Here is a solution using dplyr >= 0.5.

library(dplyr)
set.seed(123)
df <- data.frame(
  x = sample(0:1, 10, replace = T),
  y = sample(0:1, 10, replace = T),
  z = 1:10
)

> df %>% distinct(x, y, .keep_all = TRUE)
    x y z
  1 0 1 1
  2 1 0 2
  3 1 1 4

Note: dplyr now contains the distinct function for this purpose.

Original answer below:

library(dplyr)
set.seed(123)
df <- data.frame(
  x = sample(0:1, 10, replace = T),
  y = sample(0:1, 10, replace = T),
  z = 1:10
)

One approach would be to group, and then only keep the first row:

df %>% group_by(x, y) %>% filter(row_number(z) == 1)

## Source: local data frame [3 x 3]
## Groups: x, y
## 
##   x y z
## 1 0 1 1
## 2 1 0 2
## 3 1 1 4

(In dplyr 0.2 you won’t need the dummy z variable and will just be
able to write row_number() == 1)

I’ve also been thinking about adding a slice() function that would
work like:

df %>% group_by(x, y) %>% slice(from = 1, to = 1)

Or maybe a variation of unique() that would let you select which
variables to use:

df %>% unique(x, y)

For completeness’ sake, the following also works:

df %>% group_by(x) %>% filter (! duplicated(y))

However, I prefer the solution using distinct, and I suspect it’s faster, too.

Most of the time, the best solution is using distinct() from dplyr, as has already been suggested.

However, here’s another approach that uses the slice() function from dplyr.

# Generate fake data for the example
  library(dplyr)
  set.seed(123)
  df <- data.frame(
    x = sample(0:1, 10, replace = T),
    y = sample(0:1, 10, replace = T),
    z = 1:10
  )

# In each group of rows formed by combinations of x and y
# retain only the first row

    df %>%
      group_by(x, y) %>%
      slice(1)

Difference from using the distinct() function

The advantage of this solution is that it makes it explicit which rows are retained from the original dataframe, and it can pair nicely with the arrange() function.

Let’s say you had customer sales data and you wanted to retain one record per customer, and you want that record to be the one from their latest purchase. Then you could write:

customer_purchase_data %>%
   arrange(desc(Purchase_Date)) %>%
   group_by(Customer_ID) %>%
   slice(1)

When selecting columns in R for a reduced data-set you can often end up with duplicates.

These two lines give the same result. Each outputs a unique data-set with two selected columns only:

distinct(mtcars, cyl, hp);

summarise(group_by(mtcars, cyl, hp));

If you want to find the rows that are duplicated you can use find_duplicates from hablar:

library(dplyr)
library(hablar)

df <- tibble(a = c(1, 2, 2, 4),
             b = c(5, 2, 2, 8))

df %>% find_duplicates()