Nettoyer vos données avec des mappeurs

Programmation fonctionnelle intermédiaire avec purrr

Colin Fay

Data Scientist & R Hacker at ThinkR

Nommer vos objets

set_names(): attribue des noms à une liste sans nom

names(visits2016)

length(visits2016)

month.abb

NULL

12

"Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"

visits2016 <- set_names(visits2016, month.abb)
names(visits2016)

"Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"

Définir des noms avec map() :

all_visits <- list(visits2015, visits2016, visits2017)
named_all_visits <- map(all_visits, ~ set_names(.x, month.abb))
names(named_all_visits[[1]])

"Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"

names(named_all_visits[[2]])

"Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"

names(named_all_visits[[3]])

"Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"

keep(): extraire les éléments qui respectent une condition

# Quel mois a reçu plus de 30000 visites ?
over_30000 <- keep(visits2016, ~ sum(.x) > 30000)
names(over_30000)

"Jan" "Mar" "Apr" "May" "Jul" "Aug" "Oct" "Nov"

limit <- as_mapper(~ sum(.x) > 30000)
# Quel mois a reçu plus de 30000 visites ?
over_mapper <- keep(visits2016, limit)
names(over_mapper)

"Jan" "Mar" "Apr" "May" "Jul" "Aug" "Oct" "Nov"

discard(): retirer les éléments qui respectent une condition

# Quel mois a reçu moins de 30000 visites ?
under_30000 <- discard(visits2016, ~ sum(.x) > 30000)
names(under_30000)

"Feb" "Jun" "Sep" "Dec"

limit <- as_mapper(~ sum(.x) > 30000)
# Quel mois a reçu moins de 30000 visites ?
under_mapper <- discard(visits2016, limit)
names(under_mapper)

"Feb" "Jun" "Sep" "Dec"

Utiliser map() et keep() :

df_list <- list(iris, airquality) %>% map(head)
map(df_list, ~ keep(.x, is.factor))

[[1]]
  Species
1  setosa
2  setosa
3  setosa
4  setosa
5  setosa
6  setosa

[[2]]
data frame with 0 columns and 6 rows

Programmation fonctionnelle intermédiaire avec purrr