I want to remove duplicate rows from a dataframe, for specific columns only. That can be obtained with distinct:

data <- tibble(a = c(1, 1, 2, 2), b = c(3, 3, 3, 4), z = c(5,4,5,5)) filtered_data <- data %>% distinct(a, b, .keep_all = T) dim(filtered_data) # [1] 3 3 

This is (almost) what I need. Yet, my problem is that the columnnames I need to use with distinct will change. So I have a string gen that contains the names of the columns I want to use for with the distinct function. They need to get unquoted to be usefull in the pipe. I found suggestions to use as.name() or eval(parse()). This however gives me a different result:

gen <- c("a", "b") filtered_data <- data %>% distinct(eval(parse(text = gen)), .keep_all = T) dim(filtered_data) # [1] 2 4 

The eval seems to do something funny with the amount of times the data is filtered. (and, adds an extra column. I could live with that, though...) So, how to obtain a similar result, as if I had used a,b, but by using a variable instead?

additional information I actually obtain gen by reading the columnnames of a dataframe: gen <- colnames(data)[1:2]. The solution suggested by @gymbrane would be perfect, if I had a way to transform the gen to c(a, b). The whole point is to avoid hardcoding the columnames. I tried things like gen <- noquotes(gen), which does not give an error in the rm_dup_rows function suggested below, but it does give a different result, giving the same sort of repeated filtering as I started with...

fixed I think I got it working. It might be unelegant, and I'm not sure if every step is necessary for the result, but it seems to work by combining the function provided by @gymbrane below with ensym and quos in a forloop while adding to a list in GlobalEnv (edit: GlobalEnv isn't necessary):

unquote_string <- function(string) { out <- list() i <- 1 for (s in string) { t <- ensym(s) out[i] <-dplyr::quos(!!t) i <- i+1 } return(out) } gen_quo <- unquote_string(gen) filtered_data <- rm_dup_rows(data, gen_quo) dim(filtered_data) # [1] 3 3 

1 Answer

How about creating a function and using quosures . Perhaps something like this is what you are looking for...

rm_dup_rows <- function(data, ...){ vars = dplyr::quos(...) data %>% distinct(!!! vars, .keep_all = T) } 

I believe this returns what you are asking for

rm_dup_rows(data = data, a, b) # A tibble: 3 x 3 a b z <dbl> <dbl> <dbl> 1 3 5 2 3 5 2 4 5 rm_dup_rows(data, b, z) # A tibble: 3 x 3 a b z <dbl> <dbl> <dbl> 1 3 5 1 3 4 2 4 5 

Additional

You could modify rm_dup_rows just slightly and construct and your vector with quos. Something like this...

rm_dup_rows <- function(data, vars){ data %>% distinct(!!! vars, .keep_all = T) } # quos your column name vector gen <- quos(a,z) rm_dup_rows(data, gen) # A tibble: 3 x 3 a b z <dbl> <dbl> <dbl> 1 3 5 1 3 4 2 3 5