Select unique values with 'select' function in 'dplyr' library

In dplyr 0.3 this can be easily achieved using the distinct() method.

Here is an example:

distinct_df = df %>% distinct(field1)

You can get a vector of the distinct values with:

distinct_vector = distinct_df$field1

You can also select a subset of columns at the same time as you perform the distinct() call, which can be cleaner to look at if you examine the data frame using head/tail/glimpse.:

distinct_df = df %>% distinct(field1) %>% select(field1) distinct_vector = distinct_df$field1

Just to add to the other answers, if you would prefer to return a vector rather than a dataframe, you have the following options:

dplyr >= 0.7.0

Use the pull verb:

mtcars %>% distinct(cyl) %>% pull()

dplyr < 0.7.0

Enclose the dplyr functions in a parentheses and combine it with $ syntax:

(mtcars %>% distinct(cyl))$cyl

The dplyr select function selects specific columns from a data frame. To return unique values in a particular column of data, you can use the group_by function. For example:

library(dplyr)

# Fake data
set.seed(5)
dat = data.frame(x=sample(1:10,100, replace=TRUE))

# Return the distinct values of x
dat %>%
  group_by(x) %>%
  summarise() 

    x
1   1
2   2
3   3
4   4
5   5
6   6
7   7
8   8
9   9
10 10

If you want to change the column name you can add the following:

dat %>%
  group_by(x) %>%
  summarise() %>%
  select(unique.x=x)

This both selects column x from among all the columns in the data frame that dplyr returns (and of course there's only one column in this case) and changes its name to unique.x.

You can also get the unique values directly in base R with unique(dat$x).

If you have multiple variables and want all unique combinations that appear in the data, you can generalize the above code as follows:

set.seed(5)
dat = data.frame(x=sample(1:10,100, replace=TRUE), 
                 y=sample(letters[1:5], 100, replace=TRUE))

dat %>% 
  group_by(x,y) %>%
  summarise() %>%
  select(unique.x=x, unique.y=y)