Question

我正在开发一个自定义函数，其目标是为分组变量..f的所有组合运行一个函数（grouping.var）提供给定的数据帧，然后使用{将这些结果整理到数据帧中{1}}软件包。

这是我编写的自定义函数。请注意，broom提供给...，而..f方法的附加参数是通过broom::tidy列表提供的。

tidy.args

如下面的示例所示，尽管此功能有效，但我怀疑# setup set.seed(123) library(tidyverse) options(pillar.sigfig = 8) # custom function grouped_tidy <- function(data, grouping.vars, ..f, ..., tidy.args = list()) { # check how many variables were entered for grouping variable vector grouping.vars <- as.list(rlang::quo_squash(rlang::enquo(grouping.vars))) grouping.vars <- if (length(grouping.vars) == 1) { grouping.vars } else { grouping.vars[-1] } # quote all argument to `..f` dots <- rlang::enquos(...) # running the grouped analysis df_results <- data %>% dplyr::group_by(.data = ., !!!grouping.vars, .drop = TRUE) %>% dplyr::group_map( .tbl = ., .f = ~ broom::tidy( x = rlang::exec(.fn = ..f, !!!dots, data = .x), unlist(tidy.args) )) # return the final dataframe with results return(df_results) }列表是否得到正确评估，因为无论选择哪种tidy.args，我总是得到相同的结果，小数点后四位地方。

95％CI

conf.level

99％CI

# using the function to get 95% CI
grouped_tidy(
  data = ggplot2::diamonds,
  grouping.vars = c(cut),
  ..f = stats::lm,
  formula = price ~ carat - 1,
  tidy.args = list(conf.int = TRUE, conf.level = 0.95)
)

#> # A tibble: 5 x 8
#> # Groups:   cut [5]
#>   cut       term   estimate std.error statistic p.value  conf.low conf.high
#>   <ord>     <chr>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>     <dbl>
#> 1 Fair      carat 4510.7919 42.614474 105.85117       0 4427.2062 4594.3776
#> 2 Good      carat 5260.8494 27.036670 194.58200       0 5207.8454 5313.8534
#> 3 Very Good carat 5672.5054 18.675939 303.73334       0 5635.8976 5709.1132
#> 4 Premium   carat 5807.1392 16.836474 344.91422       0 5774.1374 5840.1410
#> 5 Ideal     carat 5819.4837 15.178657 383.39911       0 5789.7324 5849.2350

关于如何更改函数，以便# using the function to get 99% CI grouped_tidy( data = ggplot2::diamonds, grouping.vars = c(cut), ..f = stats::lm, formula = price ~ carat - 1, tidy.args = list(conf.int = TRUE, conf.level = 0.99) ) #> # A tibble: 5 x 8 #> # Groups: cut [5] #> cut term estimate std.error statistic p.value conf.low conf.high #> <ord> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Fair carat 4510.7919 42.614474 105.85117 0 4427.2062 4594.3776 #> 2 Good carat 5260.8494 27.036670 194.58200 0 5207.8454 5313.8534 #> 3 Very Good carat 5672.5054 18.675939 303.73334 0 5635.8976 5709.1132 #> 4 Premium carat 5807.1392 16.836474 344.91422 0 5774.1374 5840.1410 #> 5 Ideal carat 5819.4837 15.178657 383.39911 0 5789.7324 5849.2350正确评估参数列表的任何想法吗？

Answer 1

set.seed(123)
library(tidyverse)
options(pillar.sigfig = 8)

grouped_tidy <- function(data,
                         grouping.vars,
                         ..f,
                         ...,
                         tidy.args = list()) {

  # functions passed to group_map must accept
  # .x and .y arguments, where .x is the data

  tidy_group <- function(.x, .y) {

    # presumes ..f won't explode if called with these args
    model <- ..f(..., data = .x)

    # mild variation on do.call to call function with
    # list of arguments
    rlang::exec(broom::tidy, model, !!!tidy.args)
  }

  data %>%
    group_by(!!!grouping.vars, .drop = TRUE) %>%
    group_map(tidy_group) %>% 
    ungroup()  # don't get bitten by groups downstream
}

grouped_tidy(
  data = ggplot2::diamonds,

  # wrap grouping columns in vars() like in scoped dplyr verbs
  grouping.vars = vars(cut),  

  ..f = stats::lm,
  formula = price ~ carat - 1,
  tidy.args = list(conf.int = TRUE, conf.level = 0.95)
)
#> # A tibble: 5 x 8
#>   cut       term   estimate std.error statistic p.value  conf.low conf.high
#>   <ord>     <chr>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>     <dbl>
#> 1 Fair      carat 4510.7919 42.614474 105.85117       0 4427.2062 4594.3776
#> 2 Good      carat 5260.8494 27.036670 194.58200       0 5207.8454 5313.8534
#> 3 Very Good carat 5672.5054 18.675939 303.73334       0 5635.8976 5709.1132
#> 4 Premium   carat 5807.1392 16.836474 344.91422       0 5774.1374 5840.1410
#> 5 Ideal     carat 5819.4837 15.178657 383.39911       0 5789.7324 5849.2350

^{由reprex package（v0.2.1）于2019-02-23创建}

列表中提供的参数未得到正确评估

1 个答案: