Count observations by group — count.dtplyr

This is a method for the dplyr count() generic. It is translated using .N in the j argument, and supplying groups to keyby as appropriate.

Usage

# S3 method for dtplyr_step
count(x, ..., wt = NULL, sort = FALSE, name = NULL)

Arguments

x

A lazy_dt()

...

<data-masking> Variables to group by.

wt

<data-masking> Frequency weights. Can be NULL or a variable:

If NULL (the default), counts the number of rows in each group.
If a variable, computes sum(wt) for each group.

sort

If TRUE, will show the largest groups at the top.

name

The name of the new column in the output.

If omitted, it will default to n. If there's already a column called n, it will use nn. If there's a column called n and nn, it'll use nnn, and so on, adding ns until it gets a new name.

Examples

library(dplyr, warn.conflicts = FALSE)

dt <- lazy_dt(dplyr::starwars)
dt %>% count(species)
#> Source: local data table [38 x 2]
#> Call:   `_DT6`[, .(n = .N), keyby = .(species)]
#> 
#>   species      n
#>   <chr>    <int>
#> 1 NA           4
#> 2 Aleena       1
#> 3 Besalisk     1
#> 4 Cerean       1
#> 5 Chagrian     1
#> 6 Clawdite     1
#> # … with 32 more rows
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt %>% count(species, sort = TRUE)
#> Source: local data table [38 x 2]
#> Call:   setorder(`_DT6`[, .(n = .N), keyby = .(species)], -n, na.last = TRUE)
#> 
#>   species      n
#>   <chr>    <int>
#> 1 Human       35
#> 2 Droid        6
#> 3 NA           4
#> 4 Gungan       3
#> 5 Kaminoan     2
#> 6 Mirialan     2
#> # … with 32 more rows
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt %>% count(species, wt = mass, sort = TRUE)
#> Source: local data table [38 x 2]
#> Call:   setorder(`_DT6`[, .(n = sum(mass, na.rm = TRUE)), keyby = .(species)], 
#>     -n, na.last = TRUE)
#> 
#>   species     n
#>   <chr>   <dbl>
#> 1 Human   1821.
#> 2 Hutt    1358 
#> 3 Droid    279 
#> 4 Wookiee  248 
#> 5 Kaleesh  159 
#> 6 Gungan   148 
#> # … with 32 more rows
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results