Nothing
#' Descriptive statistics for dataframe
#'
#' Everyone has a funcion like this, str, glimpse, glance etc. This one is
#' specifically designed for use with \code{finalfit} language. It is different
#' in dividing variables by numeric vs factor.
#'
#' @param .data Dataframe.
#' @param dependent Optional character vector: name(s) of depdendent
#' variable(s).
#' @param explanatory Optional character vector: name(s) of explanatory
#' variable(s).
#' @param digits Significant digits for continuous variable summaries
#' @param levels_cut Max number of factor levels to include in factor levels
#' summary (in order to avoid the long printing of variables with many
#' factors).
#'
#' @return Dataframe on summary data.
#' @export
#' @importFrom stats median
#'
#' @examples
#' library(finalfit)
#' dependent = 'mort_5yr'
#' explanatory = c("age", "nodes", "age.factor", "extent.factor", "perfor.factor")
#' colon_s %>%
#' finalfit_glimpse(dependent, explanatory)
ff_glimpse <- function(.data, dependent=NULL, explanatory=NULL, digits = 1,
levels_cut = 5){
if(any(class(.data) %in% c("tbl_df", "tbl"))) .data = data.frame(.data)
if(is.null(dependent) && is.null(explanatory)){
df.in = .data
} else {
df.in = .data %>% dplyr::select(dependent, explanatory)
}
# Continuous
df.in %>%
dplyr::select_if(is.numeric) -> df.numeric
if(dim(df.numeric)[2]!=0){
df.numeric %>%
missing_glimpse(digits=digits) -> df.numeric.out1
df.numeric %>%
purrr::map_df(function(x){
mean = mean(x, na.rm = TRUE)
sd = sd(x, na.rm = TRUE)
min = min(x, na.rm = TRUE)
quartile_25 = quantile(x, probs = 0.25, na.rm = TRUE)
median = median(x, na.rm = TRUE)
quartile_75 = quantile(x, probs = 0.75, na.rm = TRUE)
max = max(x, na.rm = TRUE)
df.out = data.frame(mean, sd, min, quartile_25, median, quartile_75, max) %>%
dplyr::mutate_all(round_tidy, digits=digits)
}) -> df.numeric.out2
df.numeric.out = data.frame(df.numeric.out1, df.numeric.out2)
}else{
df.numeric.out = df.numeric
}
# Factors
df.in %>%
dplyr::select_if(Negate(is.numeric)) -> df.factors
if(dim(df.factors)[2]!=0){
df.factors %>%
missing_glimpse(digits=digits) -> df.factors.out1
fac2char = function(., cut = levels_cut) {
length(levels(.)) > cut
}
df.factors %>%
dplyr::mutate_if(fac2char, as.character) -> df.factors
df.factors %>%
purrr::map_df(function(x){
levels_n = length(levels(as.factor(x)))
levels = ifelse(is.factor(x),
forcats::fct_na_value_to_level(x, level = "(Missing)") %>%
levels() %>%
paste0("\"", ., "\"", collapse = ", "),
"-")
levels_count = ifelse(is.factor(x),
summary(x) %>%
paste(collapse = ", "),
"-")
levels_percent = ifelse(is.factor(x),
summary(x) %>%
prop.table() %>%
{. * 100} %>%
format(digits = 2) %>%
paste(collapse=", "),
"-")
df.out = tibble::tibble(levels_n, levels, levels_count, levels_percent) %>% data.frame()
}) -> df.factors.out2
df.factors.out = data.frame(df.factors.out1, df.factors.out2)
}else{
df.factors.out = df.factors
}
# Previous "always print" version
## Change to standard
# cat("Continuous\n")
# print(df.numeric.out, row.names = TRUE)
# cat("\nCategorical\n")
# print(df.factors.out, row.names = TRUE)
#
# return(invisible(
# list(
# continuous = df.numeric.out,
# categorical = df.factors.out))
# )
#
return(
list(
Continuous = df.numeric.out,
Categorical = df.factors.out)
)
}
#' @rdname ff_glimpse
#' @export
finalfit_glimpse <- ff_glimpse
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.