features {fabletools}

Extract features from a dataset

Description

Create scalar valued summary features for a dataset from feature functions.

Usage

features(.tbl, .var, features, ...)

features_at(.tbl, .vars, features, ...)

features_all(.tbl, features, ...)

features_if(.tbl, .predicate, features, ...)

Arguments

.tbl

A dataset

.var

An expression that produces a vector from which the features are computed.

features

A list of functions (or lambda expressions) for the features to compute. feature_set() is a useful helper for building sets of features.

...

Additional arguments to be passed to each feature. These arguments will only be passed to features which use it in their formal arguments (base::formals()), and not via their .... While passing na.rm = TRUE to stats::var() will work, it will not for base::mean() as its formals are x and .... To more precisely pass inputs to each function, you should use lambdas in the list of features (~ mean(., na.rm = TRUE)).

.vars

A tidyselect compatible selection of the column(s) to compute features on.

.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.

Details

Lists of available features can be found in the following pages:

• Features by package

• Features by tag

See Also

feature_set()

Examples

# Provide a set of functions as a named list to features.
library(tsibble)
tourism %>% 
  features(Trips, features = list(mean = mean, sd = sd))

# Search and use useful features with `feature_set()`. 


library(feasts)

tourism %>% 
  features(Trips, features = feature_set(tags = "autocorrelation"))

# Best practice is to use anonymous functions for additional arguments
tourism %>% 
  features(Trips, list(~ quantile(., probs=seq(0,1,by=0.2))))