组合两个具有相同数据但更喜欢一个输入的数据框

Question

我有两个数据帧或多或少包含两列相同的数据，'User ID'（例如37f879fb-9667-4de2-9cc9-b48918fcaa1f）和'Frequent User'（可以是'Frequent' 或 'Infrequent').

我正在尝试将这两个数据框合并在一起，但是如果一个数据框有 'Frequent' 用于频繁用户，另一个是 'Infrequent'，我希望它更喜欢并保留或替换为频繁用户用户每次。我不确定如何实现这个

df1

structure(list(user_id = c("000f2b1e-dde2-4227-9122-d197c674dea8", 
"001abd72-53f1-436a-a26f-e3187543afb6", "001c1e12-a8f7-4459-9b23-6b8bdc5d2625", 
"002d8272-8ee2-4e1a-a523-5ba0813c20f9", "0037abe8-7623-4ac7-9fbb-7398f505c1f6", 
"003911c6-c013-43b7-9996-e771dbe3ac83"), frequent_user = c("Frequent", 
"Infrequent", "Infrequent", "Infrequent", "Infrequent", "Infrequent"
)), row.names = c(NA, 6L), class = "data.frame")

df2

structure(list(user_id = c("000f2b1e-dde2-4227-9122-d197c674dea8", 
"001abd72-53f1-436a-a26f-e3187543afb6", "001c1e12-a8f7-4459-9b23-6b8bdc5d2625", 
"002d8272-8ee2-4e1a-a523-5ba0813c20f9", "0037abe8-7623-4ac7-9fbb-7398f505c1f6", 
"003911c6-c013-43b7-9996-e771dbe3ac83"), frequent_user = c("Infrequent", 
"Infrequent", "Infrequent", "Infrequent", "Infrequent", "Infrequent"
)), row.names = c(NA, 6L), class = "data.frame")

Answer 1

试试下面的代码

transform(
  merge(df1, df2, by = "user_id", all = TRUE),
  frequent_user = c("Infrequent", "Frequent")[1 + (rowSums(cbind(frequent_user.x, frequent_user.y) == "Frequent") > 0)]
)[c("user_id", "frequent_user")]

这给出了

                               user_id frequent_user
1 000f2b1e-dde2-4227-9122-d197c674dea8      Frequent
2 001abd72-53f1-436a-a26f-e3187543afb6    Infrequent
3 001c1e12-a8f7-4459-9b23-6b8bdc5d2625    Infrequent
4 002d8272-8ee2-4e1a-a523-5ba0813c20f9    Infrequent
5 0037abe8-7623-4ac7-9fbb-7398f505c1f6    Infrequent
6 003911c6-c013-43b7-9996-e771dbe3ac83    Infrequent

Answer 2

这是 tidyverse 的解决方案 left_join 和 transmute,

library(tidyverse)

left_join(
        df1, df2, by = "user_id"
) %>% group_by(user_id) %>% 
        transmute(
                frequent_user = case_when(
                        frequent_user.x == frequent_user.y ~ frequent_user.y,
                        TRUE ~ "Frequent"
                )
        )

给出以下输出，

# A tibble: 6 x 2
# Groups:   user_id [6]
  user_id                              frequent_user
  <chr>                                <chr>        
1 000f2b1e-dde2-4227-9122-d197c674dea8 Frequent     
2 001abd72-53f1-436a-a26f-e3187543afb6 Infrequent   
3 001c1e12-a8f7-4459-9b23-6b8bdc5d2625 Infrequent   
4 002d8272-8ee2-4e1a-a523-5ba0813c20f9 Infrequent   
5 0037abe8-7623-4ac7-9fbb-7398f505c1f6 Infrequent   
6 003911c6-c013-43b7-9996-e771dbe3ac83 Infrequent

Answer 3

这是一个没有 join 函数的 dplyr 解决方案：

library(dplyr)

df1 %>% 
  bind_rows(df2) %>% 
  group_by(user_id) %>% 
  arrange(user_id, frequent_user) %>% 
  slice(1) %>%
  ungroup()

returns

# A tibble: 6 x 2
  user_id                              frequent_user
  <chr>                                <chr>        
1 000f2b1e-dde2-4227-9122-d197c674dea8 Frequent     
2 001abd72-53f1-436a-a26f-e3187543afb6 Infrequent   
3 001c1e12-a8f7-4459-9b23-6b8bdc5d2625 Infrequent   
4 002d8272-8ee2-4e1a-a523-5ba0813c20f9 Infrequent   
5 0037abe8-7623-4ac7-9fbb-7398f505c1f6 Infrequent   
6 003911c6-c013-43b7-9996-e771dbe3ac83 Infrequent

组合两个具有相同数据但更喜欢一个输入的数据框

Combine two dataframes that have the same data but prefer one enter over another

merge

r

dataframe