You can calculate a series of summary statistics (features) of a given variable for a dataset. For example, a three number summary, the minimum, median, and maximum, can be calculated for a given variable. This is designed to work with the features() function shown in the examples. Other available features in brolgar include:

  • feat_three_num() - minimum, median, maximum

  • feat_five_num() - minimum, q25, median, q75, maximum.

  • feat_ranges() - min, max, range difference, interquartile range.

  • feat_spread() - variance, standard deviation, median absolute distance, and interquartile range

  • feat_monotonic() - is it always increasing, decreasing, or unvarying?

  • feat_diff_summary() - the summary statistics of the differences amongst a value, including the five number summary, as well as the standard deviation and variance. Returns NA if there is only one observation, as we can't take the difference of one observation, and a difference of 0 in these cases would be misleading.

  • feat_brolgar() all features in brolgar.

feat_three_num(x, ...)

feat_five_num(x, ...)

feat_ranges(x, ...)

feat_spread(x, ...)

feat_monotonic(x, ...)

feat_brolgar(x, ...)

feat_diff_summary(x, ...)

Arguments

x

A vector to extract features from.

...

Further arguments passed to other functions.

Examples

# You can use any of the features `feat_*` in conjunction with `features` # like so: heights %>% features(height_cm, # variable you want to explore feat_three_num) # the feature summarisation you want to perform
#> # A tibble: 144 x 4 #> country min med max #> <chr> <dbl> <dbl> <dbl> #> 1 Afghanistan 161. 167. 168. #> 2 Albania 168. 170. 170. #> 3 Algeria 166. 169 171. #> 4 Angola 159. 167. 169. #> 5 Argentina 167. 168. 174. #> 6 Armenia 164. 169. 172. #> 7 Australia 170 172. 178. #> 8 Austria 162. 167. 179. #> 9 Azerbaijan 170. 172. 172. #> 10 Bahrain 161. 164. 164 #> # … with 134 more rows