Nothing
## ---- message=FALSE-----------------------------------------------------------
library(rtables)
## -----------------------------------------------------------------------------
d1 <- subset(ex_adsl, AGE < 25)
d1$AGE <- as.factor(d1$AGE)
lyt1 <- basic_table() %>%
split_cols_by("AGE") %>%
analyze("SEX")
build_table(lyt1, d1)
## -----------------------------------------------------------------------------
sd_cutfun <- function(x) {
cutpoints <- c(
min(x),
mean(x) - sd(x),
mean(x) + sd(x),
max(x)
)
names(cutpoints) <- c("", "Low", "Medium", "High")
cutpoints
}
lyt1 <- basic_table() %>%
split_cols_by_cutfun("AGE", cutfun = sd_cutfun) %>%
analyze("SEX")
build_table(lyt1, ex_adsl)
## -----------------------------------------------------------------------------
lyt1 <- basic_table() %>%
split_cols_by_cuts(
"AGE",
cuts = c(0, 30, 60, 100),
cutlabels = c("0-30 y.o.", "30-60 y.o.", "60-100 y.o.")) %>%
analyze("SEX")
build_table(lyt1, ex_adsl)
## -----------------------------------------------------------------------------
picky_splitter <- function(var) {
function(df, spl, vals, labels, trim) {
orig_vals <- vals
if (is.null(vals)) {
vec <- df[[var]]
vals <- if(is.factor(vec)) levels(vec) else unique(vec)
}
if (length(vals) == 1)
do_base_split(spl = spl, df = df, vals = vals, labels = labels, trim = trim)
else
add_overall_level("Overall", label = "All Obs", first = FALSE)(df = df, spl = spl,
vals = orig_vals, trim = trim)
}
}
d1 <- subset(ex_adsl, ARM == "A: Drug X")
d1$ARM <- factor(d1$ARM)
lyt1 <- basic_table() %>%
split_cols_by("ARM", split_fun = picky_splitter("ARM")) %>%
analyze("AGE")
## -----------------------------------------------------------------------------
build_table(lyt1, d1)
## -----------------------------------------------------------------------------
build_table(lyt1, ex_adsl)
## -----------------------------------------------------------------------------
dta_test <- data.frame(
USUBJID = rep(1:6, each = 3),
PARAMCD = rep("lab", 6 * 3),
AVISIT = rep(paste0("V", 1:3), 6),
ARM = rep(LETTERS[1:3], rep(6, 3)),
AVAL = c(9:1, rep(NA, 9)),
CHG = c(1:9, rep(NA, 9))
)
my_afun <- function(x, .spl_context) {
n <- sum(!is.na(x))
meanval <- mean(x, na.rm = TRUE)
sdval <- sd(x, na.rm = TRUE)
## get the split value of the most recent parent
## (row) split above this analyze
val <- .spl_context[nrow(.spl_context), "value"]
## do a silly thing to decide the different format precisiosn
## your real logic would go here
valnum <- min(2L, as.integer(gsub("[^[:digit:]]*", "", val)))
fstringpt <- paste0("xx.", strrep("x", valnum))
fmt_mnsd <- sprintf("%s (%s)", fstringpt, fstringpt)
in_rows(n = n,
"Mean, SD" = c(meanval, sdval),
.formats = c(n = "xx", "Mean, SD" = fmt_mnsd))
}
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("AVISIT") %>%
split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
analyze_colvars(my_afun)
build_table(lyt, dta_test)
## -----------------------------------------------------------------------------
my_afun <- function(x, .var, .spl_context) {
n <- sum(!is.na(x))
meanval <- mean(x, na.rm = TRUE)
sdval <- sd(x, na.rm = TRUE)
## get the split value of the most recent parent
## (row) split above this analyze
val <- .spl_context[nrow(.spl_context), "value"]
## we show it if its not a CHG within V1
show_it <- val != "V1" || .var != "CHG"
## do a silly thing to decide the different format precisiosn
## your real logic would go here
valnum <- min(2L, as.integer(gsub("[^[:digit:]]*", "", val)))
fstringpt <- paste0("xx.", strrep("x", valnum))
fmt_mnsd <- if(show_it) sprintf("%s (%s)", fstringpt, fstringpt) else "xx"
in_rows(n = if(show_it) n, ## NULL otherwise
"Mean, SD" = if(show_it) c(meanval, sdval), ## NULL otherwise
.formats = c(n = "xx", "Mean, SD" = fmt_mnsd)
)
}
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("AVISIT") %>%
split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
analyze_colvars(my_afun)
build_table(lyt, dta_test)
## -----------------------------------------------------------------------------
my_afun <- function(x, .var, ref_rowgroup, .spl_context) {
n <- sum(!is.na(x))
meanval <- mean(x, na.rm = TRUE)
sdval <- sd(x, na.rm = TRUE)
## get the split value of the most recent parent
## (row) split above this analyze
val <- .spl_context[nrow(.spl_context), "value"]
## we show it if its not a CHG within V1
show_it <- val != ref_rowgroup || .var != "CHG"
fmt_mnsd <- if(show_it) "xx.x (xx.x)" else "xx"
in_rows(n = if(show_it) n, ## NULL otherwise
"Mean, SD" = if(show_it) c(meanval, sdval), ## NULL otherwise
.formats = c(n = "xx", "Mean, SD" = fmt_mnsd)
)
}
lyt2 <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("AVISIT") %>%
split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
analyze_colvars(my_afun, extra_args = list(ref_rowgroup = "V1"))
build_table(lyt2, dta_test)
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