按名称分组并将 r 中的列数相加

Question

我有一个包含 405 个观察值和 39 个变量的数据集。但只有两列对进一步分析很重要。

我想将具有相似名称的第一行组合在一起，然后将第二列中的数字相加。

可重现的数据集如下所示：

df1 <- data.frame(name=c("Google Ads", "Google Doubleclick","Facebook Login",
"Facebook Ads","Twitter MoPub","Flurry","Amazon advertisment","Microsoft ","Ad4screen","imobi"),
value=c(10,20,30,40,50,60,70,80,90,100),unimportant=c(1,2,3,4,5,6,7,8,9,10))

结果应该在一个新的 data.frame 中，如下所示：

 df2 <- data.frame (name=c("Google","Facebook","Twitter","Flurry","Amazon","Microsoft","Others"),
value=c(30,70,50,60,70,80,190))

Answer 1

tidyverse 的做法。

• 首先将所有有效名称存储在一个向量中，比如valid_names
• 此后在df1 中创建一个新列all_names 由-
  • 首先使用str_split 在空间' ' 处拆分所有字符串
  • 之后使用purrr::map_chr() 检查是否有任何拆分字符串与您的valid_names 匹配，如果是，则仅检索该字符串，否则获取others
• 此后group_by 在此字段上。 （我先省略了一步mutate，然后group_by，直接在group_by语句中创建了新字段，有效）
• 现在根据需要总结您的 important 值。

valid_names =c("Google","Facebook","Twitter","Flurry","Amazon","Microsoft")

valid_names
#> [1] "Google"    "Facebook"  "Twitter"   "Flurry"    "Amazon"    "Microsoft"

df1 <- data.frame(name=c("Google Ads", "Google Doubleclick","Facebook Login",
                         "Facebook Ads","Twitter MoPub","Flurry","Amazon advertisment","Microsoft ","Ad4screen","imobi"),
                  value=c(10,20,30,40,50,60,70,80,90,100),unimportant=c(1,2,3,4,5,6,7,8,9,10))
df1
#>                   name value unimportant
#> 1           Google Ads    10           1
#> 2   Google Doubleclick    20           2
#> 3       Facebook Login    30           3
#> 4         Facebook Ads    40           4
#> 5        Twitter MoPub    50           5
#> 6               Flurry    60           6
#> 7  Amazon advertisment    70           7
#> 8           Microsoft     80           8
#> 9            Ad4screen    90           9
#> 10               imobi   100          10
library(tidyverse)

df1 %>% group_by(all_names = str_split(name, ' '),
               all_names = map_chr(all_names, ~ ifelse(any(.x %in% valid_names),.x[.x %in% valid_names], 'others'))) %>%
  summarise(value = sum(value), .groups = 'drop')
#> # A tibble: 7 x 2
#>   all_names value
#>   <chr>     <dbl>
#> 1 Amazon       70
#> 2 Facebook     70
#> 3 Flurry       60
#> 4 Google       30
#> 5 Microsoft    80
#> 6 others      190
#> 7 Twitter      50

^{由reprex package (v2.0.0) 于 2021-06-22 创建}

Answer 2

这适用于使用adist 函数和partial=TRUE 来查看部分字符串匹配的示例数据。它需要定义已知组，而不是试图找到它们。我认为这项工作是值得的，因为一旦知道输出，它就会大大简化问题

df1 <- data.frame(name=c("Google Ads", "Google Doubleclick","Facebook Login",
                         "Facbook Ads","Twitter MoPub","Flurry","Amazon advertisment","Microsoft ","Ad4screen","imobi"),
                  value=c(10,20,30,40,50,60,70,80,90,100),unimportant=c(1,2,3,4,5,6,7,8,9,10))

# types we want to map. known is the groupings
types <- unique(df1$name)
known <- c("Google","Facebook","Twitter","Flurry","Amazon","Microsoft")

# use distrance measures, and look for matches on partial strings eg
# ignore the Doubleclick part when matching on Google
distance <- adist(known, types, partial=TRUE)

# cap controls leniancy in matching e.g. Facbook and Facebook have a dist of 1
# whilst Facebook and Facebook is a perfect match with score of 0
# Raise to be more leniant
cap <- 1
# loop through the types
map_all <- sapply(seq_along(types), function(i){
  # find minimum value, check if its below the cap. If so, assign to the closest
  # group, else assign to others
  v <- min(distance[,i])
  if(v <= cap){
    map_i <- known[which.min(distance[,i])]
  }else{
    map_i <- "Others"
  }
  map_i
})

# now merge in to df1, then sum out using your preferred method
df_map <- data.frame(name=types, group=map_all)
df_merged <- merge(df1, df_map, by="name")
df2 <- aggregate(value ~ group, sum, data=df_merged)
df2
      group value
1    Amazon    70
2  Facebook    70
3    Flurry    60
4    Google    30
5 Microsoft    80
6    Others   190
7   Twitter    50