I’m trying to import a .txt using read_tsv but when filtering columns I end up with a dataframe of 0 obs. Using base r read.table, the subsequent dataframe generates the intended outcome minus rows that match criteria in filter(). I change a column type to character after the read_tsv generated a warning however this did not change the outcome of 0 obs. What can I do with read_tsv to ensure that filter() will produce the intended dataframe that removes a small number of rows that fulfill the criteria?

data input here https://file.io/QUOyiQEktNMS

    spombe_protein_6plex_mq_output <- read_tsv("Galaxy116_MaxQuant_Protein_Groups_SP.txt") #attempt to read in using tidyverse
            
        Rows: 4310 Columns: 84                                                                                                                                             
        ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────
        Delimiter: "t"
        chr (21): Protein IDs, Majority protein IDs, Peptide counts (all), Peptide counts (razor+unique), Peptide counts (unique), P...
        dbl (62): Number of proteins, Peptides, Razor + unique peptides, Unique peptides, Peptides E1, Razor + unique peptides E1, U...
        lgl  (1): Reverse
        
        ℹ Use `spec()` to retrieve the full column specification for this data.
        ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
        Warning message:
        One or more parsing issues, call `problems()` on your data frame for details, e.g.:
          dat <- vroom(...)
          problems(dat) 
        
spombe_protein_6plex_mq_output2 <- spombe_protein_6plex_mq_output %>% 
       mutate(across(Reverse, as.character))
        
 spombe_test <- read.table("Galaxy116_MaxQuant_Protein_Groups_SP.txt", header = TRUE, sep = "t", stringsAsFactors = FALSE) #base utils
        
       spombe_protein_6plex_mq_output_filtered <- spombe_protein_6plex_mq_output2 %>% 
               dplyr::filter(Reverse != "+") %>%
               dplyr::filter(`Only identified by site` != "+") %>%
               dplyr::filter(`Potential contaminant` != "+")
        
        #returns 0 obs of 84 variables
        
        spombe_test2 <- spombe_test %>% 
                filter(Reverse != "+") %>%
                filter(`Only.identified.by.site` != "+") %>%
                filter(Potential.contaminant != "+") 
        
        #returns 4212 obs of 84 variables