Tidyverse：过滤分组数据框中的n个最大组

Question

我想根据计数过滤n个最大的组，然后对过滤后的数据帧进行一些计算

这是一些数据

Brand <- c("A","B","C","A","A","B","A","A","B","C")
Category <- c(1,2,1,1,2,1,2,1,2,1)
Clicks <- c(10,11,12,13,14,15,14,13,12,11)
df <- data.frame(Brand,Category,Clicks)

|Brand | Category| Clicks|
|:-----|--------:|------:|
|A     |        1|     10|
|B     |        2|     11|
|C     |        1|     12|
|A     |        1|     13|
|A     |        2|     14|
|B     |        1|     15|
|A     |        2|     14|
|A     |        1|     13|
|B     |        2|     12|
|C     |        1|     11|

这是我的预期输出。我想按计数过滤出两个最大的品牌，然后找到每个品牌/类别组合中的平均点击次数

|Brand | Category| mean_clicks|
|:-----|--------:|-----------:|
|A     |        1|        12.0|
|A     |        2|        14.0|
|B     |        1|        15.0|
|B     |        2|        11.5|

我以为这样的代码可以实现（但不能）

df %>%
  group_by(Brand, Category) %>%
  top_n(2, Brand) %>% # Largest 2 brands by count
  summarise(mean_clicks = mean(Clicks))

编辑：理想的答案应该能够在数据库表以及本地表上使用

Answer 1

使用dplyr过滤数据帧的另一join解决方案：

library(dplyr)

df %>%
  group_by(Brand) %>%
  summarise(n = n()) %>%
  top_n(2) %>% # select top 2
  left_join(df, by = "Brand") %>% # filters out top 2 Brands
  group_by(Brand, Category) %>%
  summarise(mean_clicks = mean(Clicks))

# # A tibble: 4 x 3
# # Groups:   Brand [?]
#   Brand Category mean_clicks
#   <fct>    <dbl>       <dbl>
# 1 A            1        12  
# 2 A            2        14  
# 3 B            1        15  
# 4 B            2        11.5

Answer 2

另一种dplyr解决方案：

df %>%
  group_by(Brand) %>%
  mutate(n = n()) %>%
  ungroup() %>%
  mutate(rank = dense_rank(desc(n))) %>%
  filter(rank == 1 | rank == 2) %>%
  group_by(Brand, Category) %>%
  summarise(mean_clicks = mean(Clicks))

# A tibble: 4 x 3
# Groups:   Brand [?]
  Brand Category mean_clicks
  <fct>    <dbl>       <dbl>
1 A           1.        12.0
2 A           2.        14.0
3 B           1.        15.0
4 B           2.        11.5

或简化版本（基于@camille的建议）：

df %>%
  group_by(Brand) %>%
  mutate(n = n()) %>%
  ungroup() %>%
  filter(dense_rank(desc(n)) < 3) %>%
  group_by(Brand, Category) %>%
  summarise(mean_clicks = mean(Clicks))

Answer 3

编辑

基于更新后的问题，我们可以先添加一个计数列，仅过滤排名靠前的n组计数，然后过滤group_by Brand和Category来找到{{1 }}。

mean

原始答案

我们可以df %>% add_count(Brand, sort = TRUE) %>% filter(n %in% head(unique(n), 2)) %>% group_by(Brand, Category) %>% summarise(mean_clicks = mean(Clicks)) # Brand Category mean_clicks # <fct> <dbl> <dbl> #1 A 1 12 #2 A 2 14 #3 B 1 15 #4 B 2 11.5 group_by并按组进行所有计算，然后按Brand过滤顶部组

top_n

Answer 4

data.table的想法是获取按Brands分组的计数并过滤前两个（按降序排列）。然后，我们将其与原始数据框合并，并找到按(Brand, Category)

分组的均值

library(data.table)

#Convert to data.table
dt1 <- setDT(df)

dt1[dt1[, .(cnt = .N), by = Brand][
             order(cnt, decreasing = TRUE), .SD[1:2]][,cnt := NULL], 
                   on = 'Brand'][, .(means = mean(Clicks)), by = .(Brand, Category)][]

给出，

   Brand Category means
1:     A        1  12.0
2:     A        2  14.0
3:     B        2  11.5
4:     B        1  15.0

Answer 5

如何使用table从基数R开始使用这种方法-

df %>%
  filter(Brand %in% names(tail(sort(table(Brand)), 2))) %>%
  group_by(Brand, Category) %>%
  summarise(mean_clicks = mean(Clicks))

# A tibble: 4 x 3
# Groups:   Brand [?]
  Brand Category mean_clicks
  <chr>    <dbl>       <dbl>
1 A         1.00        12.0
2 A         2.00        14.0
3 B         1.00        15.0
4 B         2.00        11.5

Answer 6

与上面略有不同。只是因为我不喜欢对大型数据集使用join。有些人可能不喜欢我制作并删除一个小数据框，对不起：（

df %>% count(Brand) %>% top_n(2,n) -> Top2
df %>% group_by(Brand, Category) %>% 
filter(Brand %in% Top2$Brand) %>% 
summarise(mean_clicks = mean(Clicks))
remove(Top2)