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.

  • 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