Nothing
## ---- echo=FALSE--------------------------------------------------------------
knitr::opts_chunk$set(comment = "#")
## ---- message=FALSE-----------------------------------------------------------
library(rtables)
library(dplyr)
## -----------------------------------------------------------------------------
n <- 400
set.seed(1)
df <- tibble(
arm = factor(sample(c("Arm A", "Arm B"), n, replace = TRUE), levels = c("Arm A", "Arm B")),
country = factor(sample(c("CAN", "USA"), n, replace = TRUE, prob = c(.55, .45)), levels = c("CAN", "USA")),
gender = factor(sample(c("Female", "Male"), n, replace = TRUE), levels = c("Female", "Male")),
handed = factor(sample(c("Left", "Right"), n, prob = c(.6, .4), replace = TRUE), levels = c("Left", "Right")),
age = rchisq(n, 30) + 10
) %>% mutate(
weight = 35 * rnorm(n, sd = .5) + ifelse(gender == "Female", 140, 180)
)
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("arm") %>%
split_cols_by("gender") %>%
split_rows_by("country") %>%
summarize_row_groups() %>%
split_rows_by("handed") %>%
summarize_row_groups() %>%
analyze("age", afun = mean, format = "xx.x")
tbl <- build_table(lyt, df)
tbl
## -----------------------------------------------------------------------------
mean(df$age[df$country == "CAN" & df$arm == "Arm A" & df$gender == "Female" & df$handed == "Left"])
## -----------------------------------------------------------------------------
df %>%
filter(country == "CAN", arm == "Arm A", gender == "Female", handed == "Left") %>%
summarise(mean_age = mean(age))
## -----------------------------------------------------------------------------
df %>%
group_by(arm, gender) %>%
filter(country == "CAN", handed == "Left") %>%
summarise(mean_age = mean(age))
## -----------------------------------------------------------------------------
average_age <- df %>%
group_by(arm, gender, country, handed) %>%
summarise(mean_age = mean(age))
average_age
## -----------------------------------------------------------------------------
lyt <- basic_table() %>%
split_cols_by("arm") %>%
split_cols_by("gender") %>%
split_rows_by("country") %>%
split_rows_by("handed") %>%
analyze("age", afun = mean, format = "xx.x")
tbl <- build_table(lyt, df)
tbl
## -----------------------------------------------------------------------------
c_h_df <- df %>%
group_by(arm, gender, country, handed) %>%
summarize(mean = mean(age), c_h_count = n()) %>%
## we need the sum below to *not* be by country, so that we're dividing by the column counts
ungroup(country) %>%
# now the `handed` grouping has been removed, therefore we can calculate percent now:
mutate(n_col = sum(c_h_count), c_h_percent = c_h_count / n_col)
c_h_df
## -----------------------------------------------------------------------------
c_df <- df %>%
group_by(arm, gender, country) %>%
summarize(c_count = n()) %>%
# now the `handed` grouping has been removed, therefore we can calculate percent now:
mutate(n_col = sum(c_count), c_percent = c_count / n_col)
c_df
## -----------------------------------------------------------------------------
full_dplyr <- left_join(c_h_df, c_df) %>% ungroup
## -----------------------------------------------------------------------------
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("arm") %>%
split_cols_by("gender") %>%
split_rows_by("country") %>%
summarize_row_groups() %>%
split_rows_by("handed") %>%
summarize_row_groups() %>%
analyze("age", afun = mean, format = "xx.x")
tbl <- build_table(lyt, df)
tbl
## -----------------------------------------------------------------------------
frm_rtables_h <- cell_values(tbl, rowpath = c("country", "CAN", "handed", "Right", "@content"),
colpath = c("arm", "Arm B", "gender", "Female"))[[1]]
frm_rtables_h
frm_dplyr_h <- full_dplyr %>%
filter(country == "CAN" & handed == "Right" & arm == "Arm B" &
gender == "Female") %>%
select(c_h_count, c_h_percent)
frm_dplyr_h
frm_rtables_c <- cell_values(tbl, rowpath = c("country", "CAN", "@content"),
colpath = c("arm", "Arm A", "gender", "Male"))[[1]]
frm_rtables_c
frm_dplyr_c <- full_dplyr %>%
filter(country == "CAN" & arm == "Arm A" & gender == "Male") %>%
select(c_count, c_percent)
frm_dplyr_c
## ----echo = FALSE, result="hidden"--------------------------------------------
stopifnot(isTRUE(all.equal(frm_rtables_h, unname(unlist(frm_dplyr_h)))))
stopifnot(isTRUE(all.equal(frm_rtables_c, unname(unlist(frm_dplyr_c[1, ])))))
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