R/decrib.R
In YuweiNi45/lng:
Defines functions
decrib
Documented in
decrib
#' create tables providing descriptive statistics
#'
#' Generic function to create tables providing descriptive statistics summary for overall and in different groups.
#'
#' @param dat a data frame or environment (or object coercible by as.data.frame to a data frame) containing the variables to be summarized
#' @param y a numeric vector of outcome variable of interest (default is NULL)
#' @param alternative a character string specifying the alternative approaches, must be one of "overall" (default), "total" or "bygroup"
#' @param digits the digits of the data values (default is 2)
#' @param ... further arguments to be passed to or from methods
#'
#' @return Generate descriptive statistical tables for different type of outcome variables of interest without missing value
#'
#' @references Hlavac Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables. R package version 5.2.1. https://CRAN.R-project.org/package=stargazer
#'
#' @seealso \code{\link[stats]{anova}}
#'
#' \code{\link[stats]{kruskal.test}}
#'
#'
#'
#'@import NHANES
#'
#'@export
decrib <-
function(dat,
y = NULL,
alternative = c("overall", "total", "bygroup"),
digit = 2,
...) {
alternative <- match.arg(alternative)
#testing data
#dat <- data2
#y <- dat$TotChol
##get the names done for each colomn of the final table for different groups
if (!is.null(y)) {
y_length <- length(y)
data_names <- colnames(dat)
name_length <- length(data_names)
result <- numeric(name_length)
for (i in 1:name_length) {
result[i] <- sum(y %in% factor(unlist(dat[data_names[i]])))
}
ncol_y <- which(result == y_length)
y_group <- data_names[ncol_y]
#data just includes the covariates
dat_noy <- dat[,-ncol_y]
#get the Total column done
###know which variable is continuous and which is categorical
ncol_allx <- ncol(dat_noy)
all_level <- numeric(ncol_allx)
for (i in 1:ncol_allx) {
if (!is.null(levels(unlist(dat_noy[, i])))) {
all_level[i] <- length(levels(unlist(dat_noy[, i])))
}
}
max_level <- max(all_level)
## if we only have continuous variables
if ((sum(all_level > 0)) == 0) {
result_tol_mec <- numeric(ncol_allx)
result_tol_sd <- numeric(ncol_allx)
con_names <- colnames(dat_noy)
for (i in 1:ncol_allx) {
result_tol_mec[i] <- mean(unlist(dat_noy[, i]))
result_tol_sd[i] <- sd(unlist(dat_noy[, i]))
}
##create the table for continuous variables' total
out1 <- data.frame(Total = paste0(
format(result_tol_mec, digits = digit),
"+/-",
format(result_tol_sd, digits = digit)
))
colnames(out1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1) <- con_names
out_totalnum <- out1
}
## if we only have cateorical variables
else if ((sum(all_level > 0)) == ncol_allx) {
result_tol_count <-
matrix(nrow = ncol_allx, ncol = max_level)
result_tol_freq <-
matrix(nrow = ncol_allx, ncol = max_level)
cat_names <- colnames(dat_noy)
cat_names_each <- character(0)
for (i in 1:ncol_allx) {
levels_cat <- levels(unlist(dat_noy[, i]))
nlevel <- length(levels_cat)
for (j in 1:nlevel) {
cat_names_each <-
c(cat_names_each,
paste0(cat_names[i], ": ", levels_cat[j]))
result_tol_count[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j]))
result_tol_freq[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j])) / length(unlist(dat_noy[, i]))
}
}
#get categorical varaibles ready
result_tol_count1 <-
result_tol_count[which(!cat_names == ""), ]
result_tol_freq1 <-
result_tol_freq[which(!cat_names == ""), ]
cat_total_count <- numeric(0)
cat_total_freq <- numeric(0)
if (!is.null(nrow(result_tol_count1))) {
for (i in 1:nrow(result_tol_count1)) {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[i, j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i, j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i, j])
}
}
}
else {
if (!is.na(result_tol_count1[i])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i])
}
}
}
} else {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[j])
}
}
} else {
cat_total_count <- result_tol_count1
cat_total_freq <- result_tol_freq1
}
}
##create the table for categorical variables' total
out1_1 <- data.frame(Total = paste0(
format(cat_total_count, digits = digit),
" (",
format(cat_total_freq * 100, digits = digit),
")"
))
colnames(out1_1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1_1) <- cat_names_each
out_totalnum <- out1_1
}
##we have both continuous and categorical variables
else{
result_tol_mec <- numeric(ncol_allx)
result_tol_sd <- numeric(ncol_allx)
result_tol_count <-
matrix(nrow = ncol_allx, ncol = max_level)
result_tol_freq <-
matrix(nrow = ncol_allx, ncol = max_level)
#as.character(unlist(dat_noy[, 1]))
all_var_names <- colnames(dat_noy)
con_names <- character(ncol_allx)
cat_names <- character(ncol_allx)
cat_names_each <- character(0)
for (i in 1:ncol_allx) {
if (all_level[i] == 0) {
con_names[i] <- all_var_names[i]
result_tol_mec[i] <- mean(unlist(dat_noy[, i]))
result_tol_sd[i] <- sd(unlist(dat_noy[, i]))
} else if (all_level[i] != 0) {
levels_cat <- levels(unlist(dat_noy[, i]))
nlevel <- length(levels_cat)
cat_names[i] <- all_var_names[i]
for (j in 1:nlevel) {
cat_names_each <-
c(cat_names_each,
paste0(cat_names[i], ": ", levels_cat[j]))
result_tol_count[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j]))
result_tol_freq[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j])) / length(unlist(dat_noy[, i]))
}
}
}
#get continuoius variables ready
con_names1 <- con_names[-which(con_names == "")]
result_tol_mec1 <-
result_tol_mec[-which(result_tol_mec == 0)]
result_tol_sd1 <- result_tol_sd[-which(result_tol_sd == 0)]
##create the table for continuous variables' total
out1 <- data.frame(Total = paste0(
format(result_tol_mec1, digits = digit),
"+/-",
format(result_tol_sd1, digits = digit)
))
colnames(out1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1) <- con_names1
#get categorical varaibles ready
all_level1 <- all_level[-which(all_level == 0)]
cat_names1 <- cat_names[-which(cat_names == "")]
result_tol_count1 <-
result_tol_count[which(!cat_names == ""), ]
result_tol_freq1 <-
result_tol_freq[which(!cat_names == ""), ]
cat_total_count <- numeric(0)
cat_total_freq <- numeric(0)
if (!is.null(nrow(result_tol_count1))) {
for (i in 1:nrow(result_tol_count1)) {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[i, j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i, j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i, j])
}
}
}
else {
if (!is.na(result_tol_count1[i])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i])
}
}
}
} else {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[j])
}
}
} else {
cat_total_count <- result_tol_count1
cat_total_freq <- result_tol_freq1
}
}
##create the table for categorical variables' total
out1_1 <- data.frame(Total = paste0(
format(cat_total_count, digits = digit),
" (",
format(cat_total_freq * 100, digits = digit),
")"
))
colnames(out1_1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1_1) <- cat_names_each
##create the total table by combining these two tables!!!! finally!!!! we made it !!!!
out_total <- rbind(out1, out1_1)
out_totalnum <- out_total
}
#===================================================================================================================#
#===================================================================================================================#
##create the table for different groups
out_all <- out_totalnum
if (is.null(levels(y))) {
out_all <- out_all
} else{
group_levels <- levels(y)
ngroup_levels <- length(group_levels)
for (g in 1:ngroup_levels) {
dat1 <- dat[which(y == group_levels[g]),]
dat_noy <- dat1[,-ncol_y]
ncol_allx <- ncol(dat_noy)
all_level <- numeric(ncol_allx)
for (i in 1:ncol_allx) {
if (!is.null(levels(unlist(dat_noy[, i])))) {
all_level[i] <- length(levels(unlist(dat_noy[, i])))
}
}
max_level <- max(all_level)
## if we only have continuous variables
if ((sum(all_level > 0)) == 0) {
result_tol_mec <- numeric(ncol_allx)
result_tol_sd <- numeric(ncol_allx)
con_names <- colnames(dat_noy)
for (i in 1:ncol_allx) {
result_tol_mec[i] <- mean(unlist(dat_noy[, i]))
result_tol_sd[i] <- sd(unlist(dat_noy[, i]))
}
##create the table for continuous variables' total
out1 <- data.frame(Total = paste0(
format(result_tol_mec, digits = digit),
"+/-",
format(result_tol_sd, digits = digit)
))
colnames(out1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1) <- con_names
out_total <- out1
}
## if we only have cateorical variables
else if ((sum(all_level > 0)) == ncol_allx) {
result_tol_count <-
matrix(nrow = ncol_allx, ncol = max_level)
result_tol_freq <-
matrix(nrow = ncol_allx, ncol = max_level)
cat_names <- colnames(dat_noy)
cat_names_each <- character(0)
for (i in 1:ncol_allx) {
levels_cat <- levels(unlist(dat_noy[, i]))
nlevel <- length(levels_cat)
cat_names[i] <- all_var_names[i]
for (j in 1:nlevel) {
cat_names_each <-
c(cat_names_each,
paste0(cat_names[i], ": ", levels_cat[j]))
result_tol_count[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j]))
result_tol_freq[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j])) / length(unlist(dat_noy[, i]))
}
}
#get categorical varaibles ready
result_tol_count1 <-
result_tol_count[which(!cat_names == ""), ]
result_tol_freq1 <-
result_tol_freq[which(!cat_names == ""), ]
cat_total_count <- numeric(0)
cat_total_freq <- numeric(0)
if (!is.null(nrow(result_tol_count1))) {
for (i in 1:nrow(result_tol_count1)) {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[i, j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i, j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i, j])
}
}
}
else {
if (!is.na(result_tol_count1[i])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i])
}
}
}
} else {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[j])
}
}
} else {
cat_total_count <- result_tol_count1
cat_total_freq <- result_tol_freq1
}
}
##create the table for categorical variables' total
out1_1 <- data.frame(Total = paste0(
format(cat_total_count, digits = digit),
" (",
format(cat_total_freq * 100, digits = digit),
")"
))
colnames(out1_1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1_1) <- cat_names_each
out_total <- out1_1
}
##we have both continuous and categorical variables
else{
result_tol_mec <- numeric(ncol_allx)
result_tol_sd <- numeric(ncol_allx)
result_tol_count <-
matrix(nrow = ncol_allx, ncol = max_level)
result_tol_freq <-
matrix(nrow = ncol_allx, ncol = max_level)
#as.character(unlist(dat_noy[, 1]))
all_var_names <- colnames(dat_noy)
con_names <- character(ncol_allx)
cat_names <- character(ncol_allx)
cat_names_each <- character(0)
for (i in 1:ncol_allx) {
if (all_level[i] == 0) {
con_names[i] <- all_var_names[i]
result_tol_mec[i] <- mean(unlist(dat_noy[, i]))
result_tol_sd[i] <- sd(unlist(dat_noy[, i]))
} else if (all_level[i] != 0) {
levels_cat <- levels(unlist(dat_noy[, i]))
nlevel <- length(levels_cat)
cat_names[i] <- all_var_names[i]
for (j in 1:nlevel) {
cat_names_each <-
c(cat_names_each,
paste0(cat_names[i], ": ", levels_cat[j]))
result_tol_count[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j]))
result_tol_freq[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j])) / length(unlist(dat_noy[, i]))
}
}
}
#get continuoius variables ready
con_names1 <- con_names[-which(con_names == "")]
result_tol_mec1 <-
result_tol_mec[-which(result_tol_mec == 0)]
result_tol_sd1 <-
result_tol_sd[-which(result_tol_sd == 0)]
##create the table for continuous variables' total
out1 <- data.frame(Total = paste0(
format(result_tol_mec1, digits = digit),
"+/-",
format(result_tol_sd1, digits = digit)
))
colnames(out1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1) <- con_names1
#get categorical varaibles ready
all_level1 <- all_level[-which(all_level == 0)]
cat_names1 <- cat_names[-which(cat_names == "")]
result_tol_count1 <-
result_tol_count[which(!cat_names == ""), ]
result_tol_freq1 <-
result_tol_freq[which(!cat_names == ""), ]
cat_total_count <- numeric(0)
cat_total_freq <- numeric(0)
if (!is.null(nrow(result_tol_count1))) {
for (i in 1:nrow(result_tol_count1)) {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[i, j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i, j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i, j])
}
}
}
else {
if (!is.na(result_tol_count1[i])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i])
}
}
}
} else {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[j])
}
}
} else {
cat_total_count <- result_tol_count1
cat_total_freq <- result_tol_freq1
}
}
##create the table for categorical variables' total
out1_1 <- data.frame(Total = paste0(
format(cat_total_count, digits = digit),
" (",
format(cat_total_freq * 100, digits = digit),
")"
))
colnames(out1_1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1_1) <- cat_names_each
##create the total table by combining these two tables!!!! finally!!!! we made it !!!!
out_total <- rbind(out1, out1_1)
}
#mkaing the tablw that combines both total and subgroups
out_all <- cbind(out_all, out_total)
}
if (alternative == "total") {
out_all <- out_totalnum
} else if (alternative == "bygroup") {
out_all <-
out_all[-which(colnames(out_all) == colnames(out_totalnum))]
} else {
out_all <- out_all
}
}
}
# if y is missing
else {
dat_noy <- dat
#get the Total column done
###know which variable is continuous and which is categorical
ncol_allx <- ncol(dat_noy)
all_level <- numeric(ncol_allx)
for (i in 1:ncol_allx) {
if (!is.null(levels(unlist(dat_noy[, i])))) {
all_level[i] <- length(levels(unlist(dat_noy[, i])))
}
}
max_level <- max(all_level)
## if we only have continuous variables
if ((sum(all_level > 0)) == 0) {
result_tol_mec <- numeric(ncol_allx)
result_tol_sd <- numeric(ncol_allx)
con_names <- colnames(dat_noy)
for (i in 1:ncol_allx) {
result_tol_mec[i] <- mean(unlist(dat_noy[, i]))
result_tol_sd[i] <- sd(unlist(dat_noy[, i]))
}
##create the table for continuous variables' total
out1 <- data.frame(Total = paste0(
format(result_tol_mec, digits = digit),
"+/-",
format(result_tol_sd, digits = digit)
))
colnames(out1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1) <- con_names
out_all <- out1
}
## if we only have cateorical variables
else if ((sum(all_level > 0)) == ncol_allx) {
result_tol_count <-
matrix(nrow = ncol_allx, ncol = max_level)
result_tol_freq <-
matrix(nrow = ncol_allx, ncol = max_level)
cat_names <- colnames(dat_noy)
cat_names_each <- character(0)
for (i in 1:ncol_allx) {
levels_cat <- levels(unlist(dat_noy[, i]))
nlevel <- length(levels_cat)
cat_names[i] <- all_var_names[i]
for (j in 1:nlevel) {
cat_names_each <-
c(cat_names_each,
paste0(cat_names[i], ": ", levels_cat[j]))
result_tol_count[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j]))
result_tol_freq[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j])) / length(unlist(dat_noy[, i]))
}
}
#get categorical varaibles ready
result_tol_count1 <-
result_tol_count[which(!cat_names == ""), ]
result_tol_freq1 <-
result_tol_freq[which(!cat_names == ""), ]
cat_total_count <- numeric(0)
cat_total_freq <- numeric(0)
if (!is.null(nrow(result_tol_count1))) {
for (i in 1:nrow(result_tol_count1)) {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[i, j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i, j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i, j])
}
}
}
else {
if (!is.na(result_tol_count1[i])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i])
}
}
}
} else {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[j])
}
}
} else {
cat_total_count <- result_tol_count1
cat_total_freq <- result_tol_freq1
}
}
##create the table for categorical variables' total
out1_1 <- data.frame(Total = paste0(
format(cat_total_count, digits = digit),
" (",
format(cat_total_freq * 100, digits = digit),
")"
))
colnames(out1_1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1_1) <- cat_names_each
out_all <- out1_1
}
##we have both continuous and categorical variables
else{
result_tol_mec <- numeric(ncol_allx)
result_tol_sd <- numeric(ncol_allx)
result_tol_count <-
matrix(nrow = ncol_allx, ncol = max_level)
result_tol_freq <-
matrix(nrow = ncol_allx, ncol = max_level)
#as.character(unlist(dat_noy[, 1]))
all_var_names <- colnames(dat_noy)
con_names <- character(ncol_allx)
cat_names <- character(ncol_allx)
cat_names_each <- character(0)
for (i in 1:ncol_allx) {
if (all_level[i] == 0) {
con_names[i] <- all_var_names[i]
result_tol_mec[i] <- mean(unlist(dat_noy[, i]))
result_tol_sd[i] <- sd(unlist(dat_noy[, i]))
} else if (all_level[i] != 0) {
levels_cat <- levels(unlist(dat_noy[, i]))
nlevel <- length(levels_cat)
cat_names[i] <- all_var_names[i]
for (j in 1:nlevel) {
cat_names_each <-
c(cat_names_each,
paste0(cat_names[i], ": ", levels_cat[j]))
result_tol_count[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j]))
result_tol_freq[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j])) / length(unlist(dat_noy[, i]))
}
}
}
#get continuoius variables ready
con_names1 <- con_names[-which(con_names == "")]
result_tol_mec1 <-
result_tol_mec[-which(result_tol_mec == 0)]
result_tol_sd1 <- result_tol_sd[-which(result_tol_sd == 0)]
##create the table for continuous variables' total
out1 <- data.frame(Total = paste0(
format(result_tol_mec1, digits = digit),
"+/-",
format(result_tol_sd1, digits = digit)
))
colnames(out1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1) <- con_names1
#get categorical varaibles ready
all_level1 <- all_level[-which(all_level == 0)]
cat_names1 <- cat_names[-which(cat_names == "")]
result_tol_count1 <-
result_tol_count[which(!cat_names == ""), ]
result_tol_freq1 <-
result_tol_freq[which(!cat_names == ""), ]
cat_total_count <- numeric(0)
cat_total_freq <- numeric(0)
if (!is.null(nrow(result_tol_count1))) {
for (i in 1:nrow(result_tol_count1)) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[i, j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i, j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i, j])
}
}
}
} else {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[j])
}
}
}
##create the table for categorical variables' total
out1_1 <- data.frame(Total = paste0(
format(cat_total_count, digits = digit),
" (",
format(cat_total_freq * 100, digits = digit),
")"
))
colnames(out1_1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1_1) <- cat_names_each
##create the total table by combining these two tables!!!! finally!!!! we made it !!!!
out_total <- rbind(out1, out1_1)
out_all <- out_total
}
}
return(out_all)
}
YuweiNi45/lng documentation built on Oct. 21, 2018, 4:15 p.m.
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Defines functions decrib
Documented in decrib
#' create tables providing descriptive statistics
#'
#' Generic function to create tables providing descriptive statistics summary for overall and in different groups.
#'
#' @param dat a data frame or environment (or object coercible by as.data.frame to a data frame) containing the variables to be summarized
#' @param y a numeric vector of outcome variable of interest (default is NULL)
#' @param alternative a character string specifying the alternative approaches, must be one of "overall" (default), "total" or "bygroup"
#' @param digits the digits of the data values (default is 2)
#' @param ... further arguments to be passed to or from methods
#'
#' @return Generate descriptive statistical tables for different type of outcome variables of interest without missing value
#'
#' @references Hlavac Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables. R package version 5.2.1. https://CRAN.R-project.org/package=stargazer
#'
#' @seealso \code{\link[stats]{anova}}
#'
#' \code{\link[stats]{kruskal.test}}
#'
#'
#'
#'@import NHANES
#'
#'@export
decrib <-
function(dat,
y = NULL,
alternative = c("overall", "total", "bygroup"),
digit = 2,
...) {
alternative <- match.arg(alternative)
#testing data
#dat <- data2
#y <- dat$TotChol
##get the names done for each colomn of the final table for different groups
if (!is.null(y)) {
y_length <- length(y)
data_names <- colnames(dat)
name_length <- length(data_names)
result <- numeric(name_length)
for (i in 1:name_length) {
result[i] <- sum(y %in% factor(unlist(dat[data_names[i]])))
}
ncol_y <- which(result == y_length)
y_group <- data_names[ncol_y]
#data just includes the covariates
dat_noy <- dat[,-ncol_y]
#get the Total column done
###know which variable is continuous and which is categorical
ncol_allx <- ncol(dat_noy)
all_level <- numeric(ncol_allx)
for (i in 1:ncol_allx) {
if (!is.null(levels(unlist(dat_noy[, i])))) {
all_level[i] <- length(levels(unlist(dat_noy[, i])))
}
}
max_level <- max(all_level)
## if we only have continuous variables
if ((sum(all_level > 0)) == 0) {
result_tol_mec <- numeric(ncol_allx)
result_tol_sd <- numeric(ncol_allx)
con_names <- colnames(dat_noy)
for (i in 1:ncol_allx) {
result_tol_mec[i] <- mean(unlist(dat_noy[, i]))
result_tol_sd[i] <- sd(unlist(dat_noy[, i]))
}
##create the table for continuous variables' total
out1 <- data.frame(Total = paste0(
format(result_tol_mec, digits = digit),
"+/-",
format(result_tol_sd, digits = digit)
))
colnames(out1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1) <- con_names
out_totalnum <- out1
}
## if we only have cateorical variables
else if ((sum(all_level > 0)) == ncol_allx) {
result_tol_count <-
matrix(nrow = ncol_allx, ncol = max_level)
result_tol_freq <-
matrix(nrow = ncol_allx, ncol = max_level)
cat_names <- colnames(dat_noy)
cat_names_each <- character(0)
for (i in 1:ncol_allx) {
levels_cat <- levels(unlist(dat_noy[, i]))
nlevel <- length(levels_cat)
for (j in 1:nlevel) {
cat_names_each <-
c(cat_names_each,
paste0(cat_names[i], ": ", levels_cat[j]))
result_tol_count[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j]))
result_tol_freq[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j])) / length(unlist(dat_noy[, i]))
}
}
#get categorical varaibles ready
result_tol_count1 <-
result_tol_count[which(!cat_names == ""), ]
result_tol_freq1 <-
result_tol_freq[which(!cat_names == ""), ]
cat_total_count <- numeric(0)
cat_total_freq <- numeric(0)
if (!is.null(nrow(result_tol_count1))) {
for (i in 1:nrow(result_tol_count1)) {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[i, j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i, j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i, j])
}
}
}
else {
if (!is.na(result_tol_count1[i])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i])
}
}
}
} else {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[j])
}
}
} else {
cat_total_count <- result_tol_count1
cat_total_freq <- result_tol_freq1
}
}
##create the table for categorical variables' total
out1_1 <- data.frame(Total = paste0(
format(cat_total_count, digits = digit),
" (",
format(cat_total_freq * 100, digits = digit),
")"
))
colnames(out1_1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1_1) <- cat_names_each
out_totalnum <- out1_1
}
##we have both continuous and categorical variables
else{
result_tol_mec <- numeric(ncol_allx)
result_tol_sd <- numeric(ncol_allx)
result_tol_count <-
matrix(nrow = ncol_allx, ncol = max_level)
result_tol_freq <-
matrix(nrow = ncol_allx, ncol = max_level)
#as.character(unlist(dat_noy[, 1]))
all_var_names <- colnames(dat_noy)
con_names <- character(ncol_allx)
cat_names <- character(ncol_allx)
cat_names_each <- character(0)
for (i in 1:ncol_allx) {
if (all_level[i] == 0) {
con_names[i] <- all_var_names[i]
result_tol_mec[i] <- mean(unlist(dat_noy[, i]))
result_tol_sd[i] <- sd(unlist(dat_noy[, i]))
} else if (all_level[i] != 0) {
levels_cat <- levels(unlist(dat_noy[, i]))
nlevel <- length(levels_cat)
cat_names[i] <- all_var_names[i]
for (j in 1:nlevel) {
cat_names_each <-
c(cat_names_each,
paste0(cat_names[i], ": ", levels_cat[j]))
result_tol_count[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j]))
result_tol_freq[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j])) / length(unlist(dat_noy[, i]))
}
}
}
#get continuoius variables ready
con_names1 <- con_names[-which(con_names == "")]
result_tol_mec1 <-
result_tol_mec[-which(result_tol_mec == 0)]
result_tol_sd1 <- result_tol_sd[-which(result_tol_sd == 0)]
##create the table for continuous variables' total
out1 <- data.frame(Total = paste0(
format(result_tol_mec1, digits = digit),
"+/-",
format(result_tol_sd1, digits = digit)
))
colnames(out1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1) <- con_names1
#get categorical varaibles ready
all_level1 <- all_level[-which(all_level == 0)]
cat_names1 <- cat_names[-which(cat_names == "")]
result_tol_count1 <-
result_tol_count[which(!cat_names == ""), ]
result_tol_freq1 <-
result_tol_freq[which(!cat_names == ""), ]
cat_total_count <- numeric(0)
cat_total_freq <- numeric(0)
if (!is.null(nrow(result_tol_count1))) {
for (i in 1:nrow(result_tol_count1)) {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[i, j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i, j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i, j])
}
}
}
else {
if (!is.na(result_tol_count1[i])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i])
}
}
}
} else {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[j])
}
}
} else {
cat_total_count <- result_tol_count1
cat_total_freq <- result_tol_freq1
}
}
##create the table for categorical variables' total
out1_1 <- data.frame(Total = paste0(
format(cat_total_count, digits = digit),
" (",
format(cat_total_freq * 100, digits = digit),
")"
))
colnames(out1_1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1_1) <- cat_names_each
##create the total table by combining these two tables!!!! finally!!!! we made it !!!!
out_total <- rbind(out1, out1_1)
out_totalnum <- out_total
}
#===================================================================================================================#
#===================================================================================================================#
##create the table for different groups
out_all <- out_totalnum
if (is.null(levels(y))) {
out_all <- out_all
} else{
group_levels <- levels(y)
ngroup_levels <- length(group_levels)
for (g in 1:ngroup_levels) {
dat1 <- dat[which(y == group_levels[g]),]
dat_noy <- dat1[,-ncol_y]
ncol_allx <- ncol(dat_noy)
all_level <- numeric(ncol_allx)
for (i in 1:ncol_allx) {
if (!is.null(levels(unlist(dat_noy[, i])))) {
all_level[i] <- length(levels(unlist(dat_noy[, i])))
}
}
max_level <- max(all_level)
## if we only have continuous variables
if ((sum(all_level > 0)) == 0) {
result_tol_mec <- numeric(ncol_allx)
result_tol_sd <- numeric(ncol_allx)
con_names <- colnames(dat_noy)
for (i in 1:ncol_allx) {
result_tol_mec[i] <- mean(unlist(dat_noy[, i]))
result_tol_sd[i] <- sd(unlist(dat_noy[, i]))
}
##create the table for continuous variables' total
out1 <- data.frame(Total = paste0(
format(result_tol_mec, digits = digit),
"+/-",
format(result_tol_sd, digits = digit)
))
colnames(out1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1) <- con_names
out_total <- out1
}
## if we only have cateorical variables
else if ((sum(all_level > 0)) == ncol_allx) {
result_tol_count <-
matrix(nrow = ncol_allx, ncol = max_level)
result_tol_freq <-
matrix(nrow = ncol_allx, ncol = max_level)
cat_names <- colnames(dat_noy)
cat_names_each <- character(0)
for (i in 1:ncol_allx) {
levels_cat <- levels(unlist(dat_noy[, i]))
nlevel <- length(levels_cat)
cat_names[i] <- all_var_names[i]
for (j in 1:nlevel) {
cat_names_each <-
c(cat_names_each,
paste0(cat_names[i], ": ", levels_cat[j]))
result_tol_count[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j]))
result_tol_freq[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j])) / length(unlist(dat_noy[, i]))
}
}
#get categorical varaibles ready
result_tol_count1 <-
result_tol_count[which(!cat_names == ""), ]
result_tol_freq1 <-
result_tol_freq[which(!cat_names == ""), ]
cat_total_count <- numeric(0)
cat_total_freq <- numeric(0)
if (!is.null(nrow(result_tol_count1))) {
for (i in 1:nrow(result_tol_count1)) {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[i, j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i, j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i, j])
}
}
}
else {
if (!is.na(result_tol_count1[i])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i])
}
}
}
} else {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[j])
}
}
} else {
cat_total_count <- result_tol_count1
cat_total_freq <- result_tol_freq1
}
}
##create the table for categorical variables' total
out1_1 <- data.frame(Total = paste0(
format(cat_total_count, digits = digit),
" (",
format(cat_total_freq * 100, digits = digit),
")"
))
colnames(out1_1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1_1) <- cat_names_each
out_total <- out1_1
}
##we have both continuous and categorical variables
else{
result_tol_mec <- numeric(ncol_allx)
result_tol_sd <- numeric(ncol_allx)
result_tol_count <-
matrix(nrow = ncol_allx, ncol = max_level)
result_tol_freq <-
matrix(nrow = ncol_allx, ncol = max_level)
#as.character(unlist(dat_noy[, 1]))
all_var_names <- colnames(dat_noy)
con_names <- character(ncol_allx)
cat_names <- character(ncol_allx)
cat_names_each <- character(0)
for (i in 1:ncol_allx) {
if (all_level[i] == 0) {
con_names[i] <- all_var_names[i]
result_tol_mec[i] <- mean(unlist(dat_noy[, i]))
result_tol_sd[i] <- sd(unlist(dat_noy[, i]))
} else if (all_level[i] != 0) {
levels_cat <- levels(unlist(dat_noy[, i]))
nlevel <- length(levels_cat)
cat_names[i] <- all_var_names[i]
for (j in 1:nlevel) {
cat_names_each <-
c(cat_names_each,
paste0(cat_names[i], ": ", levels_cat[j]))
result_tol_count[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j]))
result_tol_freq[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j])) / length(unlist(dat_noy[, i]))
}
}
}
#get continuoius variables ready
con_names1 <- con_names[-which(con_names == "")]
result_tol_mec1 <-
result_tol_mec[-which(result_tol_mec == 0)]
result_tol_sd1 <-
result_tol_sd[-which(result_tol_sd == 0)]
##create the table for continuous variables' total
out1 <- data.frame(Total = paste0(
format(result_tol_mec1, digits = digit),
"+/-",
format(result_tol_sd1, digits = digit)
))
colnames(out1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1) <- con_names1
#get categorical varaibles ready
all_level1 <- all_level[-which(all_level == 0)]
cat_names1 <- cat_names[-which(cat_names == "")]
result_tol_count1 <-
result_tol_count[which(!cat_names == ""), ]
result_tol_freq1 <-
result_tol_freq[which(!cat_names == ""), ]
cat_total_count <- numeric(0)
cat_total_freq <- numeric(0)
if (!is.null(nrow(result_tol_count1))) {
for (i in 1:nrow(result_tol_count1)) {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[i, j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i, j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i, j])
}
}
}
else {
if (!is.na(result_tol_count1[i])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i])
}
}
}
} else {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[j])
}
}
} else {
cat_total_count <- result_tol_count1
cat_total_freq <- result_tol_freq1
}
}
##create the table for categorical variables' total
out1_1 <- data.frame(Total = paste0(
format(cat_total_count, digits = digit),
" (",
format(cat_total_freq * 100, digits = digit),
")"
))
colnames(out1_1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1_1) <- cat_names_each
##create the total table by combining these two tables!!!! finally!!!! we made it !!!!
out_total <- rbind(out1, out1_1)
}
#mkaing the tablw that combines both total and subgroups
out_all <- cbind(out_all, out_total)
}
if (alternative == "total") {
out_all <- out_totalnum
} else if (alternative == "bygroup") {
out_all <-
out_all[-which(colnames(out_all) == colnames(out_totalnum))]
} else {
out_all <- out_all
}
}
}
# if y is missing
else {
dat_noy <- dat
#get the Total column done
###know which variable is continuous and which is categorical
ncol_allx <- ncol(dat_noy)
all_level <- numeric(ncol_allx)
for (i in 1:ncol_allx) {
if (!is.null(levels(unlist(dat_noy[, i])))) {
all_level[i] <- length(levels(unlist(dat_noy[, i])))
}
}
max_level <- max(all_level)
## if we only have continuous variables
if ((sum(all_level > 0)) == 0) {
result_tol_mec <- numeric(ncol_allx)
result_tol_sd <- numeric(ncol_allx)
con_names <- colnames(dat_noy)
for (i in 1:ncol_allx) {
result_tol_mec[i] <- mean(unlist(dat_noy[, i]))
result_tol_sd[i] <- sd(unlist(dat_noy[, i]))
}
##create the table for continuous variables' total
out1 <- data.frame(Total = paste0(
format(result_tol_mec, digits = digit),
"+/-",
format(result_tol_sd, digits = digit)
))
colnames(out1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1) <- con_names
out_all <- out1
}
## if we only have cateorical variables
else if ((sum(all_level > 0)) == ncol_allx) {
result_tol_count <-
matrix(nrow = ncol_allx, ncol = max_level)
result_tol_freq <-
matrix(nrow = ncol_allx, ncol = max_level)
cat_names <- colnames(dat_noy)
cat_names_each <- character(0)
for (i in 1:ncol_allx) {
levels_cat <- levels(unlist(dat_noy[, i]))
nlevel <- length(levels_cat)
cat_names[i] <- all_var_names[i]
for (j in 1:nlevel) {
cat_names_each <-
c(cat_names_each,
paste0(cat_names[i], ": ", levels_cat[j]))
result_tol_count[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j]))
result_tol_freq[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j])) / length(unlist(dat_noy[, i]))
}
}
#get categorical varaibles ready
result_tol_count1 <-
result_tol_count[which(!cat_names == ""), ]
result_tol_freq1 <-
result_tol_freq[which(!cat_names == ""), ]
cat_total_count <- numeric(0)
cat_total_freq <- numeric(0)
if (!is.null(nrow(result_tol_count1))) {
for (i in 1:nrow(result_tol_count1)) {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[i, j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i, j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i, j])
}
}
}
else {
if (!is.na(result_tol_count1[i])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i])
}
}
}
} else {
if (!is.null(ncol(result_tol_count1))) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[j])
}
}
} else {
cat_total_count <- result_tol_count1
cat_total_freq <- result_tol_freq1
}
}
##create the table for categorical variables' total
out1_1 <- data.frame(Total = paste0(
format(cat_total_count, digits = digit),
" (",
format(cat_total_freq * 100, digits = digit),
")"
))
colnames(out1_1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1_1) <- cat_names_each
out_all <- out1_1
}
##we have both continuous and categorical variables
else{
result_tol_mec <- numeric(ncol_allx)
result_tol_sd <- numeric(ncol_allx)
result_tol_count <-
matrix(nrow = ncol_allx, ncol = max_level)
result_tol_freq <-
matrix(nrow = ncol_allx, ncol = max_level)
#as.character(unlist(dat_noy[, 1]))
all_var_names <- colnames(dat_noy)
con_names <- character(ncol_allx)
cat_names <- character(ncol_allx)
cat_names_each <- character(0)
for (i in 1:ncol_allx) {
if (all_level[i] == 0) {
con_names[i] <- all_var_names[i]
result_tol_mec[i] <- mean(unlist(dat_noy[, i]))
result_tol_sd[i] <- sd(unlist(dat_noy[, i]))
} else if (all_level[i] != 0) {
levels_cat <- levels(unlist(dat_noy[, i]))
nlevel <- length(levels_cat)
cat_names[i] <- all_var_names[i]
for (j in 1:nlevel) {
cat_names_each <-
c(cat_names_each,
paste0(cat_names[i], ": ", levels_cat[j]))
result_tol_count[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j]))
result_tol_freq[i, j] <-
length(which(unlist(dat_noy[, i]) == levels_cat[j])) / length(unlist(dat_noy[, i]))
}
}
}
#get continuoius variables ready
con_names1 <- con_names[-which(con_names == "")]
result_tol_mec1 <-
result_tol_mec[-which(result_tol_mec == 0)]
result_tol_sd1 <- result_tol_sd[-which(result_tol_sd == 0)]
##create the table for continuous variables' total
out1 <- data.frame(Total = paste0(
format(result_tol_mec1, digits = digit),
"+/-",
format(result_tol_sd1, digits = digit)
))
colnames(out1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1) <- con_names1
#get categorical varaibles ready
all_level1 <- all_level[-which(all_level == 0)]
cat_names1 <- cat_names[-which(cat_names == "")]
result_tol_count1 <-
result_tol_count[which(!cat_names == ""), ]
result_tol_freq1 <-
result_tol_freq[which(!cat_names == ""), ]
cat_total_count <- numeric(0)
cat_total_freq <- numeric(0)
if (!is.null(nrow(result_tol_count1))) {
for (i in 1:nrow(result_tol_count1)) {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[i, j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[i, j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[i, j])
}
}
}
} else {
for (j in 1:ncol(result_tol_count1)) {
if (!is.na(result_tol_count1[j])) {
cat_total_count <- c(cat_total_count, result_tol_count1[j])
cat_total_freq <-
c(cat_total_freq, result_tol_freq1[j])
}
}
}
##create the table for categorical variables' total
out1_1 <- data.frame(Total = paste0(
format(cat_total_count, digits = digit),
" (",
format(cat_total_freq * 100, digits = digit),
")"
))
colnames(out1_1) <-
c("Total [Mean (SD) or N(%)]")
rownames(out1_1) <- cat_names_each
##create the total table by combining these two tables!!!! finally!!!! we made it !!!!
out_total <- rbind(out1, out1_1)
out_all <- out_total
}
}
return(out_all)
}
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