函数和数据快捷方式歧义.x, ~

Question

library(tidyverse)
#> Warning: package 'tidyverse' was built under R version 3.6.3
#> Warning: package 'ggplot2' was built under R version 3.6.3
#> Warning: package 'tidyr' was built under R version 3.6.3
#> Warning: package 'purrr' was built under R version 3.6.3
#> Warning: package 'dplyr' was built under R version 3.6.3
#> Warning: package 'stringr' was built under R version 3.6.3
#> Warning: package 'forcats' was built under R version 3.6.3
library(gapminder)
#> Warning: package 'gapminder' was built under R version 3.6.2
gap <- gapminder %>%
  filter(continent == "Asia") %>%
  mutate(yr1952 = year - 1952)

gap_nested <- gap %>%
  group_by(country) %>%
  nest()

gap_nested
#> # A tibble: 33 x 2
#> # Groups:   country [33]
#>    country          data             
#>    <fct>            <list>           
#>  1 Afghanistan      <tibble [12 x 6]>
#>  2 Bahrain          <tibble [12 x 6]>
#>  3 Bangladesh       <tibble [12 x 6]>
#>  4 Cambodia         <tibble [12 x 6]>
#>  5 China            <tibble [12 x 6]>
#>  6 Hong Kong, China <tibble [12 x 6]>
#>  7 India            <tibble [12 x 6]>
#>  8 Indonesia        <tibble [12 x 6]>
#>  9 Iran             <tibble [12 x 6]>
#> 10 Iraq             <tibble [12 x 6]>
#> # ... with 23 more rows
gap_nested$data[[1]]
#> # A tibble: 12 x 6
#>    continent  year lifeExp      pop gdpPercap yr1952
#>    <fct>     <int>   <dbl>    <int>     <dbl>  <dbl>
#>  1 Asia       1952    28.8  8425333      779.      0
#>  2 Asia       1957    30.3  9240934      821.      5
#>  3 Asia       1962    32.0 10267083      853.     10
#>  4 Asia       1967    34.0 11537966      836.     15
#>  5 Asia       1972    36.1 13079460      740.     20
#>  6 Asia       1977    38.4 14880372      786.     25
#>  7 Asia       1982    39.9 12881816      978.     30
#>  8 Asia       1987    40.8 13867957      852.     35
#>  9 Asia       1992    41.7 16317921      649.     40
#> 10 Asia       1997    41.8 22227415      635.     45
#> 11 Asia       2002    42.1 25268405      727.     50
#> 12 Asia       2007    43.8 31889923      975.     55

gap_fitted <- gap_nested %>%
  mutate(fit = map(data, ~ lm(lifeExp ~ yr1952, data = .x)))
gap_fitted
#> # A tibble: 33 x 3
#> # Groups:   country [33]
#>    country          data              fit   
#>    <fct>            <list>            <list>
#>  1 Afghanistan      <tibble [12 x 6]> <lm>  
#>  2 Bahrain          <tibble [12 x 6]> <lm>  
#>  3 Bangladesh       <tibble [12 x 6]> <lm>  
#>  4 Cambodia         <tibble [12 x 6]> <lm>  
#>  5 China            <tibble [12 x 6]> <lm>  
#>  6 Hong Kong, China <tibble [12 x 6]> <lm>  
#>  7 India            <tibble [12 x 6]> <lm>  
#>  8 Indonesia        <tibble [12 x 6]> <lm>  
#>  9 Iran             <tibble [12 x 6]> <lm>  
#> 10 Iraq             <tibble [12 x 6]> <lm>  
#> # ... with 23 more rows
gap_fitted$fit[[1]]
#> 
#> Call:
#> lm(formula = lifeExp ~ yr1952, data = .x)
#> 
#> Coefficients:
#> (Intercept)       yr1952  
#>     29.9073       0.2753

gap_fitted <- gap_fitted %>%
  mutate(
    intercept = map_dbl(fit, ~ coef(.x)[["(Intercept)"]]),
    slope = map_dbl(fit, ~ coef(.x)[["yr1952"]])
  )
gap_fitted
#> # A tibble: 33 x 5
#> # Groups:   country [33]
#>    country          data              fit    intercept slope
#>    <fct>            <list>            <list>     <dbl> <dbl>
#>  1 Afghanistan      <tibble [12 x 6]> <lm>        29.9 0.275
#>  2 Bahrain          <tibble [12 x 6]> <lm>        52.7 0.468
#>  3 Bangladesh       <tibble [12 x 6]> <lm>        36.1 0.498
#>  4 Cambodia         <tibble [12 x 6]> <lm>        37.0 0.396
#>  5 China            <tibble [12 x 6]> <lm>        47.2 0.531
#>  6 Hong Kong, China <tibble [12 x 6]> <lm>        63.4 0.366
#>  7 India            <tibble [12 x 6]> <lm>        39.3 0.505
#>  8 Indonesia        <tibble [12 x 6]> <lm>        36.9 0.635
#>  9 Iran             <tibble [12 x 6]> <lm>        45.0 0.497
#> 10 Iraq             <tibble [12 x 6]> <lm>        50.1 0.235
#> # ... with 23 more rows

^{由 reprex package (v0.3.0)}

于 2020-07-29 创建

任何人都可以向我解释一下上面代码中使用的快捷方式的实际文本，如 ~、.x。有时简单的“。”将会被使用。是否有用实际名称代替这些快捷方式的包。代词.x是否表示map函数中的data参数？

Answer 1

符号 ~ 表示您正在处理公式。

在 rlang 包中（以及在 tidyverse 包中的扩展）公式可以解释为匿名函数。

因此如果你写：

map(data, ~ lm(lifeExp ~ yr1952, data = .x))

就像写作：

map(data, function(x) lm(lifeExp ~ yr1952, data = x))

第一种方法非常有用，比匿名函数更具可读性。

当您处理多个输入时，您还可以使用 .y 甚至 ..1、..2 等

允许魔法的函数是purrr::as_mapper，最终调用rlang::as_function。

这是否回答了您的问题？

函数和数据快捷方式歧义.x, ~

Function and data shortcuts ambiguity .x, ~

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