df_stats: Calculate statistics for "response" variables

View source: R/df_stats.R

df_statsR Documentation

Calculate statistics for "response" variables


Creates a data frame of statistics calculated on one or more response variables, possibly for each group formed by combinations of additional variables. The resulting data frame has one column for each of the statistics requested as well as columns for any grouping variables and a column identifying the response variable for which the statistics was calculated.


  drop = TRUE,
  fargs = list(),
  sep = "_",
  format = c("wide", "long"),
  groups = NULL,
  long_names = FALSE,
  nice_names = FALSE,
  na.action = "na.warn"



A formula indicating which variables are to be used. Semantics are approximately as in lm() since stats::model.frame() is used to turn the formula into a data frame. But first conditions and groups are re-expressed into a form that stats::model.frame() can interpret. Multiple response variables can be separated by + on the left hand side of the formula. A one-sided formula ~ rhs | cond is treated as rhs ~ 1 | cond. See details.


A data frame or list containing the variables.


Functions used to compute the statistics. If this is empty, a default set of summary statistics is used. Functions used must accept a vector of values and return either a (possibly named) single value, a (possibly named) vector of values, or a data frame with one row. Functions can be specified with character strings, names, or expressions that look like function calls with the first argument missing. The latter option provides a convenient way to specify additional arguments. See the examples. Note: If these arguments are named, those names will be used in the data frame returned (see details). Such names may not be among the names of the named arguments of df_stats().

If a function is specified using ::, be sure to include the trailing parens, even if there are no additional arguments required.


A logical indicating whether combinations of the grouping variables that do not occur in data should be dropped from the result.


Arguments passed to the functions in ....


A character string to separate components of names. Set to "" if you don't want separation.


One of "long" or "wide" indicating the desired shape of the returned data frame.


An expression or formula to be evaluated in data and defining (additional) groups. This isn't necessary, since these can be placed into the formula, but it is provided for similarity to other functions from the mosaic package.


A logical indicating whether the default names should include the name of the variable being summarized as well as the summarizing function name in the default case when names are not derived from the names of the returned object or an argument name.


A logical indicating whether make.names() should be used to force names of the returned data frame to by syntactically valid.


A function (or character string naming a function) that determines how NAs are treated. Options include "na.warn" which removes missing data and emits a warning, "na.pass" which includes all of the data, "na.omit" or "na.exclude" which silently discard missing data, and "na.fail" which fails if there is missing data. See link[stats]{na.pass}() and na.warn() for details. The default is "na.warn" unless no function are specified in ..., in which case "na.pass" is used since the default function reports the number of missing values.


Use a one-sided formula to compute summary statistics for the right hand side expression over the entire data. Use a two-sided formula to compute summary statistics for the left hand (response) expression(s) for each combination of levels of the expressions occurring on the right hand side. This is most useful when the left hand side is quantitative and each expression on the right hand side has relatively few unique values. A function like mosaic::ntiles() is often useful to create a few groups of roughly equal size determined by ranges of a quantitative variable. See the examples.

Note that unlike dplyr::summarise(), df_stats() ignores any grouping defined in data if data is a grouped tibble.


A data frame. Names of columns in the resulting data frame consist of three parts separated by sep. The first part is the argument name, if it exists, else the function. The second part is the name of the variable being summarised if long_names == TRUE and the first part is the function name, else "" The third part is the names of the object returned by the summarizing function, if they exist, else a sequence of consecutive integers or "" if there is only one component returned by the summarizing function. See the examples.

Cautions Regarding Formulas

The use of | to define groups is tricky because (a) stats::model.frame() doesn't handle this sort of thing and (b) | is also used for logical or. The current algorithm for handling this will turn the first occurrence of | into an attempt to condition, so logical or cannot be used before conditioning in the formula. If you have need of logical or, we suggest creating a new variable that contains the results of evaluating the expression.

Similarly, addition (+) is used to separate grouping variables, not for arithmetic.


df_stats( ~ hp, data = mtcars)
# There are several ways to specify functions
df_stats( ~ hp, data = mtcars, mean, trimmed_mean = mean(trim = 0.1), "median",
  range, Q = quantile(c(0.25, 0.75)))
# When using ::, be sure to include parens, even if there are no additional arguments.
df_stats( ~ hp, data = mtcars, mean = base::mean(), trimmed_mean = base::mean(trim = 0.1))

# force names to by syntactically valid
df_stats( ~ hp, data = mtcars, Q = quantile(c(0.25, 0.75)), nice_names = TRUE)
# longer names
df_stats( ~ hp, data = mtcars, mean, trimmed_mean = mean(trim = 0.1), "median", range,
  long_names = TRUE)
# wide vs long format
df_stats( hp ~ cyl, data = mtcars, mean, median, range)
df_stats( hp + wt + mpg ~ cyl, data = mtcars, mean, median, range)
df_stats( hp ~ cyl, data = mtcars, mean, median, range, format = "long")
# More than one grouping variable -- 4 ways.
df_stats( hp ~ cyl + gear, data = mtcars, mean, median, range)
df_stats( hp ~ cyl | gear, data = mtcars, mean, median, range)
df_stats( hp ~ cyl, groups = ~gear, data = mtcars, mean, median, range)
df_stats( hp ~ cyl, groups = gear, data = mtcars, mean, median, range)

# because the result is a data frame, df_stats() is also useful for creating plots
if(require(ggformula)) {
  gf_violin(hp ~ cyl, data = mtcars, group = ~ cyl) |>
  gf_point(mean ~ cyl, data = df_stats(hp ~ cyl, data = mtcars, mean),
    color = ~ "mean") |>
  gf_point(median ~ cyl, data = df_stats(hp ~ cyl, data = mtcars, median),
    color = ~"median") |>
  gf_labs(color = "")

# magrittr style piping is also supported
if (require(ggformula)) {
  mtcars |>
    df_stats(hp ~ cyl, mean, median, range)
  mtcars |>
    df_stats(hp ~ cyl + gear, mean, median, range) |>
    gf_point(mean ~ cyl, color = ~ factor(gear)) |>
    gf_line(mean ~ cyl, color = ~ factor(gear))

# can be used with a categorical response, too
if (require(mosaic)) {
  df_stats(sex ~ substance, data = HELPrct, table, prop_female = prop)
if (require(mosaic)) {
  df_stats(sex ~ substance, data = HELPrct, table, props)

mosaicCore documentation built on Nov. 5, 2023, 9:06 a.m.