A família de funções map

Machine Learning no tidyverse

Dmitriy (Dima) Gorenshteyn

Lead Data Scientist, Memorial Sloan Kettering Cancer Center

Fluxo de colunas de lista

A função map

Média da população por país

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

Média da população por país

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

[[1]]
[1] 23129438

[[2]]
[1] 30783053

[[3]]
[1] 16074837

[[4]]
[1] 7746272

2: Trabalhe com colunas de 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: Simplifique colunas de 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

Fluxo de colunas de lista

Trabalhe e simplifique colunas de lista com map_*()

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

Trabalhe e simplifique colunas de lista com 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

Crie modelos com 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>

Vamos mapear algo!

Machine Learning no tidyverse