/
get_summary_stats.R
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get_summary_stats.R
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#' @include utilities.R
NULL
#'Compute Summary Statistics
#'@description Compute summary statistics for one or multiple numeric variables.
#'@param data a data frame
#'@param ... (optional) One or more unquoted expressions (or variable names)
#' separated by commas. Used to select a variable of interest. If no variable
#' is specified, then the summary statistics of all numeric variables in the
#' data frame is computed.
#'@param type type of summary statistics. Possible values include: \code{"full",
#' "common", "robust", "five_number", "mean_sd", "mean_se", "mean_ci",
#' "median_iqr", "median_mad", "quantile", "mean", "median", "min", "max"}
#'@param show a character vector specifying the summary statistics you want to
#' show. Example: \code{show = c("n", "mean", "sd")}. This is used to filter
#' the output after computation.
#' @param probs numeric vector of probabilities with values in [0,1]. Used only when type = "quantile".
#'@return A data frame containing descriptive statistics, such as: \itemize{
#' \item \strong{n}: the number of individuals \item \strong{min}: minimum
#' \item \strong{max}: maximum \item \strong{median}: median \item
#' \strong{mean}: mean \item \strong{q1, q3}: the first and the third quartile,
#' respectively. \item \strong{iqr}: interquartile range \item \strong{mad}:
#' median absolute deviation (see ?MAD) \item \strong{sd}: standard deviation
#' of the mean \item \strong{se}: standard error of the mean \item \strong{ci}: 95 percent confidence interval of the mean }
#' @examples
#' # Full summary statistics
#' data("ToothGrowth")
#' ToothGrowth %>% get_summary_stats(len)
#'
#' # Summary statistics of grouped data
#' # Show only common summary
#' ToothGrowth %>%
#' group_by(dose, supp) %>%
#' get_summary_stats(len, type = "common")
#'
#' # Robust summary statistics
#' ToothGrowth %>% get_summary_stats(len, type = "robust")
#'
#' # Five number summary statistics
#' ToothGrowth %>% get_summary_stats(len, type = "five_number")
#'
#' # Compute only mean and sd
#' ToothGrowth %>% get_summary_stats(len, type = "mean_sd")
#'
#' # Compute full summary statistics but show only mean, sd, median, iqr
#' ToothGrowth %>%
#' get_summary_stats(len, show = c("mean", "sd", "median", "iqr"))
#'
#'@export
get_summary_stats <- function(
data, ..., type = c("full", "common", "robust", "five_number",
"mean_sd", "mean_se", "mean_ci", "median_iqr", "median_mad", "quantile",
"mean", "median", "min", "max" ),
show = NULL, probs = seq(0, 1, 0.25)
){
type = match.arg(type)
if(is_grouped_df(data)){
results <- data %>%
doo(get_summary_stats, ..., type = type, show = show, probs = probs)
return(results)
}
data <- data %>% select_numeric_columns()
vars <- data %>% get_selected_vars(...)
n.vars <- length(vars)
if(n.vars >= 1){
data <- data %>% select(!!!syms(vars))
}
variable <- .value. <- NULL
data <- data %>%
gather(key = "variable", value = ".value.") %>%
filter(!is.na(.value.)) %>%
dplyr::mutate(variable = factor(.data$variable, levels = vars)) %>%
group_by(variable)
results <- switch(
type,
common = common_summary(data),
robust = robust_summary(data),
five_number = five_number_summary(data),
mean_sd = mean_sd(data),
mean_se = mean_se(data),
mean_ci = mean_ci(data),
median_iqr = median_iqr(data),
median_mad = median_mad(data),
quantile = quantile_summary(data, probs),
mean = mean_(data),
median = median_(data),
min = min_(data),
max = max_(data),
full_summary(data)
) %>%
dplyr::ungroup() %>%
dplyr::mutate_if(is.numeric, round, digits = 3)
if(!is.null(show)){
show <- unique(c("variable", "n", show))
results <- results %>% select(!!!syms(show))
}
results
}
full_summary <- function(data){
confidence <- 0.95
alpha <- 1 - confidence
.value. <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
min = min(.value., na.rm=TRUE),
max = max(.value., na.rm=TRUE),
median = stats::median(.value., na.rm=TRUE),
q1 = stats::quantile(.value., 0.25, na.rm = TRUE),
q3 = stats::quantile(.value., 0.75, na.rm = TRUE),
iqr = stats::IQR(.value., na.rm=TRUE),
mad = stats::mad(.value., na.rm=TRUE),
mean = mean(.value., na.rm = TRUE),
sd = stats::sd(.value., na.rm = TRUE)
) %>%
mutate(
se = .data$sd / sqrt(.data$n),
ci = abs(stats::qt(alpha/2, .data$n-1)*.data$se)
)
}
common_summary <- function(data){
confidence <- 0.95
alpha <- 1 - confidence
.value. <- ci <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
min = min(.value., na.rm=TRUE),
max = max(.value., na.rm=TRUE),
median = stats::median(.value., na.rm=TRUE),
iqr = stats::IQR(.value., na.rm=TRUE),
mean = mean(.value., na.rm = TRUE),
sd = stats::sd(.value., na.rm = TRUE)
) %>%
mutate(
se = .data$sd / sqrt(.data$n),
ci = abs(stats::qt(alpha/2, .data$n-1)*.data$se)
)
}
robust_summary <- function(data){
.value. <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
median = stats::median(.value., na.rm=TRUE),
iqr = stats::IQR(.value., na.rm=TRUE)
)
}
quantile_summary <- function(data, probs = seq(0, 1, 0.25)){
core_func <- function(data, probs){
.value. <- NULL
n <- sum(!is.na(data$.value.))
names(n) <- "n"
q <- stats::quantile(data$.value., probs, na.rm = TRUE)
results <- t(matrix(c(n, q)))
colnames(results) <- c("n", names(q))
tibble::as_tibble(results)
}
results <- data %>%
nest() %>%
mutate(.results. = map(data, core_func, probs)) %>%
select(.data$variable, .data$.results.) %>%
unnest(cols = ".results.")
results
}
five_number_summary <- function(data){
.value. <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
min = min(.value., na.rm=TRUE),
max = max(.value., na.rm=TRUE),
q1 = stats::quantile(.value., 0.25, na.rm = TRUE),
median = stats::median(.value., na.rm=TRUE),
q3 = stats::quantile(.value., 0.75, na.rm = TRUE)
)
}
mean_ <- function(data){
.value. <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
mean = mean(.value., na.rm = TRUE)
)
}
median_ <- function(data){
.value. <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
median = stats::median(.value., na.rm=TRUE)
)
}
max_ <- function(data){
.value. <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
max = max(.value., na.rm = TRUE)
)
}
min_ <- function(data){
.value. <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
min = min(.value., na.rm = TRUE)
)
}
mean_sd <- function(data){
.value. <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
mean = mean(.value., na.rm = TRUE),
sd = stats::sd(.value., na.rm = TRUE)
)
}
mean_se <- function(data){
.value. <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
mean = mean(.value., na.rm = TRUE),
sd = stats::sd(.value., na.rm = TRUE)
) %>%
mutate(se = .data$sd / sqrt(.data$n))%>%
select(-.data$sd)
}
mean_ci <- function(data){
confidence <- 0.95
alpha <- 1 - confidence
.value. <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
mean = mean(.value., na.rm = TRUE),
sd = stats::sd(.value., na.rm = TRUE)
) %>%
mutate(
se = .data$sd / sqrt(.data$n),
ci = abs(stats::qt(alpha/2, .data$n-1)*.data$se)
)%>%
select(-.data$se, -.data$sd)
}
median_iqr <- function(data){
.value. <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
median = stats::median(.value., na.rm=TRUE),
iqr = stats::IQR(.value., na.rm=TRUE)
)
}
median_mad <- function(data){
.value. <- NULL
data %>%
dplyr::summarise(
n = sum(!is.na(.value.)),
median = stats::median(.value., na.rm=TRUE),
mad = stats::mad(.value., na.rm=TRUE)
)
}