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这感觉就像一个非常简单的操作 - 从一个数据帧中按组计算平均值并将其合并到另一个预先格式化的数据帧 - 我的 UDF 做到了这一点，而这并不是我真正苦苦挣扎的部分。

我想要的是让我的函数遍历一系列（列表、向量等）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，以便它构建一个包含我在该列表中提供的所有变量的数据框。我的真实数据库有 50 多个变量，我想用不同的变量组合制作所有不同类型的新数据框。一个列表可能有 5 个变量，另一个可能有 25 个。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、应用、其他包等，或者更改我的函数是怎么写的？我错过了什么？

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
#However my real dataframe has 50+ columns and I don't want to copy and paste this line 50 times, changing the argument every time.
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
#I want my function to iterate through a list of arguments which are essentially just character #strings in order for the UDF to work 
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)
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1 回答 1

2

你说你没有设置使用列表，所以我使用向量。

我的解决方案依赖于最新版本的函数dplyr：across()函数。

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)
)

# The function
compute_avg <- function(.data, names){
    
    names_quo <- enquos(names)  
  
    .data %>%
    group_by(Name) %>%
    summarise(
      across(
        .cols = !!!names_quo,
        .fns = ~ mean(.x, na.rm = TRUE),
        .names = "{.col}_Average"
      )
    )
}

compute_avg(.data = data_sample, names = c(Defense_Grade, Tackle_Grade))

# A tibble: 2 x 3
  Name            Defense_Grade_Average Tackle_Grade_Average
  <chr>                           <dbl>                <dbl>
1 Andre Walker                     95.3                   76
2 Dalton Campbell                  88.7                   69
于 2020-12-17T13:07:18.997 回答