How do I subset values squished together (by rows) in a for loop and isolate values not squished together

I have an ordered dataframe that has a categorical variable (specifically, bird families) in one of the columns. Now, because they are ordered in a specific manner, with one row for each species, some bird families have all species squished together in the dataframe while some others are interrupted by species of other families. What I am trying to do is subset each chunk (same family) one by one as they appear, in a forloop, so that I can do some further processing before moving on to the next chunk. I cannot use unique() as this negates the fact that some families are interrupted, putting them all together in the subset. Here's an example subset of the dataset:

structure(list(jetzspp = c("Acanthisitta_chloris", "Xenicus_gilviventris", 
"Ampelioides_tschudii", "Pipreola_aureopectus", "Pipreola_chlorolepidota", 
"Xenopipo_holochlora", "Xenopipo_uniformis", "Tityra_cayana", 
"Tityra_inquisitor", "Tachuris_rubrigastra", "Conopias_parvus"
), iocorder = c("Passeriformes", "Passeriformes", "Passeriformes", 
"Passeriformes", "Passeriformes", "Passeriformes", "Passeriformes", 
"Passeriformes", "Passeriformes", "Passeriformes", "Passeriformes"
), bird_family = c("Acanthisittidae", "Acanthisittidae", "Cotingidae", 
"Cotingidae", "Cotingidae", "Pipridae", "Pipridae", "Cotingidae", 
"Cotingidae", "Tyrannidae", "Tyrannidae")), row.names = c(NA, 
-11L), class = c("tbl_df", "tbl", "data.frame"))

Here I want to subset Acanthisittidae and then Cotingidae (only the three squished together) and Pipridae and then Cotingidae (the next two squished together) and so on.

Further, I want to isloate the names of the bird families that have an interrupted appearance in the dataset.

I have not found a function to track transitions yet. Making each interrupted family chunk name unique and then looping through them can be possible (?) but its not efficient for a database 3k+ long with many interruptions as I will have to change them back.

Solution

This may help with part of your question. The consecutive_id() and mutate() functions from dplyr will give you a new column with each grouping in bird_family having a unique id. Then, you can use those values for your loop. If you provide more information about your final desired output, it may be possible to do this without using a loop. Either way, here's a solution to get you started.

Update based on comment from OP:

To return an alternating binary identifier, you can apply the modulo operator %% to the values created previously by the consecutive_id() function. I have created two columns for illustrative purposes. If you only require a single id column, declare "binary_id" as "id" and the values in id will be overwritten:

library(dplyr)

df <- structure(list(jetzspp = c("Acanthisitta_chloris", "Xenicus_gilviventris", 
"Ampelioides_tschudii", "Pipreola_aureopectus", "Pipreola_chlorolepidota", 
"Xenopipo_holochlora", "Xenopipo_uniformis", "Tityra_cayana", 
"Tityra_inquisitor", "Tachuris_rubrigastra", "Conopias_parvus"
), iocorder = c("Passeriformes", "Passeriformes", "Passeriformes", 
"Passeriformes", "Passeriformes", "Passeriformes", "Passeriformes", 
"Passeriformes", "Passeriformes", "Passeriformes", "Passeriformes"
), bird_family = c("Acanthisittidae", "Acanthisittidae", "Cotingidae", 
"Cotingidae", "Cotingidae", "Pipridae", "Pipridae", "Cotingidae", 
"Cotingidae", "Tyrannidae", "Tyrannidae")), row.names = c(NA, 
-11L), class = c("tbl_df", "tbl", "data.frame"))

# Add unique id and binary id to each 'run' of values
df <- df %>%  
  mutate(id = consecutive_id(bird_family),
         binary_id = ifelse(id %% 2 == 0, 1, 0)) %>%
  data.frame()

df
                   jetzspp      iocorder     bird_family id binary_id
1     Acanthisitta_chloris Passeriformes Acanthisittidae  1         0
2     Xenicus_gilviventris Passeriformes Acanthisittidae  1         0
3     Ampelioides_tschudii Passeriformes      Cotingidae  2         1
4     Pipreola_aureopectus Passeriformes      Cotingidae  2         1
5  Pipreola_chlorolepidota Passeriformes      Cotingidae  2         1
6      Xenopipo_holochlora Passeriformes        Pipridae  3         0
7       Xenopipo_uniformis Passeriformes        Pipridae  3         0
8            Tityra_cayana Passeriformes      Cotingidae  4         1
9        Tityra_inquisitor Passeriformes      Cotingidae  4         1
10    Tachuris_rubrigastra Passeriformes      Tyrannidae  5         0
11         Conopias_parvus Passeriformes      Tyrannidae  5         0

for(i in unique(df$id)) {
  
  print(subset(df, df$id == i))
  
}

               jetzspp      iocorder     bird_family id binary_id
1 Acanthisitta_chloris Passeriformes Acanthisittidae  1         0
2 Xenicus_gilviventris Passeriformes Acanthisittidae  1         0
                  jetzspp      iocorder bird_family id binary_id
3    Ampelioides_tschudii Passeriformes  Cotingidae  2         1
4    Pipreola_aureopectus Passeriformes  Cotingidae  2         1
5 Pipreola_chlorolepidota Passeriformes  Cotingidae  2         1
              jetzspp      iocorder bird_family id binary_id
6 Xenopipo_holochlora Passeriformes    Pipridae  3         0
7  Xenopipo_uniformis Passeriformes    Pipridae  3         0
            jetzspp      iocorder bird_family id binary_id
8     Tityra_cayana Passeriformes  Cotingidae  4         1
9 Tityra_inquisitor Passeriformes  Cotingidae  4         1
                jetzspp      iocorder bird_family id binary_id
10 Tachuris_rubrigastra Passeriformes  Tyrannidae  5         0
11      Conopias_parvus Passeriformes  Tyrannidae  5         0