R/data_des.r
In shanquan0301/DataDescription: Structure the Data as Table 1 Used in Publication
Defines functions
cate_des
data_des
Documented in
data_des
#' @title Structure the Data as Table 1 Used in Publication.
#' @description Category date will be presented as N(percentage). continuous data will be presented in a customized form.
#' @param data data table.
#' @param row_var The variable shown on the row, can't be NUll.
#' @param col_var The variable shown on the column, can be NULL.
#' @param col_perc When presented the category variable, the percentage should be calculated based on row or column. Default is column.
#' @param fun The function used on continuous variable.
#' @param comb_sym It determines how the continuous variable will be presented.
#' @param round_cate Decimal of category variable. Default is 1.
#' @param round_cont Decimal of continuous variable. Default is 2.
#' @param p_value If calculate the p value
#' @param test_name_cont test on continuous variable
#' @param test_name_cate test on category variable
#' @param ... Arguments given to \code{"fun"}
#' @author Shanquan CHEN \email{shanquan0301@gmial.com}
#' @keywords data description
#' @examples
##' data(mtcars)
##' mtcars$judge <- TRUE
##' mtcars$judge[3:7] <- FALSE
##' mtcars$judge2 <- "TRUE"
##' mtcars$judge2[3:7] <- "FALSE"
##'
##' # continuous varialbe was shown in mean(sd)
##' res_tab <- data_des(data = mtcars,
##' row_var = c("vs", "mpg", "wt", "gear", "judge", "judge2"),
##' fun = c("mean", "sd"),
##' comb_sym = c("(", ")"),
##' round_cate = 2,
##' round_cont = 3)
##'
##' res_tab
##'
##' # continuous varialbe was shown in median[25% quantile, 75% quantile]
##' res_tab <- data_des(data = mtcars,
##' row_var = c("vs", "mpg", "wt", "gear", "judge", "judge2"),
##' fun = c("median", "quantile"),
##' comb_sym = c("[", "]"),
##' probs = c(0.25, 0.75))
##'
##' res_tab
##'
##' # continuous varialbe was shown in mean[25% quantile, 75% quantile]
##' res_tab <- data_des(data = mtcars,
##' row_var = c("vs", "mpg", "wt", "gear", "judge", "judge2"),
##' col_var = "am",
##' fun = c("mean", "quantile"),
##' comb_sym = c("[", "]"),
##' probs = c(0.25, 0.75),
##' round_cate = 2,
##' round_cont = 3)
##'
##' res_tab
##'
#' @import dplyr stringr smd
#' @importFrom stats sd quantile
#' @importFrom magrittr %$%
#' @importFrom tidyr spread
#' @export data_des
#function for all-------
data_des <- function(data,
row_var = c("vs", "mpg"),
col_var = NULL,
col_perc = TRUE,
fun = c("mean", "sd"),
comb_sym = c(" (", ")"),
round_cate = 1,
round_cont = 2,
p_value = TRUE,
weight = NULL,
test_name_cont = "t.test",
test_name_cate = "chisq.test",
...){
class(data) <- class(data)[which(class(data) != "rowwise_df")]
if (is.null(col_var)){
res_f <- data.frame(Variable = character(0), comb = character(0))
} else {
if(p_value == TRUE){res_f <- data.frame(Variable = character(0),
col_var = character(0),
comb = character(0),
test_name = character(0),
test_value = numeric(0),
p_value = numeric(0))}
if(p_value != TRUE){res_f <- data.frame(Variable = character(0),
col_var = character(0),
comb = character(0))}
}
for (var in row_var){
print(var)
var <- as.name(var)
m_len <- data %$% length(unique(eval(var)))
m_class <- data %$% class(eval(var))
if(m_len == 2 | m_class[1] %in% c("character", "logical", "factor", "ordered")){
res <- cate_des(data = data,
row_var = as.character(var),
col_var = col_var,
col_perc = col_perc,
p_value = p_value,
round_cate = round_cate,
weight = weight,
test_name = test_name_cate)
} else {
res <- cont_des(data = data,
row_var = as.character(var),
col_var = col_var,
fun = fun,
p_value = p_value,
weight = weight,
comb_sym = comb_sym,
round_cont = round_cont,
test_name = test_name_cont,
...)
}
if(is.null(col_var)){
res <- res %>% select(Variable, comb)
} else {
if(p_value == TRUE){res <- res %>% select(Variable, as.character(col_var), comb, test_name, test_value, p_value)}
if(p_value != TRUE){res <- res %>% select(Variable, as.character(col_var), comb)}
names(res)[2] <- "col_var"
}
res_f <- rbind(res_f, res)
}
if(is.null(col_var)){
return(res_f)
} else {
u_Variable <- unique(res_f$Variable)
res_f <- res_f %>% spread(key = col_var, value = comb)
#res_f <- res_f %>% filter (!is.na(Variable), !is.na(age_g)) %>% spread(key = col_var, value = comb)
res_f <- res_f[match(u_Variable, res_f$Variable), ]
names(res_f)[which(names(res_f) == "<NA>")] <- "All"
names(res_f)[-1] <- str_c(as.character(col_var), " (= ", names(res_f)[-1], ")")
names(res_f)[str_detect(names(res_f), "test_value")] <- "test_value"
names(res_f)[str_detect(names(res_f), "test_name")] <- "test_name"
names(res_f)[str_detect(names(res_f), "p_value")] <- "p_value"
if("p_value" %in% names(res_f)){
res_f <- res_f %>% select(Variable, starts_with(as.character(col_var)), test_name, test_value, p_value)
}
return(res_f)
}
}
#function for category variable------
cate_des <- function(data,
row_var = "gear",
col_var = "vs",
col_perc = TRUE,
round_cate = 1,
p_value = TRUE,
weight = NUll,
test_name = "chisq.test"){
class(data) <- class(data)[which(class(data) != "rowwise_df")]
row_var <- as.name(row_var)
m_class <- data %$% class(eval(row_var))
if("ordered" %in% m_class) {test_name <- "prop.trend.test"}
#if col_var == NULL, it means no column
if(is.null(col_var)){
res_2 <- data %>%
filter(!is.na(!!row_var)) %>%
group_by(!!row_var) %>%
summarise(n = n()) %>%
mutate(N = sum(n),
`Percentage(%)` = sprintf(str_glue("%.{round_cate}f"), round(n/N * 100, round_cate)))
res <- res_2
} else {
col_var <- as.name(col_var)
if (col_perc){
res_1 <- data %>%
filter(!is.na(!!row_var), !is.na(!!col_var)) %>%
group_by(!!row_var, !!col_var) %>%
summarise(n = n()) %>%
group_by(!!col_var) %>%
mutate(N = sum(n),
`Percentage(%)` = sprintf(str_glue("%.{round_cate}f"), round(n/N * 100, round_cate)))
} else {
res_1 <- data %>%
filter(!is.na(!!row_var), !is.na(!!col_var)) %>%
group_by(!!row_var, !!col_var) %>%
summarise(n = n()) %>%
mutate(N = sum(n),
`Percentage(%)` = sprintf(str_glue("%.{round_cate}f"), round(n/N * 100, round_cate)))
}
res_2 <- data %>%
filter(!is.na(!!row_var)) %>%
group_by(!!row_var) %>%
summarise(n = n()) %>%
mutate(N = sum(n),
`Percentage(%)` = sprintf(str_glue("%.{round_cate}f"), round(n/N * 100, round_cate)))
res <- bind_rows(res_1, res_2)
}
#combine the row
res <- res %>% rowwise() %>%
mutate(comb = str_c(n, " (", `Percentage(%)`, "%)"),
Variable = str_c(as.character(row_var), " (= ", !!row_var, ")")
)
#add p_value
if(!is.null(col_var) & p_value == TRUE){
m_tab <- eval(parse(text =str_glue("data %$% table(as.character({row_var}), as.character({col_var}))")))
events <- m_tab[, 1]
trials <- rowSums(m_tab)
if(test_name %in% c("smd")){
if (is.null(weight)) {
mdat_test <- eval(parse(text = str_glue("data %$% smd(x = {row_var}, g = {col_var}, std.error = TRUE)")))
}
if (!is.null(weight)) {
mdat_test <- eval(parse(text = str_glue("data %$% smd(x = {row_var}, g = {col_var}, w = {weight}, std.error = TRUE)")))
}
}
if (test_name == "chisq.test"){
mdat_test <- eval(parse(text = str_glue("{test_name}(m_tab)")))
}
if (test_name == "prop.trend.test"){
mdat_test <- prop.trend.test(events, trials)
}
if(test_name == "smd") {
res$test_name <- test_name
#mdat_test <- unlist(summary(mdat_test))
res$test_value <- mdat_test$estimate
res$p_value <- mdat_test$std.error
}
if(test_name %in% c("chisq.test", "prop.trend.test")){
res$test_name <- test_name
res$test_value <- mdat_test$statistic
res$p_value <- mdat_test$p.value
}
}
names(res)[1] <- "cate"
return(res)
}
#function for continuous variable-------
cont_des <- function(data,
row_var = "mpg",
col_var = "vs",
fun = c("mean", "sd"),
comb_sym = c(" (", ")"),
round_cont = 2,
p_value = TRUE,
weight = NULL,
test_name = "t.test",
...){
class(data) <- class(data)[which(!class(data) %in% c("rowwise_df", "grouped_df"))]
row_var <- as.name(row_var)
n_col <- eval(parse(text = str_glue("length(na.omit(unique(data${col_var})))")))
if(n_col > 2) {test_name <- "aov"}
if(is.null(col_var)){
res_2 <- data %>%
summarise(mean = do.call(fun[1], args = list(!!row_var, na.rm = TRUE)),
sd = str_c(sprintf(str_glue("%.{round_cont}f"),
round(do.call(fun[2],
args = list(!!row_var,
na.rm = TRUE,
...)),
round_cont)),
collapse = ", "))
res <- res_2
} else {
col_var <- as.name(col_var)
res_1 <- data %>%
filter(!is.na(!!col_var)) %>%
group_by(!!col_var) %>%
summarise(mean = do.call(fun[1], args = list(!!row_var, na.rm = TRUE)),
sd = str_c(sprintf(str_glue("%.{round_cont}f"),
round(do.call(fun[2],
args = list(!!row_var,
na.rm = TRUE,
...)),
round_cont)),
collapse = ", "))
res_2 <- data %>%
summarise(mean = do.call(fun[1], args = list(!!row_var, na.rm = TRUE)),
sd = str_c(sprintf(str_glue("%.{round_cont}f"),
round(do.call(fun[2],
args = list(!!row_var,
na.rm = TRUE,
...)),
round_cont)),
collapse = ", "))
res <- bind_rows(res_1, res_2)
}
if (class(res$sd) == "numeric"){
res <- res %>% rowwise() %>%
mutate(Variable = as.character(row_var),
comb = str_c(sprintf(str_glue("%.{round_cont}f"), round(mean, round_cont)),
comb_sym[1],
sprintf(str_glue("%.{round_cont}f"), round(sd, round_cont)),
comb_sym[2]))
} else {
res <- res %>% rowwise() %>%
mutate(Variable = as.character(row_var),
comb = str_c(sprintf(str_glue("%.{round_cont}f"), round(mean, round_cont)),
comb_sym[1],
sd,
comb_sym[2]))
}
#add p_value
if(!is.null(col_var) & p_value == TRUE){
if(test_name %in% c("t.test", "aov", "wilcox.test")){
mdat_test <- eval(parse(text = str_glue("{test_name}({row_var} ~ {col_var}, data = data)")))
}
if(test_name %in% "smd"){
if (is.null(weight)){
mdat_test <- eval(parse(text = str_glue("data %$% smd(x = {row_var}, g = {col_var}, std.error = TRUE)")))
}
if (!is.null(weight)){
mdat_test <- eval(parse(text = str_glue("data %$% smd(x = {row_var}, g = {col_var}, w = {weight}, std.error = TRUE)")))
}
}
if(test_name == "aov") {
res$test_name <- test_name
mdat_test <- unlist(summary(mdat_test))
res$test_value <- mdat_test["F value1"]
res$p_value <- mdat_test["Pr(>F)1"]
}
if(test_name == "smd") {
res$test_name <- test_name
#mdat_test <- unlist(summary(mdat_test))
res$test_value <- mdat_test$estimate
res$p_value <- mdat_test$std.error
}
if(!(test_name %in% c("aov", "smd"))) {
res$test_name <- test_name
res$test_value <- mdat_test$statistic
res$p_value <- mdat_test$p.value
}
}
n_loc <- which(names(res) == "mean")
names(res)[n_loc:(n_loc + 1)] <- fun
return(res)
}
utils::globalVariables(c("N", "Percentage(%)", "Variable", "comb"))
shanquan0301/DataDescription documentation built on March 13, 2024, 2:49 a.m.
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Defines functions cate_des data_des
Documented in data_des
#' @title Structure the Data as Table 1 Used in Publication.
#' @description Category date will be presented as N(percentage). continuous data will be presented in a customized form.
#' @param data data table.
#' @param row_var The variable shown on the row, can't be NUll.
#' @param col_var The variable shown on the column, can be NULL.
#' @param col_perc When presented the category variable, the percentage should be calculated based on row or column. Default is column.
#' @param fun The function used on continuous variable.
#' @param comb_sym It determines how the continuous variable will be presented.
#' @param round_cate Decimal of category variable. Default is 1.
#' @param round_cont Decimal of continuous variable. Default is 2.
#' @param p_value If calculate the p value
#' @param test_name_cont test on continuous variable
#' @param test_name_cate test on category variable
#' @param ... Arguments given to \code{"fun"}
#' @author Shanquan CHEN \email{shanquan0301@gmial.com}
#' @keywords data description
#' @examples
##' data(mtcars)
##' mtcars$judge <- TRUE
##' mtcars$judge[3:7] <- FALSE
##' mtcars$judge2 <- "TRUE"
##' mtcars$judge2[3:7] <- "FALSE"
##'
##' # continuous varialbe was shown in mean(sd)
##' res_tab <- data_des(data = mtcars,
##' row_var = c("vs", "mpg", "wt", "gear", "judge", "judge2"),
##' fun = c("mean", "sd"),
##' comb_sym = c("(", ")"),
##' round_cate = 2,
##' round_cont = 3)
##'
##' res_tab
##'
##' # continuous varialbe was shown in median[25% quantile, 75% quantile]
##' res_tab <- data_des(data = mtcars,
##' row_var = c("vs", "mpg", "wt", "gear", "judge", "judge2"),
##' fun = c("median", "quantile"),
##' comb_sym = c("[", "]"),
##' probs = c(0.25, 0.75))
##'
##' res_tab
##'
##' # continuous varialbe was shown in mean[25% quantile, 75% quantile]
##' res_tab <- data_des(data = mtcars,
##' row_var = c("vs", "mpg", "wt", "gear", "judge", "judge2"),
##' col_var = "am",
##' fun = c("mean", "quantile"),
##' comb_sym = c("[", "]"),
##' probs = c(0.25, 0.75),
##' round_cate = 2,
##' round_cont = 3)
##'
##' res_tab
##'
#' @import dplyr stringr smd
#' @importFrom stats sd quantile
#' @importFrom magrittr %$%
#' @importFrom tidyr spread
#' @export data_des
#function for all-------
data_des <- function(data,
row_var = c("vs", "mpg"),
col_var = NULL,
col_perc = TRUE,
fun = c("mean", "sd"),
comb_sym = c(" (", ")"),
round_cate = 1,
round_cont = 2,
p_value = TRUE,
weight = NULL,
test_name_cont = "t.test",
test_name_cate = "chisq.test",
...){
class(data) <- class(data)[which(class(data) != "rowwise_df")]
if (is.null(col_var)){
res_f <- data.frame(Variable = character(0), comb = character(0))
} else {
if(p_value == TRUE){res_f <- data.frame(Variable = character(0),
col_var = character(0),
comb = character(0),
test_name = character(0),
test_value = numeric(0),
p_value = numeric(0))}
if(p_value != TRUE){res_f <- data.frame(Variable = character(0),
col_var = character(0),
comb = character(0))}
}
for (var in row_var){
print(var)
var <- as.name(var)
m_len <- data %$% length(unique(eval(var)))
m_class <- data %$% class(eval(var))
if(m_len == 2 | m_class[1] %in% c("character", "logical", "factor", "ordered")){
res <- cate_des(data = data,
row_var = as.character(var),
col_var = col_var,
col_perc = col_perc,
p_value = p_value,
round_cate = round_cate,
weight = weight,
test_name = test_name_cate)
} else {
res <- cont_des(data = data,
row_var = as.character(var),
col_var = col_var,
fun = fun,
p_value = p_value,
weight = weight,
comb_sym = comb_sym,
round_cont = round_cont,
test_name = test_name_cont,
...)
}
if(is.null(col_var)){
res <- res %>% select(Variable, comb)
} else {
if(p_value == TRUE){res <- res %>% select(Variable, as.character(col_var), comb, test_name, test_value, p_value)}
if(p_value != TRUE){res <- res %>% select(Variable, as.character(col_var), comb)}
names(res)[2] <- "col_var"
}
res_f <- rbind(res_f, res)
}
if(is.null(col_var)){
return(res_f)
} else {
u_Variable <- unique(res_f$Variable)
res_f <- res_f %>% spread(key = col_var, value = comb)
#res_f <- res_f %>% filter (!is.na(Variable), !is.na(age_g)) %>% spread(key = col_var, value = comb)
res_f <- res_f[match(u_Variable, res_f$Variable), ]
names(res_f)[which(names(res_f) == "<NA>")] <- "All"
names(res_f)[-1] <- str_c(as.character(col_var), " (= ", names(res_f)[-1], ")")
names(res_f)[str_detect(names(res_f), "test_value")] <- "test_value"
names(res_f)[str_detect(names(res_f), "test_name")] <- "test_name"
names(res_f)[str_detect(names(res_f), "p_value")] <- "p_value"
if("p_value" %in% names(res_f)){
res_f <- res_f %>% select(Variable, starts_with(as.character(col_var)), test_name, test_value, p_value)
}
return(res_f)
}
}
#function for category variable------
cate_des <- function(data,
row_var = "gear",
col_var = "vs",
col_perc = TRUE,
round_cate = 1,
p_value = TRUE,
weight = NUll,
test_name = "chisq.test"){
class(data) <- class(data)[which(class(data) != "rowwise_df")]
row_var <- as.name(row_var)
m_class <- data %$% class(eval(row_var))
if("ordered" %in% m_class) {test_name <- "prop.trend.test"}
#if col_var == NULL, it means no column
if(is.null(col_var)){
res_2 <- data %>%
filter(!is.na(!!row_var)) %>%
group_by(!!row_var) %>%
summarise(n = n()) %>%
mutate(N = sum(n),
`Percentage(%)` = sprintf(str_glue("%.{round_cate}f"), round(n/N * 100, round_cate)))
res <- res_2
} else {
col_var <- as.name(col_var)
if (col_perc){
res_1 <- data %>%
filter(!is.na(!!row_var), !is.na(!!col_var)) %>%
group_by(!!row_var, !!col_var) %>%
summarise(n = n()) %>%
group_by(!!col_var) %>%
mutate(N = sum(n),
`Percentage(%)` = sprintf(str_glue("%.{round_cate}f"), round(n/N * 100, round_cate)))
} else {
res_1 <- data %>%
filter(!is.na(!!row_var), !is.na(!!col_var)) %>%
group_by(!!row_var, !!col_var) %>%
summarise(n = n()) %>%
mutate(N = sum(n),
`Percentage(%)` = sprintf(str_glue("%.{round_cate}f"), round(n/N * 100, round_cate)))
}
res_2 <- data %>%
filter(!is.na(!!row_var)) %>%
group_by(!!row_var) %>%
summarise(n = n()) %>%
mutate(N = sum(n),
`Percentage(%)` = sprintf(str_glue("%.{round_cate}f"), round(n/N * 100, round_cate)))
res <- bind_rows(res_1, res_2)
}
#combine the row
res <- res %>% rowwise() %>%
mutate(comb = str_c(n, " (", `Percentage(%)`, "%)"),
Variable = str_c(as.character(row_var), " (= ", !!row_var, ")")
)
#add p_value
if(!is.null(col_var) & p_value == TRUE){
m_tab <- eval(parse(text =str_glue("data %$% table(as.character({row_var}), as.character({col_var}))")))
events <- m_tab[, 1]
trials <- rowSums(m_tab)
if(test_name %in% c("smd")){
if (is.null(weight)) {
mdat_test <- eval(parse(text = str_glue("data %$% smd(x = {row_var}, g = {col_var}, std.error = TRUE)")))
}
if (!is.null(weight)) {
mdat_test <- eval(parse(text = str_glue("data %$% smd(x = {row_var}, g = {col_var}, w = {weight}, std.error = TRUE)")))
}
}
if (test_name == "chisq.test"){
mdat_test <- eval(parse(text = str_glue("{test_name}(m_tab)")))
}
if (test_name == "prop.trend.test"){
mdat_test <- prop.trend.test(events, trials)
}
if(test_name == "smd") {
res$test_name <- test_name
#mdat_test <- unlist(summary(mdat_test))
res$test_value <- mdat_test$estimate
res$p_value <- mdat_test$std.error
}
if(test_name %in% c("chisq.test", "prop.trend.test")){
res$test_name <- test_name
res$test_value <- mdat_test$statistic
res$p_value <- mdat_test$p.value
}
}
names(res)[1] <- "cate"
return(res)
}
#function for continuous variable-------
cont_des <- function(data,
row_var = "mpg",
col_var = "vs",
fun = c("mean", "sd"),
comb_sym = c(" (", ")"),
round_cont = 2,
p_value = TRUE,
weight = NULL,
test_name = "t.test",
...){
class(data) <- class(data)[which(!class(data) %in% c("rowwise_df", "grouped_df"))]
row_var <- as.name(row_var)
n_col <- eval(parse(text = str_glue("length(na.omit(unique(data${col_var})))")))
if(n_col > 2) {test_name <- "aov"}
if(is.null(col_var)){
res_2 <- data %>%
summarise(mean = do.call(fun[1], args = list(!!row_var, na.rm = TRUE)),
sd = str_c(sprintf(str_glue("%.{round_cont}f"),
round(do.call(fun[2],
args = list(!!row_var,
na.rm = TRUE,
...)),
round_cont)),
collapse = ", "))
res <- res_2
} else {
col_var <- as.name(col_var)
res_1 <- data %>%
filter(!is.na(!!col_var)) %>%
group_by(!!col_var) %>%
summarise(mean = do.call(fun[1], args = list(!!row_var, na.rm = TRUE)),
sd = str_c(sprintf(str_glue("%.{round_cont}f"),
round(do.call(fun[2],
args = list(!!row_var,
na.rm = TRUE,
...)),
round_cont)),
collapse = ", "))
res_2 <- data %>%
summarise(mean = do.call(fun[1], args = list(!!row_var, na.rm = TRUE)),
sd = str_c(sprintf(str_glue("%.{round_cont}f"),
round(do.call(fun[2],
args = list(!!row_var,
na.rm = TRUE,
...)),
round_cont)),
collapse = ", "))
res <- bind_rows(res_1, res_2)
}
if (class(res$sd) == "numeric"){
res <- res %>% rowwise() %>%
mutate(Variable = as.character(row_var),
comb = str_c(sprintf(str_glue("%.{round_cont}f"), round(mean, round_cont)),
comb_sym[1],
sprintf(str_glue("%.{round_cont}f"), round(sd, round_cont)),
comb_sym[2]))
} else {
res <- res %>% rowwise() %>%
mutate(Variable = as.character(row_var),
comb = str_c(sprintf(str_glue("%.{round_cont}f"), round(mean, round_cont)),
comb_sym[1],
sd,
comb_sym[2]))
}
#add p_value
if(!is.null(col_var) & p_value == TRUE){
if(test_name %in% c("t.test", "aov", "wilcox.test")){
mdat_test <- eval(parse(text = str_glue("{test_name}({row_var} ~ {col_var}, data = data)")))
}
if(test_name %in% "smd"){
if (is.null(weight)){
mdat_test <- eval(parse(text = str_glue("data %$% smd(x = {row_var}, g = {col_var}, std.error = TRUE)")))
}
if (!is.null(weight)){
mdat_test <- eval(parse(text = str_glue("data %$% smd(x = {row_var}, g = {col_var}, w = {weight}, std.error = TRUE)")))
}
}
if(test_name == "aov") {
res$test_name <- test_name
mdat_test <- unlist(summary(mdat_test))
res$test_value <- mdat_test["F value1"]
res$p_value <- mdat_test["Pr(>F)1"]
}
if(test_name == "smd") {
res$test_name <- test_name
#mdat_test <- unlist(summary(mdat_test))
res$test_value <- mdat_test$estimate
res$p_value <- mdat_test$std.error
}
if(!(test_name %in% c("aov", "smd"))) {
res$test_name <- test_name
res$test_value <- mdat_test$statistic
res$p_value <- mdat_test$p.value
}
}
n_loc <- which(names(res) == "mean")
names(res)[n_loc:(n_loc + 1)] <- fun
return(res)
}
utils::globalVariables(c("N", "Percentage(%)", "Variable", "comb"))
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