使用 dplyr 进行汇总并保持相同的变量名

我发现 data.table 和 dplyr 在尝试做同样的事情时有不同的结果。我想使用 dplyr 语法，但让它以 data.table 的方式进行计算。用例是我想将小计添加到表中。为此，我需要对每个变量进行一些聚合，但然后保留相同的变量名称（在转换后的版本中）。 Data.table 允许我对变量执行一些聚合并保持相同的名称。然后使用同一变量进行另一个聚合。它将继续使用未转换的版本。然而，Dplyr 将使用转换后的版本。

In the 总结文档说：

# Note that with data frames, newly created summaries immediately
# overwrite existing variables
mtcars %>%
  group_by(cyl) %>%
  summarise(disp = mean(disp), sd = sd(disp))

这基本上是我遇到的问题，但我想知道是否有一个好的解决方法。我发现的一件事是将转换后的变量命名为其他名称rename最后，但这对我来说看起来不太好。如果有一种很好的方法来进行小计，那么也很高兴知道。我浏览了这个网站，没有看到讨论的具体情况。任何帮助将不胜感激！

这里我做了一个简单的例子，一次使用 data.table 的结果，一次使用 dplyr 的结果。我想采用这个简单的表格并附加一个小计行，它是感兴趣的列（总计）的加权平均值。

library(data.table)
library(dplyr)

dt <- data.table(Group = LETTERS[1:5],
                 Count = c(1000, 1500, 1200, 2000, 5000),
                 Total = c(50, 300, 600, 400, 1000))
dt[, Count_Dist := Count/sum(Count)]
dt[, .(Count_Dist = sum(Count_Dist), Weighted_Total = sum(Count_Dist*Total))]

dt <- rbind(dt[, .(Group, Count_Dist, Total)],
      dt[, .(Group = "All", Count_Dist = sum(Count_Dist), Total = sum(Count_Dist*Total))])
setnames(dt, "Total", "Weighted_Avg_Total")

dt

df <- data.frame(Group = LETTERS[1:5],
                 Count = c(1000, 1500, 1200, 2000, 5000),
                 Total = c(50, 300, 600, 400, 1000))

df %>%
  mutate(Count_Dist = Count/sum(Count)) %>%
  summarize(Count_Dist = sum(Count_Dist),
            Weighted_Total = sum(Count_Dist*Total))

df %>% 
  mutate(Count_Dist = Count/sum(Count)) %>%
  select(Group, Count_Dist, Total) %>% 
  rbind(df %>%
          mutate(Count_Dist = Count/sum(Count)) %>%
          summarize(Group = "All",
                    Count_Dist = sum(Count_Dist),
                    Total = sum(Count_Dist*Total))) %>% 
  rename(Weighted_Avg_Total = Total)

再次感谢任何帮助！

一个可能的解决方案是跳过mutate步骤及使用transmute为了第一mutate/select-step并直接从原始变量计算所需的变量，而无需为第二个变量创建中间变量mutate-step:

df %>% 
  transmute(Group, Count_Dist = Count/sum(Count), Weighted_Avg_Total = Total) %>% 
  bind_rows(df %>%
              summarize(Group = "All",
                        Count_Dist = sum(Count/sum(Count)),
                        Weighted_Avg_Total = sum((Count/sum(Count))*Total)))

这使：

  Group Count_Dist Weighted_Avg_Total
1     A 0.09345794            50.0000
2     B 0.14018692           300.0000
3     C 0.11214953           600.0000
4     D 0.18691589           400.0000
5     E 0.46728972          1000.0000
6   All 1.00000000           656.0748

另一种可能的解决方案是改变新变量的计算顺序dplyr然后使用select使列顺序恢复为您最初想要的：

df %>% 
  mutate(Count_Dist = Count/sum(Count)) %>%
  select(Group, Count_Dist, Weighted_Avg_Total = Total) %>% 
  bind_rows(df %>%
              mutate(Count_Dist = Count/sum(Count)) %>%
              summarize(Group = "All",
                        Weighted_Avg_Total = sum(Count_Dist*Total),
                        Count_Dist = sum(Count_Dist)) %>% 
              select(Group, Count_Dist, Weighted_Avg_Total))

如果您想包括Count-column ，你也可以这样做（基于我下面的评论）：

df %>% 
  transmute(Group = Group, Count_Dist = Count/sum(Count), Weighted_Avg_Total = Total, Count) %>% 
  bind_rows(df %>%
              summarize(Group = "All",
                        Count_Dist = sum(Count/sum(Count)),
                        Weighted_Avg_Total = sum((Count/sum(Count))*Total),
                        Count = sum(Count)))