如何使用正则表达式 group_by 和汇总多个变量？

Question

我想使用正则表达式来识别要用于 group_by 的变量并有效地汇总我的数据。我不能单独做，因为我有大量的变量要汇总，并且每次都需要动态传递 group_by 的变量。 data.table 接受使用正则表达式传递分组变量，但不接受汇总变量。到目前为止，我使用 tidyverse 的尝试也没有成功。任何帮助将不胜感激。

My data:

    tempDF <- structure(list(d1 = c("A", "B", "C", "A", "C"), d2 = c(40L, 50L, 20L, 50L, 20L), 
        d3 = c(20L, 40L, 50L, 40L, 50L), d4 = c(60L, 30L, 30L,60L, 30L), p_A = c(1L, 
        3L, 2L, 3L, 2L), p_B = c(3L, 4L, 3L, 3L, 4L), p_C = c(2L, 1L, 1L,2L, 1L), p4 = c(5L, 
        5L, 4L, 5L, 4L)), class = "data.frame", row.names = c(NA, -5L))

    View(tempDF)    
    lLevels<-c("d1")
    lContinuum<-c("p_A", "p_B", "p_C")


My attempts:

    setDT(tempDF)[ , list(group_means = mean(eval((paste0(lContinuum)))), by=eval((paste0(lLevels))))] 
       group_means by
    1:          NA d1
    Warning message:
    In mean.default(eval((paste0(lContinuum)))) :
      argument is not numeric or logical: returning NA

    But a single variable works:
    setDT(tempDF)[ , list(group_means = mean(p_A)), by=eval((paste0(lLevels)))]                                            
    setDT(tempDF)[ , list(group_means = mean(p_B)), by=eval((paste0(lLevels)))]                                            
    setDT(tempDF)[ , list(group_means = mean(p_C)), by=eval((paste0(lLevels)))]                                            


Expected output:

    tempDF %>%
    group_by(d1) %>%
    summarise(p_A_mean = mean(p_A), p_B_mean = mean(p_B), p_C_mean = mean(p_C))

    # A tibble: 3 x 4
      d1    p_A_mean p_B_mean p_C_mean
      <chr>    <dbl>    <dbl>    <dbl>
    1 A            2      3          2
    2 B            3      4          1
    3 C            2      3.5        1

Answer 1

虽然它看起来有点迂回，但将其重塑为长格式将允许不仅按 d1 分组，而且还可以按 p_A ... p_C 中的许多值进行分组数据集。

编辑：还添加了代码以通过正则表达式保留某些列 (d_cols)。

library(tidyverse)

tempDF <- structure(
  list(d1 = c("A", "B", "C", "A", "C"), 
       d2 = c(40L, 50L, 20L, 50L, 20L), 
       d3 = c(20L, 40L, 50L, 40L, 50L), 
       d4 = c(60L, 30L, 30L,60L, 30L),
       d5 = c("AA", "BB", "CC", "AA", "CC"), 
       p_A = c(1L, 3L, 2L, 3L, 2L), 
       p_B = c(3L, 4L, 3L, 3L, 4L), 
       p_C = c(2L, 1L, 1L,2L, 1L), 
       p4 = c(5L, 5L, 4L, 5L, 4L)), 
  class = "data.frame", 
  row.names = c(NA, -5L))

# columns of d to keep, in strings
d_cols <- str_subset(colnames(tempDF), "d[15]")

tempDF %>% 
  pivot_longer(cols = matches("p_")) %>% 
  group_by(!!!syms(d_cols), name) %>% 
  summarize(mean  = mean(value)) %>% 
  pivot_wider(id_cols = d_cols,
              values_from = mean,
              names_prefix = "mean_")
#> # A tibble: 3 x 5
#> # Groups:   d1, d5 [3]
#>   d1    d5    mean_p_A mean_p_B mean_p_C
#>   <chr> <chr>    <dbl>    <dbl>    <dbl>
#> 1 A     AA           2      3          2
#> 2 B     BB           3      4          1
#> 3 C     CC           2      3.5        1

^{由 reprex package (v0.3.0)}

于 2019-10-19 创建

Answer 2

我确信这可以变得更高效/简洁但符合规范：

summarise_df <- function(df, grouping_var){

  # Store string of the grouping var name:

  grouping_vec <- gsub(".*[$]", "", deparse(substitute(grouping_var)))

  # split apply combine summary - return dataframe:

  tmpdf_list <- lapply(split(df[,sapply(df, is.numeric)], df[,grouping_vec]),
                  function(x){sapply(x, function(y){mean(y)})})


}

tmp <- do.call(rbind, summarise_df(df, df$d1))

df <- data.frame(cbind(d1 = row.names(tmp), tmp), row.names = NULL)

摘要变量也是动态的：

# 
summarise_df <- function(df, grouping_var, summary_vars){

  # Store string of the grouping var name:

  grouping_vec <- gsub(".*[$]", "", deparse(substitute(grouping_var)))

  # split apply combine summary - return dataframe:

  tmpdf_list <- lapply(split(df[,summary_vars], df[,grouping_vec]),
                       function(x){sapply(x, function(y){mean(y)})})


}

tmp <- do.call(rbind, summarise_df(df, df$d1, c("p_A", "p_B", "p_C")))

tmp_df <- data.frame(cbind(d1 = row.names(tmp), tmp), row.names = NULL)

Answer 3

data.table方法非常简单：

library(data.table)

setDT(tempDF)

tempDF[, lapply(.SD, mean),
         by = lLevels,
        .SDcols = lContinuum]

   d1 p_A p_B p_C
1:  A   2 3.0   2
2:  B   3 4.0   1
3:  C   2 3.5   1

dplyr 中的类似方法是：

library(dplyr)
tempDF%>%
  group_by_at(lLevels)%>%
  summarize_at(lContinuum, mean)

# A tibble: 3 x 4
  d1      p_A   p_B   p_C
  <chr> <dbl> <dbl> <dbl>
1 A         2   3       2
2 B         3   4       1
3 C         2   3.5     1

无论哪种情况，您都可以将 lLevels 和 lContinuum 替换为正则表达式。 dplyr 选项还允许 select 助手，例如 starts_with() 和 ends_with():

https://www.rdocumentation.org/packages/tidyselect/versions/0.2.5/topics/select_helpers .

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