替换列中匹配的值

我是 R 编程新手，我一直停留在下面的示例上。

基本上我有两个数据集：

数据集1：

ID       Category        
1        CatZZ         
2        CatVV         
3        CatAA  
4        CatQQ

数据集2：

ID  Category  
1   Cat600  
3   Cat611 

我试图用 dataset2 中的“类别”值覆盖 dataset1 中的“类别”值，其中两个数据集之间存在 ID 匹配。

所以结果看起来像这样：

数据集1：

ID  Category    
1   Cat600  
2   CatVV  
3   Cat611  
4   CatQQ  

In tidyverse你可以做：

df1 %>%
 left_join(df2, by = c("ID" = "ID")) %>% #Merging the two dfs on ID
 mutate(Category = if_else(!is.na(Category.y), Category.y, Category.x)) %>% #If there is a match, taking the value from df2, otherwise from df1
 select(ID, Category) #Deleting the redundant variables

  ID Category
1  1   Cat600
2  2    CatVV
3  3   Cat611
4  4    CatQQ

Or:

df1 %>%
 left_join(df2, by = c("ID" = "ID")) %>% #Merging the two dfs on ID
 gather(var, val, -ID) %>% #Transforming the data from wide to long format
 arrange(ID) %>% #Arranging by ID
 group_by(ID) %>% #Grouping by ID
 mutate(Category = if_else(!is.na(nth(val, 2)), nth(val, 2), first(val))) %>% #If non-NA, taking the value from df2, otherwise from df1
 spread(var, val) %>% #Returning the data to wide format
 select(ID, Category) #Removing the redundant variables 

     ID Category
  <int> <chr>   
1     1 Cat600  
2     2 CatVV   
3     3 Cat611  
4     4 CatQQ

样本数据：

df1 <- read.table(text = "ID       Category        
1        CatZZ         
2        CatVV         
3        CatAA  
4        CatQQ", header = TRUE, stringsAsFactors = FALSE)

df2 <- read.table(text = "ID  Category  
1   Cat600  
                  3   Cat611", header = TRUE, stringsAsFactors = FALSE)