How to create a consecutive group number

Question

I have a data frame (all_data) in which I have a list of sites (1... to n) and their scores e.g.

  site  score
     1    10
     1    11  
              


        

Answer 1

Another way to do it. That I think is easy to get even when you know little about R:

library(dplyr)
df <- data.frame('site' = c(1, 1, 1, 4, 4, 4, 8, 8, 8))
df <- mutate(df, 'number' = cumsum(site != lag(site, default=-1)))

Answer 2

You can turn site into a factor and then return the numeric or integer values of that factor:

dat <- data.frame(site = rep(c(1,4,8), each = 3), score = runif(9))
dat$number <- as.integer(factor(dat$site))
dat

  site     score number
1    1 0.5305773      1
2    1 0.9367732      1
3    1 0.1831554      1
4    4 0.4068128      2
5    4 0.3438962      2
6    4 0.8123883      2
7    8 0.9122846      3
8    8 0.2949260      3
9    8 0.6771526      3

Answer 3

In the new dplyr 1.0.0 we can use cur_group_id() which gives a unique numeric identifier to a group.

library(dplyr)
df %>% group_by(site) %>% mutate(number = cur_group_id())

#  site score number
#  <int> <int>  <int>
#1     1    10      1
#2     1    11      1
#3     1    12      1
#4     4    10      2
#5     4    11      2
#6     4    11      2
#7     8     9      3
#8     8     8      3
#9     8     7      3

data

df <- structure(list(site = c(1L, 1L, 1L, 4L, 4L, 4L, 8L, 8L, 8L), 
score = c(10L, 11L, 12L, 10L, 11L, 11L, 9L, 8L, 7L)), 
class = "data.frame", row.names = c(NA, -9L))

Answer 4

Using the data from @Jaap, a different dplyr possibility using dense_rank() could be:

dat %>%
 mutate(ID = dense_rank(site))

   site     score ID
1     1 0.1884490  1
2     1 0.1087422  1
3     1 0.7438149  1
4     8 0.1150771  3
5     8 0.9978203  3
6     8 0.7781222  3
7     4 0.4081830  2
8     4 0.2782333  2
9     4 0.9566959  2
10    8 0.2545320  3
11    8 0.1201062  3
12    8 0.5449901  3

Or a rleid()-like dplyr approach, with the data arranged first:

dat %>%
 arrange(site) %>%
 mutate(ID = with(rle(site), rep(seq_along(lengths), lengths)))

   site     score ID
1     1 0.1884490  1
2     1 0.1087422  1
3     1 0.7438149  1
4     4 0.4081830  2
5     4 0.2782333  2
6     4 0.9566959  2
7     8 0.1150771  3
8     8 0.9978203  3
9     8 0.7781222  3
10    8 0.2545320  3
11    8 0.1201062  3
12    8 0.5449901  3

Or using duplicated() and cumsum():

df %>%
 mutate(ID = cumsum(!duplicated(site)))

The same with base R:

df$ID <- with(rle(df$site), rep(seq_along(lengths), lengths))

Or:

df$ID <- cumsum(!duplicated(df$site))

Answer 5

Another solution using the data.table package.

Example with the more complete datset provided by Jaap:

setDT(dat)[, number := frank(site, ties.method = "dense")]
dat
    site     score number
 1:    1 0.3107920      1
 2:    1 0.3640102      1
 3:    1 0.1715318      1
 4:    8 0.7247535      3
 5:    8 0.1263025      3
 6:    8 0.4657868      3
 7:    4 0.6915818      2
 8:    4 0.3558270      2
 9:    4 0.3376173      2
10:    8 0.7934963      3
11:    8 0.9641918      3
12:    8 0.9832120      3

Answer 6

This should be fairly efficient and understandable:

Dat$sitenum <- match(Dat$site, unique(Dat$site))