Chapter 5 Code Optimisation

5.1 Compose functions

5.1.1 I want to…

Write a function that wraps other functions.

5.1.2 Here’s how to:

library(broom)
tidy_lm <- compose(tidy, lm)
tidy_lm(Sepal.Length ~ Species, data = iris) 

term	estimate	std.error	statistic	p.value
(Intercept)	5.006	0.0728022	68.761639	0
Speciesversicolor	0.930	0.1029579	9.032819	0
Speciesvirginica	1.582	0.1029579	15.365506	0

5.1.3 Ok, but why?

compose(y, x) composes a function that will do y(x()).

5.1.4 See also

• A Crazy Little Thing Called {purrr} - Part 5: code optimization

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5.2 Prefilled functions

5.2.1 I want to…

Prefill a function so that I won’t have to specify the arguments any time I use it.

5.2.2 Here’s how to:

mean_na_rm <- partial(mean, na.rm = TRUE)
mean_na_rm(airquality$Ozone)

## [1] 42.12931

5.2.3 Ok, but why?

partial(f, args = "x") returns a function with a prefilled function with args = "x".

5.2.4 See also

• A Crazy Little Thing Called {purrr} - Part 5: code optimization

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5.3 Negate a function

5.3.1 I want to…

Inverse what a function does.

5.3.2 Here’s how to:

is_not_numeric <- negate(is.numeric)
is_not_numeric("this")

## [1] TRUE

5.3.3 Ok, but why?

negate(f) returns a function that does !f(...).

5.3.4 See also

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5.4 Negate a function

5.4.1 I want to…

Change what input my function can take.

5.4.2 Here’s how to:

lifted_is_na <- lift_ld(is.na)
lifted_is_na(1,2,3,NA)

## [1] FALSE FALSE FALSE  TRUE

5.4.3 Ok, but why?

The lift_* family of functions change the type of input of other function.

5.4.4 See also