Assign value to variable based on values on multiple other columns (alternative to ifelse)

Question

I have a data frame describing a large number of people. I want to assign each person to a group, based on several variables. For example, let's say I have the variable "state" with 5 states, the variable "age group" with 4 groups and the variable "income" with 5 groups. I will have 5x4x5 = 100 groups, that I want to name with numbers going from 1 to 100. I have always done this in the past using a combination of ifelse statements, but now as I have 100 possible outcomes I am wondering if there is a faster way than specifying each combination by hand.

Here's a MWE with the expected outcome:

mydata <- as.data.frame(cbind(c("FR","UK","UK","IT","DE","ES","FR","DE","IT","UK"),
 c("20","80","20","40","60","20","60","80","40","60"),c(1,4,2,3,1,5,5,3,4,2)))
colnames(mydata) <- c("Country","Age","Income")

group_grid <- transform(expand.grid(state = c("IT","FR","UK","ES","DE"), 
       age = c("20","40","60","80"), income = 1:5), val = 1:100)

desired_result <- as.data.frame(cbind(c("FR","UK","UK","IT","DE","ES","FR","DE","IT","UK"),

                                      c("20","80","20","40","60","20","60","80","40","60"),
                                      c(1,4,2,3,1,5,5,3,4,2),
                                      c(2,78,23,46,15,84,92,60,66,33)))

colnames(desired_result) <- c("Country","Age","Income","Group_code")

Answer 1

mydata$Group_code <- with(mydata, as.integer(interaction(Country, Age, Income)))应该这样做。

Answer 2

Here is left_join option using dplyr

library(dplyr)
grpD <- group_grid %>% 
            mutate_if(is.factor, as.character) %>% #change to character class as joining
            mutate(income = as.character(income))#with same class columns are reqd.
mydata %>%
      mutate_if(is.factor, as.character) %>%  #change class here too
      left_join(., grpD, by= c("Country" = "state", "Age" = "age", "Income" = "income"))
#    Country Age Income val
#1       FR  20      1   2
#2       UK  80      4  78
#3       UK  20      2  23
#4       IT  40      3  46
#5       DE  60      1  15
#6       ES  20      5  84
#7       FR  60      5  92
#8       DE  80      3  60
#9       IT  40      4  66
#10      UK  60      2  33