Warum saubererer Code?

Fortgeschrittene funktionale Programmierung mit purrr

Colin Fay

Data Scientist & R Hacker

Wo ist Walter?

library(broom)
library(dplyr)
lm(Sepal.Length ~ Species, data=iris) %>% tidy() %>% filter(p.value < 0.05)
lm(Pepal.Length ~ Species, data=iris) %>% tidy() %>% filter(p.value < 0.05)
lm(Sepal.Width ~ Species, data=iris) %>% tidy() %>% filter(p.value < 0.05)
lm(Sepal.Length ~ Species, data=iris) %>% tidy() %>% ilter(p.value < 0.05)

Walter finden

library(purrr)
tidy_iris_lm <- compose(
  as_mapper(~ filter(.x, p.value < 0.05)), 
  tidy, 
  partial(lm, data=iris, na.action = na.fail)
)

list(
  Petal.Length ~ Petal.Width, 
  Petal.Width ~ Sepal.Width, 
  Sepal.Width ~ Sepal.Length
) %>% map(tidy_iris_lm)

Was ist sauberer Code?

Sauberer Code ist:

Schlank
Lesbar
Verständlich
Wartbar

compose()

Funktionen komponieren:

library(purrr)

rounded_mean <- compose(round, mean)
rounded_mean(1:2811)

Saubereren Code komponieren

# VON
round(mean(1:10))
round(mean(1:100))
round(mean(1:1000))
round(mean(1:10000))

# ZU
round(median(1:10))
round(median(1:100))
round(median(1:1000))
round(median(1:10000))

-> 4 Änderungen

# VON
my_stats <- compose(round, mean)
my_stats(1:10)
my_stats(1:100)
my_stats(1:1000)
my_stats(1:10000)

# ZU
my_stats <- compose(round, median)
my_stats(1:10)
my_stats(1:100)
my_stats(1:1000)
my_stats(1:10000)

-> 1 Änderung

Lass uns üben!

Fortgeschrittene funktionale Programmierung mit purrr