) -2.00 (0.00) -2.00 (my_na) my_na
#> Median -2.00 -2.00 my_na
#> Min - Max -2.00 - -2.00 -2.00 - -2.00 my_na.formats, .labels, and
.indent_mods depend on the names of .stats.
Here is how you can change the default formatting.
# changing n count format and label and indentation
fix_layout %>%
summarize_change("CHG",
variables = list(value = "AVAL", baseline_flag = "ABLFLL"),
.stats = c("n", "mean"), # reducing the number of stats for visual appreciation
.formats = c(n = "xx.xx"),
.labels = c(n = "NnNn"),
.indent_mods = c(n = 5), na_str = "nA"
) %>%
build_table(dta_test) %>%
print()
#> A B C
#> —————————————————————————————————————
#> V1
#> NnNn 2.00 1.00 0.00
#> Mean 7.5 3.0 nA
#> V2
#> NnNn 2.00 1.00 0.00
#> Mean -1.0 -1.0 nA
#> V3
#> NnNn 2.00 1.00 0.00
#> Mean -2.0 -2.0 nAWhat if I want something special for the format?
# changing n count format and label and indentation
fix_layout %>%
summarize_change("CHG",
variables = list(value = "AVAL", baseline_flag = "ABLFLL"),
.stats = c("n", "mean"), # reducing the number of stats for visual appreciation
.formats = c(n = function(x, ...) as.character(x * 100))
) %>% # Note you need ...!!!
build_table(dta_test) %>%
print()
#> A B C
#> —————————————————————————
#> V1
#> n 200 100 0
#> Mean 7.5 3.0 NA
#> V2
#> n 200 100 0
#> Mean -1.0 -1.0 NA
#> V3
#> n 200 100 0
#> Mean -2.0 -2.0 NAAdding a custom statistic (and custom format):
# changing n count format and label and indentation
fix_layout %>%
summarize_change(
"CHG",
variables = list(value = "AVAL", baseline_flag = "ABLFLL"),
.stats = c("n", "my_stat" = function(df, ...) {
a <- mean(df$AVAL, na.rm = TRUE)
b <- list(...)$.N_row # It has access at all `?rtables::additional_fun_params`
a / b
}),
.formats = c("my_stat" = function(x, ...) sprintf("%.2f", x))
) %>%
build_table(dta_test)
#> A B C
#> ————————————————————————————
#> V1
#> n 2 1 0
#> my_stat 1.25 0.50 NA
#> V2
#> n 2 1 0
#> my_stat 1.08 0.33 NA
#> V3
#> n 2 1 0
#> my_stat 0.92 0.17 NAIn all of these layers there are specific parameters that need to be
available, and, while rtables has multiple way to handle
formatting and NA values, we had to decide how to correctly
handle these and additional extra arguments. We follow the following
scheme:
Level 1: summarize_change(): all parameters without a
starting dot .* are used or added to
extra_args. Specifically, here we solve NA
values by using inclNAs option in
rtables::analyze(). This will add to ...
na.rm = inclNAs. Also na_str is here set. We
may want to be statistic dependent in the future, but we still need to
think how to accomplish that. We add the
rtables::additional_fun_params to the analysis function so
to make them available as ... in the next level.
Level 2: a_change_from_baseline(): all parameters
starting with a dot . are used. Mainly .stats,
.formats, .labels, and
.indent_mods are used. We also add
extra_afun_params to the ... list for the
statistical function. Notice the handling for additional parameters in
the do.call() function.