| distinct {dplyr} | R Documentation |
Retain only unique/distinct rows from an input tbl. This is similar
to unique.data.frame(), but considerably faster.
distinct(.data, ..., .keep_all = FALSE)
.data |
a tbl |
... |
Optional variables to use when determining uniqueness. If there are multiple rows for a given combination of inputs, only the first row will be preserved. If omitted, will use all variables. |
.keep_all |
If |
Comparing list columns is not fully supported. Elements in list columns are compared by reference. A warning will be given when trying to include list columns in the computation. This behavior is kept for compatibility reasons and may change in a future version. See examples.
df <- tibble( x = sample(10, 100, rep = TRUE), y = sample(10, 100, rep = TRUE) ) nrow(df) nrow(distinct(df)) nrow(distinct(df, x, y)) distinct(df, x) distinct(df, y) # Can choose to keep all other variables as well distinct(df, x, .keep_all = TRUE) distinct(df, y, .keep_all = TRUE) # You can also use distinct on computed variables distinct(df, diff = abs(x - y)) # The same behaviour applies for grouped data frames # except that the grouping variables are always included df <- tibble( g = c(1, 1, 2, 2), x = c(1, 1, 2, 1) ) %>% group_by(g) df %>% distinct() df %>% distinct(x) # Values in list columns are compared by reference, this can lead to # surprising results tibble(a = as.list(c(1, 1, 2))) %>% glimpse() %>% distinct() tibble(a = as.list(1:2)[c(1, 1, 2)]) %>% glimpse() %>% distinct()