Here, we are not counting the number of rows in the dataset, but rather we are counting the number observations for each keys in the data.

add_n_obs(.data, ...)

Arguments

.data

data.frame

...

extra arguments

Value

dataframe with n_obs, the number of observations per key added.

Examples

#> #> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’: #> #> filter, lag
#> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union
# you can explore the data to see those cases that have exactly two # observations: heights %>% add_n_obs() %>% filter(n_obs == 2)
#> # A tsibble: 16 x 5 [!] #> # Key: country [8] #> country year n_obs continent height_cm #> <chr> <dbl> <int> <chr> <dbl> #> 1 Botswana 1910 2 Africa 165. #> 2 Botswana 1980 2 Africa 167. #> 3 Burundi 1920 2 Africa 166. #> 4 Burundi 1930 2 Africa 169. #> 5 Costa Rica 1940 2 Americas 166. #> 6 Costa Rica 1980 2 Americas 174. #> 7 El Salvador 1990 2 Americas 169. #> 8 El Salvador 2000 2 Americas 171. #> 9 Libya 1890 2 Africa 166. #> 10 Libya 1920 2 Africa 165. #> 11 Mongolia 1910 2 Asia 163. #> 12 Mongolia 1930 2 Asia 165. #> 13 Singapore 1970 2 Asia 172. #> 14 Singapore 2000 2 Asia 175. #> 15 Trinidad and Tobago 1980 2 Americas 174. #> 16 Trinidad and Tobago 2000 2 Americas 174.