Create one or more scalar variables summarizing the variables of an
existing tbl. Tbls with groups created by
group_by() will result in one
row in the output for each group. Tbls with no groups will result in one row.
summarise(.data, ...) summarize(.data, ...)
An object of the same class as
.data. One grouping level will
summarize() are synonyms.
The data frame backend supports creating a variable and using it in the
same summary. This means that previously created summary variables can be
further transformed or combined within the summary, as in
However, it also means that summary variables with the same names as previous
variables overwrite them, making those variables unavailable to later summary
This behaviour may not be supported in other backends. To avoid unexpected results, consider using new names for your summary variables, especially when creating multiple summaries.
When applied to a data frame, row names are silently dropped. To preserve,
convert to an explicit variable with
# A summary applied to ungrouped tbl returns a single row mtcars %>% summarise(mean = mean(disp), n = n())#> mean n #> 1 230.7219 32# Usually, you'll want to group first mtcars %>% group_by(cyl) %>% summarise(mean = mean(disp), n = n())#> # A tibble: 3 x 3 #> cyl mean n #> <dbl> <dbl> <int> #> 1 4 105. 11 #> 2 6 183. 7 #> 3 8 353. 14# Each summary call removes one grouping level (since that group # is now just a single row) mtcars %>% group_by(cyl, vs) %>% summarise(cyl_n = n()) %>% group_vars()#>  "cyl"# Reusing variable names when summarising may lead to unexpected results mtcars %>% group_by(cyl) %>% summarise(disp = mean(disp), sd = sd(disp), double_disp = disp * 2)#> # A tibble: 3 x 4 #> cyl disp sd double_disp #> <dbl> <dbl> <dbl> <dbl> #> 1 4 105. NA 210. #> 2 6 183. NA 367. #> 3 8 353. NA 706.# Refer to column names stored as strings with the `.data` pronoun: var <- "mass" summarise(starwars, avg = mean(.data[[var]], na.rm = TRUE))#> # A tibble: 1 x 1 #> avg #> <dbl> #> 1 97.3# For more complex cases, knowledge of tidy evaluation and the # unquote operator `!!` is required. See https://tidyeval.tidyverse.org/ # # One useful and simple tidy eval technique is to use `!!` to # bypass the data frame and its columns. Here is how to divide the # column `mass` by an object of the same name: mass <- 100 summarise(starwars, avg = mean(mass / !!mass, na.rm = TRUE))#> # A tibble: 1 x 1 #> avg #> <dbl> #> 1 0.973