Create scalar valued summary features for a dataset from feature functions.
features(.tbl, .var, features, ...) features_at(.tbl, .vars, features, ...) features_all(.tbl, features, ...) features_if(.tbl, .predicate, features, ...)
| .tbl | A dataset |
|---|---|
| .var, .vars | The variable(s) to compute features on |
| features | A list of functions (or lambda expressions) for the features to compute. |
| ... | Additional arguments to be passed to each feature. These arguments will only be passed to features which use it in their formal arguments ( |
| .predicate | A predicate function (or lambda expression) to be applied to the columns or a logical vector. The variables for which .predicate is or returns TRUE are selected. |
Lists of available features can be found in the following pages:
# Provide a set of functions as a named list to features. library(tsibble) tourism %>% features(Trips, features = list(mean = mean, sd = sd))#> # A tibble: 304 x 5 #> Region State Purpose mean sd #> <chr> <chr> <chr> <dbl> <dbl> #> 1 Adelaide South Australia Business 156. 35.6 #> 2 Adelaide South Australia Holiday 157. 27.1 #> 3 Adelaide South Australia Other 56.6 17.3 #> 4 Adelaide South Australia Visiting 205. 32.5 #> 5 Adelaide Hills South Australia Business 2.66 4.30 #> 6 Adelaide Hills South Australia Holiday 10.5 6.37 #> 7 Adelaide Hills South Australia Other 1.40 1.65 #> 8 Adelaide Hills South Australia Visiting 14.2 10.7 #> 9 Alice Springs Northern Territory Business 14.6 7.20 #> 10 Alice Springs Northern Territory Holiday 31.9 18.1 #> # … with 294 more rows# Search and use useful features with `feature_set()`. if(requireNamespace("feasts")) library(feasts) tourism %>% features(Trips, features = feature_set(tags = "autocorrelation"))#> # A tibble: 304 x 14 #> Region State Purpose acf1 acf10 diff1_acf1 diff1_acf10 diff2_acf1 #> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Adelaide South Aus… Busine… 0.0333 0.131 -0.520 0.463 -0.676 #> 2 Adelaide South Aus… Holiday 0.0456 0.372 -0.343 0.614 -0.487 #> 3 Adelaide South Aus… Other 0.517 1.15 -0.409 0.383 -0.675 #> 4 Adelaide South Aus… Visiti… 0.0684 0.294 -0.394 0.452 -0.518 #> 5 Adelaide… South Aus… Busine… 0.0709 0.134 -0.580 0.415 -0.750 #> 6 Adelaide… South Aus… Holiday 0.131 0.313 -0.536 0.500 -0.716 #> 7 Adelaide… South Aus… Other 0.261 0.330 -0.253 0.317 -0.457 #> 8 Adelaide… South Aus… Visiti… 0.139 0.117 -0.472 0.239 -0.626 #> 9 Alice Sp… Northern … Busine… 0.217 0.367 -0.500 0.381 -0.658 #> 10 Alice Sp… Northern … Holiday -0.00660 2.11 -0.153 2.11 -0.274 #> # … with 294 more rows, and 6 more variables: diff2_acf10 <dbl>, #> # season_acf1 <dbl>, pacf5 <dbl>, diff1_pacf5 <dbl>, diff2_pacf5 <dbl>, #> # season_pacf <dbl># Best practice is to use anonymous functions for additional arguments tourism %>% features(Trips, list(~ quantile(., probs=seq(0,1,by=0.2))))#> # A tibble: 304 x 9 #> Region State Purpose `0%` `20%` `40%` `60%` `80%` `100%` #> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Adelaide South Austra… Busine… 68.7 127. 145. 160. 182. 242. #> 2 Adelaide South Austra… Holiday 108. 133. 148. 160. 181. 224. #> 3 Adelaide South Austra… Other 25.9 42.3 50.6 55.9 70.4 107. #> 4 Adelaide South Austra… Visiti… 137. 177. 194. 214. 237. 270. #> 5 Adelaide H… South Austra… Busine… 0 0 0.763 1.79 4.56 28.6 #> 6 Adelaide H… South Austra… Holiday 0 5.29 7.51 11.3 15.5 35.8 #> 7 Adelaide H… South Austra… Other 0 0 0.685 1.18 2.44 8.95 #> 8 Adelaide H… South Austra… Visiti… 0.778 8.15 10.2 14.1 19.3 81.1 #> 9 Alice Spri… Northern Ter… Busine… 1.01 8.41 11.3 15.8 21.5 34.1 #> 10 Alice Spri… Northern Ter… Holiday 2.81 14.6 24.1 36.2 46.6 76.5 #> # … with 294 more rows