R：汇总数据，但需要拆分一些汇总的观察结果

Question

我有两个需要合并的数据框。下面是每个组成的示例。这些是学区：第一个是收入，第二个是成绩。

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School district     revenue
Richland 1          8702
Richland 2          3749
Richland Board       892
Charleston          6324
Greenville          1245
Greenville Board     371

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School district     grade
Richland 1          A
Richland 2          A+
Charleston          B
Greenville          D

目标是合并这两个数据框，并将最终结果汇总到与第二（等级）数据框相同的级别。我不得不做一个数据字典来合并它们，因为每个名称都不同（尽管我在这里删除了它以简化），而且还要处理聚合。我打算按以下方式设置我的字典：

School_dist1    School_dist2
Richland 1      Richland 1
Richland 2      Richland 2
?????           Richland Board
Charleston      Charleston
Greenville      Greenville
Greenville      Greenville Board

然后我会简单地在 school_dist1 列上进行聚合。正如您所看到的，问题在于，虽然格林维尔委员会可以简单地聚合到格林维尔，但里奇兰委员会需要在两个里奇兰委员会之间（平均分配）。

我尝试使用我能想到的每一个可能的关键字来搜索解决方案，但由于问题的怪异性质，我找不到任何东西。它的要点是，我需要聚合数据，但需要拆分一些观察结果，然后在它们被聚合到的其他观察值之间共享。

有没有办法做到这一点？我说得有道理吗？我完全被这个难住了。

Answer 1

这是回家的路很长，但它会带你到那里......

# your data, dont use spaces in column names
df1 <- read.table(text = "School_district     revenue
Richland_1          8702
                 Richland_2          3749
                 Richland_Board       892
                 Charleston          6324
                 Greenville          1245
                 Greenville_Board     371", header = T)

df2 <- read.table(text = "School_district     grade
Richland_1          A
Richland_2          A+
Charleston          B
Greenville          D", header = T)

library(tidyverse)
# split df1 with boards and non-boards into separate dfs
boards <- dplyr::filter(df1, grepl("Board", df1$School_district)) %>%
    dplyr::mutate(School_district = gsub("_Board", "", School_district))
df1 <-  dplyr::filter(df1, !grepl("Board", df1$School_district))

# look up how many times a certain school district appears in df1
boards$num_splits <- map_int(boards$School_district,
                             ~ grep(., df1$School_district) %>% length)
# add new column for revenue divided by number of appearances
boards <- transmute(boards,
                    match_name = School_district,
                    add_value = revenue / num_splits)

# if I knew how to use fuzzy_join you could probably drop this part
df1$match_name <- gsub("_.*", "", df1$School_district)

full_join(df1, boards) %>%
    rowwise() %>%
    mutate(new_revenue = sum(revenue, add_value, na.rm = T)) %>%
    select(-match_name) %>%
    full_join(df2)

# A tibble: 4 × 5
School_district revenue add_value new_revenue  grade
<chr>   <int>     <dbl>       <dbl> <fctr>
1      Richland_1    8702       446        9148      A
2      Richland_2    3749       446        4195     A+
3      Charleston    6324        NA        6324      B
4      Greenville    1245       371        1616      D