Make duplicate rows as replicates in R dataframe-CodePudding

I have a data frame with duplicated rows having one continuous variable column and 2-factor columns (0,1). The goal is to find the duplicated rows and identify them as replicates in a new column.

Here is the structure of the data frame

  cont.var fact1 fact2
1      1.0     1     0
2      1.0     0     1
3      1.5     1     0
4      1.5     1     0
5      1.5     0     1
6      1.5     0     1

Now let's say

If cont.var has value 1.0 in two rows but has different values for fact1 and fact2, so it will be assigned two different replicates.
If cont.var has value 1.5 and fact1/fact2 is also the same for successive rows, they will be given the same replicate identifier.

Expected Output

   cont.var fact1 fact2 rep
1       1.0     1     0   1
2       1.0     0     1   2
3       1.5     1     0   3
4       1.5     1     0   3
5       1.5     0     1   4
6       1.5     0     1   4

What I have tried

library(dplyr)

sample.df <- data.frame(
  cont.var = c(1,1,1.5,1.5,1.5,1.5,2,2,2,3),
  fact1 = c(1,0,1,1,0,0,1,1,0,1),
  fact2 = c(0,1,0,0,1,1,0,0,1,0)
)
  
sample.df %>% 
    group_by(cont.var, fact1, fact2) %>% 
    mutate(replicate = make.unique(as.character(cont.var), "_"))

Incorrect Output

I would expect that row-1 and row-2 will have different replicate counts.
I would expect that Replicate count for row-3 == row-4 and row-5 == row-6, but row-5 != row-3

   cont.var fact1 fact2 replicate
1       1.0     1     0         1
2       1.0     0     1         1
3       1.5     1     0       1.5
4       1.5     1     0     1.5_1
5       1.5     0     1       1.5
6       1.5     0     1     1.5_1

I couldn't find a straightforward solution to this; I would really appreciate any help.

Thanks in advance.

CodePudding user response：

You can use data.table::rleid:

library(dplyr)
df %>% 
  mutate(rleid = data.table::rleid(cont.var, fact1, fact2))

  cont.var fact1 fact2 rleid
1      1.0     1     0     1
2      1.0     0     1     2
3      1.5     1     0     3
4      1.5     1     0     3
5      1.5     0     1     4
6      1.5     0     1     4

If you have dplyr's dev. version, you can also use consecutive_id, the dplyr version of data.table::rleid:

#devtools::install_github("tidyverse/dplyr")
library(dplyr)
df %>% 
  mutate(rleid2 = consecutive_id(cont.var, fact1, fact2))

Finally, a base R option would be to match the rows by unique values:

df$rleid <- match(do.call(paste, df), do.call(paste, unique(df)))

CodePudding user response：

Another dplyr method, in case you're already grouped:

quux %>%
  group_by(cont.var, fact1, fact2) %>%
  mutate(rep = group_indices()) %>%
  ungroup()
# # A tibble: 6 x 4
#   cont.var fact1 fact2   rep
#      <dbl> <int> <int> <int>
# 1      1       1     0     2
# 2      1       0     1     1
# 3      1.5     1     0     4
# 4      1.5     1     0     4
# 5      1.5     0     1     3
# 6      1.5     0     1     3

While the actual values are not the same, the spirit of your request is retained.

CodePudding user response：

Here is another base R solution:

sample.df <- data.frame(
  cont.var = c(1,1,1.5,1.5,1.5,1.5,2,2,2,3),
  fact1 = c(1,0,1,1,0,0,1,1,0,1),
  fact2 = c(0,1,0,0,1,1,0,0,1,0)
)


sample.df$replicate <- cumsum(!duplicated(sample.df)) 
sample.df
#>    cont.var fact1 fact2 replicate
#> 1       1.0     1     0         1
#> 2       1.0     0     1         2
#> 3       1.5     1     0         3
#> 4       1.5     1     0         3
#> 5       1.5     0     1         4
#> 6       1.5     0     1         4
#> 7       2.0     1     0         5
#> 8       2.0     1     0         5
#> 9       2.0     0     1         6
#> 10      3.0     1     0         7

EDIT ensure dups are continuous:

sample.df <- sample.df[with(sample.df, order(fact2,fact1,cont.var)),]
sample.df$replicate <- cumsum(!duplicated(sample.df)) 
sample.df
#>    cont.var fact1 fact2 replicate
#> 1       1.0     1     0         1
#> 3       1.5     1     0         2
#> 4       1.5     1     0         2
#> 7       2.0     1     0         3
#> 8       2.0     1     0         3
#> 10      3.0     1     0         4
#> 2       1.0     0     1         5
#> 5       1.5     0     1         6
#> 6       1.5     0     1         6
#> 9       2.0     0     1         7