如何在 R 中的值行上创建分类列条件？

Question

My dataframe is the following:

df <- data.frame (RR_Code  = c("848140", "848180", "848190", "848310", "848360", "848410", "848490", "850131", "850132", "850133"),
                  Model = c("X1", "FG", "FD", "XR", "RT", "FG", "CV", "GH", "ER", "RF"))
   RR_Code Model
1   848140    X1
2   848180    FG
3   848190    FD
4   848310    XR
5   848360    RT
6   848410    FG
7   848490    CV
8   850131    GH
9   850132    ER
10  850133    RF

Now I want to add a category column according to these 4 dataframes below. If RR_Code match codes in R1 value in new column must be R1 and so on

R1 <- c("848140", "848180", "848190")
R2 <- c("848310", "848360", "848410")
R3 <- c("848490")
R4 <- c("850131", "850132", "850133")

Expected outcome:

   RR_Code Model   Category
1   848140    X1      R1
2   848180    FG      R1
3   848190    FD      R1
4   848310    XR      R2
5   848360    RT      R2
6   848410    FG      R2
7   848490    CV      R3
8   850131    GH      R4
9   850132    ER      R4
10  850133    RF      R4

Answer 1

You could create a list recording the relation of matches.

R.lst <- list(
  R1 = c("848140", "848180", "848190"),
  R2 = c("848310", "848360", "848410"),
  R3 = c("848490"),
  R4 = c("850131", "850132", "850133")
)

Then assign this named list to the levels of Category :

df$Category <- factor(df$RR_Code)
levels(df$Category) <- R.lst

---

Another way is using fct_collapse() from forcats package to collapse factor levels into manually defined groups.

library(tidyverse)

df %>%
  mutate(Category = fct_collapse(RR_Code, !!!R.lst))

   RR_Code Model Category
1   848140    X1       R1
2   848180    FG       R1
3   848190    FD       R1
4   848310    XR       R2
5   848360    RT       R2
6   848410    FG       R2
7   848490    CV       R3
8   850131    GH       R4
9   850132    ER       R4
10  850133    RF       R4

Answer 2

You could also turn this into a join operation. Most of the work is just putting your R* vectors into a data.frame for the join.

library(dplyr)
tibble::lst(R1, R2, R3, R4) %>%
  stack() %>%
  rename(Categoyr=ind, RR_Code=values) %>% 
  right_join(df)
#    RR_Code Categoyr Model
# 1   848140       R1    X1
# 2   848180       R1    FG
# 3   848190       R1    FD
# 4   848310       R2    XR
# 5   848360       R2    RT
# 6   848410       R2    FG
# 7   848490       R3    CV
# 8   850131       R4    GH
# 9   850132       R4    ER
# 10  850133       R4    RF