How to filter a dataframe by rows without losing the index (or row number)?

Question

I have a small dataframe (dt) containing binary labels from separate catboost runs:

structure(list(old.cat.lab = c(1, 1, 0, 0, 0, 1, 0, 0, 0, 1), 
new.cat.lab = c(1, 1, 0, 0, 1, 1, 0, 1, 0, 1)), row.names = c(NA, 10L), class = "data.frame")

I want to filter the rows where dt$new.cat.lab == 1 using (from the dplyr package):

dt.match <- dt %>% filter(dt$new.cat.lab ==1, .preserve = T)

The problem is that the filter function assigns a new row number. I would like to preserve the row numbers (index) in the new variable. The .preserve=T command within dplyr's filter function doesn't seem to do that.

Answer 1

The tidyverse, doesn't preserve the row names, we can create a new column of row names and then apply the filter

library(dplyr)
library(tibble)
dt %>%
   rownames_to_column('rn') %>%
   filter(new.cat.lab ==1)%>%
   column_to_rownames('rn')
#   old.cat.lab new.cat.lab
#1            1           1
#2            1           1
#5            0           1
#6            1           1
#8            0           1
#10           1           1

According to ?dplyr::filter, the .preserve is for grouping structure

.preserve - Relevant when the .data input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.

---

In base R, this can be done with subset

subset(dt, new.cat.lab == 1)

Or use as.logical

subset(dt, as.logical(new.cat.lab))

Answer 2

library(dplyr)

iris %>% 
  mutate(index = rownames(.)) %>% 
  relocate(index, .before = "Sepal.Length") %>% 
  subset(., Sepal.Length == max(Sepal.Length))

Created an index column using the rownames/rownumbers for tracking purpose. then filter. Below is another variant

iris %>% 
  mutate(index = rownames(.)) %>% 
  relocate(index, .before = "Sepal.Length") %>% 
  filter(Sepal.Length == max(Sepal.Length))