将列表传递给函数参数？

Question

大图：我希望我的用户定义函数像循环一样遍历参数列表（或向量）。 （在这种情况下，参数是一个字符串）

get_avg2 <- function(v_name) {

avg <- "_Average"      

data_1 <-  PFF_College_Defense_data %>% 
  dplyr::group_by(Name) %>% 
  dplyr::summarise("{{ v_name }}_{avg}" := mean({{ v_name }}, na.rm = TRUE))

PFF_NCAA_Average_grades <- merge(PFF_NCAA_Average_grades, data_1, by = "Name")

return(PFF_NCAA_Average_grades)

}


v_names <- list("hits", "tackles", "forced_fumbles")

for (i in v_names) {

get_avg2(i)

}
#didn't work

PFF_NCAA_Average_grades <- purrr::map_df(v_names, get_avg2)

#didnt' work

我正在尝试从数据框中按组获取平均值并将其存储为另一个数据框。我编写了一个 UDF 来接受一个参数作为来自原始数据库的变量名，然后 UDF 运行计算并将其合并到我预先格式化以适合 UDF 的结果的已创建数据帧。我想将一个列表传递给我的函数，并让它像循环一样遍历该列表。虽然我只是在概念上似乎无法掌握这个概念或 purrr::map 的使用，但我认为这可以解决问题。

我知道我能做到：

PFF_NCAA_Average_grades <- get_avg2(hits)

PFF_NCAA_Average_grades <- get_avg2(tackles)

PFF_NCAA_Average_grades <- get_avg2(forced_fumbles)

但这看起来又丑又慢。有人可以帮我从概念上理解最好的方法吗？

提前致谢！！！

*** 已使用 REPREX 更新 ******

library(tidyverse)

data_sample <- data.frame(
  Name = c("Dalton Campbell", "Dalton Campbell", "Dalton Campbell", "Andre Walker", "Andre Walker", "Andre Walker"),
  Defense_Grade = c(88, 86, 92, 94, 97, 95),
  Tackle_Grade = c(66, 69, 72, 74, 76, 78),
  Coverage_Grade = c(44, 43, 44, 76, 73, 78)
)

#Here I set up the dataframe which the function will bind to 
data_sample_averages <-  data_sample %>% 
  group_by(Name) %>% 
  dplyr::summarise(Defense_Grade_Average = mean(Defense_Grade))
#> `summarise()` ungrouping output (override with `.groups` argument)


#Function which computes average of variable (the only argument) and merges it back to data_sample_averages
get_avg2 <- function(v_name) {
  
  avg <- "_Average"      
  
  data_1 <-  data_sample %>% 
    dplyr::group_by(Name) %>% 
    dplyr::summarise("{{ v_name }}_{avg}" := mean({{ v_name }}, na.rm = TRUE))
  
  data_sample_averages <- merge(data_sample_averages, data_1, by = "Name")
  
  return(data_sample_averages)

}

#This works - it computers the average of Tackle_Grade and binds it to data_sample_averages
data_sample_averages <- get_avg2(Tackle_Grade)
#> `summarise()` ungrouping output (override with `.groups` argument)

#shows you the averages
print(data_sample_averages)
#>              Name Defense_Grade_Average Tackle_Grade__Average
#> 1    Andre Walker              95.33333                    76
#> 2 Dalton Campbell              88.66667                    69


#Neither of these work - this is where I'm stuck
variable_list <- list("Defense_Grade", "Tackle_Grade", "Coverage Grade")

data_sample_averages <- lapply(variable_list, get_avg2)
#> Warning in mean.default(~"Defense_Grade", na.rm = TRUE): argument is not numeric
#> or logical: returning NA

#> Warning in mean.default(~"Defense_Grade", na.rm = TRUE): argument is not numeric
#> or logical: returning NA
#> `summarise()` ungrouping output (override with `.groups` argument)
#> Warning in mean.default(~"Tackle_Grade", na.rm = TRUE): argument is not numeric
#> or logical: returning NA
#> Warning in mean.default(~"Tackle_Grade", na.rm = TRUE): argument is not numeric
#> or logical: returning NA
#> `summarise()` ungrouping output (override with `.groups` argument)
#> Warning in mean.default(~"Coverage Grade", na.rm = TRUE): argument is not
#> numeric or logical: returning NA
#> Warning in mean.default(~"Coverage Grade", na.rm = TRUE): argument is not
#> numeric or logical: returning NA
#> `summarise()` ungrouping output (override with `.groups` argument)

data_sample_averages <- purrr::map(variable_list, get_avg2)
#> Warning in mean.default(~"Defense_Grade", na.rm = TRUE): argument is not numeric
#> or logical: returning NA
#> Warning in mean.default(~"Defense_Grade", na.rm = TRUE): argument is not numeric
#> or logical: returning NA
#> `summarise()` ungrouping output (override with `.groups` argument)
#> Warning in mean.default(~"Tackle_Grade", na.rm = TRUE): argument is not numeric
#> or logical: returning NA
#> Warning in mean.default(~"Tackle_Grade", na.rm = TRUE): argument is not numeric
#> or logical: returning NA
#> `summarise()` ungrouping output (override with `.groups` argument)
#> Warning in mean.default(~"Coverage Grade", na.rm = TRUE): argument is not
#> numeric or logical: returning NA
#> Warning in mean.default(~"Coverage Grade", na.rm = TRUE): argument is not
#> numeric or logical: returning NA
#> `summarise()` ungrouping output (override with `.groups` argument)

这感觉就像一个非常简单的操作 - 从一个数据帧按组计算平均值并将其绑定到另一个数据帧 - 这并不是我真正苦苦挣扎的部分。我想要的是让我的函数自动迭代一系列参数。我希望能够快速构建一个变量列表（或向量 - 我没有设置使用列表）并将其作为参数传递给函数，以便它使用我提供的变量构建一个数据框。但我愿意接受这样的想法，即我在概念上有些错误，我应该使用循环、呼噜声、映射等，或者改变我的函数的编写方式？

Answer 1

您的独立示例和您传递列表的函数之间的区别是 - 在独立示例中，您传递的是不带引号的变量 (get_avg2(Tackle_Grade))，而在传递向量/列表时，您传递的是带引号的变量。 (variable_list <- list("Defense_Grade", "Tackle_Grade", "Coverage Grade"))。

在列表中传递不带引号的变量并不容易，因此我们最好更改可以接受带引号的变量/字符串的函数。为此，您需要将功能更改为：

library(dplyr)

get_avg2 <- function(v_name) {
  avg <- "_Average"      
  data_1 <-  data_sample %>% 
    dplyr::group_by(Name) %>% 
    dplyr::summarise(!!paste0(v_name, avg):=mean(.data[[v_name]], na.rm = TRUE))
  data_sample_averages <- merge(data_sample_averages, data_1, by = "Name")
  
  return(data_sample_averages)
  
}

对于单个值，您将其称为：

get_avg2("Tackle_Grade")

#             Name Defense_Grade_Average Tackle_Grade_Average
#1    Andre Walker              95.33333                   76
#2 Dalton Campbell              88.66667                   69

对于值的列表/向量，您可以使用 lapply ：

variable_list <- c("Defense_Grade", "Tackle_Grade", "Coverage_Grade")
lapply(variable_list, get_avg2)

#[[1]]
#             Name Defense_Grade_Average.x Defense_Grade_Average.y
#1    Andre Walker                95.33333                95.33333
#2 Dalton Campbell                88.66667                88.66667

#[[2]]
#             Name Defense_Grade_Average Tackle_Grade_Average
#1    Andre Walker              95.33333                   76
#2 Dalton Campbell              88.66667                   69

#[[3]]
#             Name Defense_Grade_Average Coverage_Grade_Average
#1    Andre Walker              95.33333               75.66667
#2 Dalton Campbell              88.66667               43.66667

---

但是，理想情况下，您不会使用 lapply/map 逐个传递这些变量，而是使用 across

data_sample %>%
  group_by(Name) %>%
  summarise(across(ends_with('Grade'), mean, na.rm = TRUE))

#.   Name            Defense_Grade Tackle_Grade Coverage_Grade
#  <chr>                   <dbl>        <dbl>          <dbl>
#1 Andre Walker             95.3           76           75.7
#2 Dalton Campbell          88.7           69           43.7

但也许你正在为别的东西构建这个函数。