library(dplyr)
# split_cols_by_cuts
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
split_cols_by_cuts("AGE",
split_label = "Age",
cuts = c(0, 25, 35, 1000),
cutlabels = c("young", "medium", "old")
) %>%
analyze(c("BMRKR2", "STRATA2")) %>%
append_topleft("counts")
tbl <- build_table(lyt, ex_adsl)
tbl
# split_rows_by_cuts
lyt2 <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by_cuts("AGE",
split_label = "Age",
cuts = c(0, 25, 35, 1000),
cutlabels = c("young", "medium", "old")
) %>%
analyze(c("BMRKR2", "STRATA2")) %>%
append_topleft("counts")
tbl2 <- build_table(lyt2, ex_adsl)
tbl2
# split_cols_by_quartiles
lyt3 <- basic_table() %>%
split_cols_by("ARM") %>%
split_cols_by_quartiles("AGE", split_label = "Age") %>%
analyze(c("BMRKR2", "STRATA2")) %>%
append_topleft("counts")
tbl3 <- build_table(lyt3, ex_adsl)
tbl3
# split_rows_by_quartiles
lyt4 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
split_rows_by_quartiles("AGE", split_label = "Age") %>%
analyze("BMRKR2") %>%
append_topleft(c("Age Quartiles", " Counts BMRKR2"))
tbl4 <- build_table(lyt4, ex_adsl)
tbl4
# split_cols_by_cutfun
cutfun <- function(x) {
cutpoints <- c(
min(x),
mean(x),
max(x)
)
names(cutpoints) <- c("", "Younger", "Older")
cutpoints
}
lyt5 <- basic_table() %>%
split_cols_by_cutfun("AGE", cutfun = cutfun) %>%
analyze("SEX")
tbl5 <- build_table(lyt5, ex_adsl)
tbl5
# split_rows_by_cutfun
lyt6 <- basic_table() %>%
split_cols_by("SEX") %>%
split_rows_by_cutfun("AGE", cutfun = cutfun) %>%
analyze("BMRKR2")
tbl6 <- build_table(lyt6, ex_adsl)
tbl6
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