Giving IDs to groups in R

Question

I have in R data frame that is divided to groups, like this:

Row	Group
1	A
2	B
3	A
4	D
5	C
6	B
7	C
8	C
9	A
10	B

I would like to add a uniaque numeric ID to each group, so finally I would have something like this:

Row	Group	ID
1	A	1
2	B	2
3	A	1
4	D	4
5	C	3
6	B	2
7	C	3
8	C	3
9	A	1
10	B	2

How could I achieve this?

Thank you very much.

Answer 1

We can use match on the sorted unique values of 'Group' on the 'Group' to get the position index

df1$ID <- with(df1, match(Group, sort(unique(Group))))

data

df1 <- structure(list(Row = 1:10, Group = c("A", "B", "A", "D", "C", 
"B", "C", "C", "A", "B")), class = "data.frame", row.names = c(NA, 
-10L))

Answer 2

Update

group_indices() was deprecated in dplyr 1.0.0.

Please use cur_group_id() instead.

df1 <- df %>% 
  group_by(Group) %>% 
  mutate(ID = cur_group_id())

First answer:

You can use group_indices

library(dplyr)

df1 <- df %>% 
  group_by(Group) %>% 
  mutate(ID = group_indices())

data

df <- tribble(
~Row,   ~Group,
1, "A", 
2, "B", 
3, "A", 
4, "D", 
5, "C", 
6, "B", 
7, "C", 
8, "C", 
9, "A", 
10,"B")

     Row Group    ID
   <int> <chr> <int>
 1     1 A         1
 2     2 B         2
 3     3 A         1
 4     4 D         4
 5     5 C         3
 6     6 B         2
 7     7 C         3
 8     8 C         3
 9     9 A         1
10    10 B         2

Answer 3

Here is a simple way.

df1$ID <- as.integer(factor(df1$Group))

---

There are 3 solutions posted, mine, TarJae's and akrun's, timed with increasing data sizes. akrun's is the fastest.

library(microbenchmark)
library(dplyr)
library(ggplot2)

funtest <- function(x, n){
  out <- lapply(seq_len(n), function(i){
    for(j in seq_len(i)) x <- rbind(x, x)
    cat("nrow(x):", nrow(x), "\n")
    mb <- microbenchmark(
      match = with(x, match(Group, sort(unique(Group)))),
      dplyr = x %>% group_by(Group) %>% mutate(ID = cur_group_id()),
      intfac = as.integer(factor(x$Group))
    )
    mb$n <- i
    mb
  })
  out <- do.call(rbind, out)
  aggregate(time ~ ., out, median)
}

df1 %>%
  funtest(10) %>%
  ggplot(aes(n, time, colour = expr)) +
  geom_line() +
  geom_point() +
  scale_x_continuous(breaks = 1:10, labels = 1:10) +
  scale_y_continuous(trans = "log10") +
  theme_bw()