1d:T7f3,# Descriptive statistics Computes descriptive statistics for a numeric vector or a table of frequencies for a factor. ```r epi.descriptives(dat, conf.level = 0.95) ``` ## Arguments - `dat`: either a numeric vector or a factor. - `conf.level`: magnitude of the returned confidence intervals. Must be a single number between 0 and 1. ## Returns If `dat` is numeric a list containing the following: - **arithmetic**: `n` number of observations, `mean` arithmetic mean, `sd` arithmetic standard deviation, `q25` 25th quantile, `q50` 50th quantile, `q75` 75th quantile, `lower` lower bound of the confidence interval, `upper` upper bound of the confidence interval, `min` minimum value, `max` maximum value, and `na` number of missing values. - **geometric**: `n` number of observations, `mean` geometric mean, `sd` geometric standard deviation, `q25` 25th quantile, `q50` 50th quantile, `q75` 75th quantile, `lower` lower bound of the confidence interval, `upper` upper bound of the confidence interval, `min` minimum value, `max` maximum value, and `na` number of missing values. - **symmetry**: `skewness` and `kurtosis`. If `dat` is a factor a data frame listing: - **level**: The levels of the factor - **n**: The frequency of the respective factor level, including the column totals. ## Examples ```r ## EXAMPLE 1: ## Generate some data: id <- 1:100 n <- rnorm(100, mean = 0, sd = 1) dat.df01 <- data.frame(id, n) # Add missing values: missing <- dat.df01$id %in% sample(dat.df01$id, size = 20) dat.df01$n[missing] <- NA epi.descriptives(dat.df01$n, conf.level = 0.95) ## EXAMPLE 2: ## Generate some data: n <- 1000; p.exp <- 0.50; p.dis <- 0.75 strata <- c(rep("A", times = n / 2), rep("B", times = n / 2)) exp <- rbinom(n = n, size = 1, prob = p.exp) dis <- rbinom(n = n, size = 1, prob = p.dis) dat.df02 <- data.frame(strata, exp, dis) dat.df02$strata <- factor(dat.df02$strata) dat.df02$exp <- factor(dat.df02$exp, levels = c("1", "0")) head(dat.df02) epi.descriptives(dat.df02$exp, conf.level = 0.95) ```

epi.descriptives function