La famiglia di funzioni map

Machine Learning nel tidyverse

Dmitriy (Dima) Gorenshteyn

Lead Data Scientist, Memorial Sloan Kettering Cancer Center

Flusso di lavoro con colonne lista

La funzione map

Media della popolazione per paese

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

Media della popolazione per paese

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

[[1]]
[1] 23129438

[[2]]
[1] 30783053

[[3]]
[1] 16074837

[[4]]
[1] 7746272

2: Lavorare con colonne lista - map() e 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: Semplificare colonne lista - 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

Flusso di lavoro con colonne lista

Lavorare e semplificare colonne lista con map_*()

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

Lavorare e semplificare con 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

Creare modelli con 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>

Mappiamo qualcosa!

Machine Learning nel tidyverse