Likely you mean to be using favstats(). Each of these computes the
mean, standard deviation, quartiles, sample size and number of missing values for a numeric vector,
but favstats() can take a formula describing how these summary statistics
should be aggregated across various subsets of the data.
Usage
fav_stats(x, ..., na.rm = TRUE, type = 7)
Value
A vector of statistical summaries
Arguments
x
numeric vector
...
additional arguments (currently ignored)
na.rm
boolean indicating whether missing data should be ignored
type
an integer between 1 and 9 selecting one of the nine quantile algorithms detailed
in the documentation for stats::quantile()
fav_stats(1:10)
fav_stats(faithful$eruptions)
data(penguins, package = "palmerpenguins")
# Note: this is favstats() rather than fav_stats()favstats(bill_length_mm ~ species, data = penguins)