La famille de fonctions map

Machine Learning dans le tidyverse

Dmitriy (Dima) Gorenshteyn

Lead Data Scientist, Memorial Sloan Kettering Cancer Center

Flux de 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 les 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 colonnes de listes

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

function	returns
map()	list
map_dbl()	double
map_lgl()	logical
map_chr()	character
map_int()	integer

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