La famille de fonctions map

Machine Learning dans le tidyverse

Dmitriy (Dima) Gorenshteyn

Lead Data Scientist, Memorial Sloan Kettering Cancer Center

Flux de travail des colonnes de listes

La fonction map

Moyenne de population par pays

mean(nested$data[[1]]$population)
[1] 23129438

Moyenne de population par pays

map(.x = nested$data, .f = ~mean(.x$population))

[[1]]
[1] 23129438

[[2]]
[1] 30783053

[[3]]
[1] 16074837

[[4]]
[1] 7746272

2 : Travailler avec des colonnes de listes – map() et mutate()

pop_df <- nested %>% 
  mutate(pop_mean = map(data, ~mean(.x$population)))

pop_df

# A tibble: 77 x 3
   country    data              pop_mean 
   <fct>      <list>            <list>   
 1 Algeria    <tibble [52 × 6]> <dbl [1]>
 2 Argentina  <tibble [52 × 6]> <dbl [1]>
 3 Australia  <tibble [52 × 6]> <dbl [1]>
 4 Austria    <tibble [52 × 6]> <dbl [1]>
 5 Bangladesh <tibble [52 × 6]> <dbl [1]>

3 : Simplifier des colonnes de listes – unnest()

pop_df %>% 
  unnest(pop_mean)

# A tibble: 77 x 3
   country    data               pop_mean
   <fct>      <list>                <dbl>
 1 Algeria    <tibble [52 × 6]>  23129438
 2 Argentina  <tibble [52 × 6]>  30783053
 3 Australia  <tibble [52 × 6]>  16074837
 4 Austria    <tibble [52 × 6]>   7746272
 5 Bangladesh <tibble [52 × 6]>  97649407

Flux de travail des colonnes de listes

Travailler et simplifier les colonnes de listes avec map_*()

fonction	renvoie
map()	liste
map_dbl()	double
map_lgl()	logique
map_chr()	caractère
map_int()	entier

Travailler et simplifier avec map_dbl()

nested %>% 
  mutate(pop_mean = map_dbl(data, ~mean(.x$population)))

# A tibble: 77 x 3
   country    data               pop_mean
   <fct>      <list>                <dbl>
 1 Algeria    <tibble [52 × 6]>  23129438
  2 Argentina  <tibble [52 × 6]>  30783053
 3 Australia  <tibble [52 × 6]>  16074837
 4 Austria    <tibble [52 × 6]>   7746272
 5 Bangladesh <tibble [52 × 6]>  97649407

Créer des modèles avec map()

nested %>%
   mutate(model = map(data, ~lm(formula = population~fertility, 
             data = .x)))

# A tibble: 77 x 3
   country    data              model   
   <fct>      <list>            <list>  
 1 Algeria    <tibble [52 × 6]> <S3: lm>
 2 Argentina  <tibble [52 × 6]> <S3: lm>
 3 Australia  <tibble [52 × 6]> <S3: lm>
 4 Austria    <tibble [52 × 6]> <S3: lm>
 5 Bangladesh <tibble [52 × 6]> <S3: lm>

Cartographions quelque chose !

Machine Learning dans le tidyverse