How to select unique point

Question

I am a novice R programmer. I have a following series of points.

df <- data.frame(x = c(1 , 2, 3, 4), y = c(6 , 3, 7, 5))
df <- df %>% mutate(k = 1) 
df <- df %>% full_join(df, by = 'k')
df <- subset(df, select = c('x.x', 'y.x', 'x.y', 'y.y'))
df

Is there way to select for "unique" points? (the order of the points do not matter)

EDIT:

x.x y.x x.y y.y
1   6   2   3
2   3   3   7
.
.
.

(I changed the 2 to 7 to clarify the problem)

Answer 1

With data.table (and working from the OP's initial df):

library(data.table)
setDT(df)

df[, r := .I ]
df[df, on=.(r > r), nomatch=0]


   x y r i.x i.y
1: 2 3 1   1   6
2: 3 2 1   1   6
3: 4 5 1   1   6
4: 3 2 2   2   3
5: 4 5 2   2   3
6: 4 5 3   3   2

This is a "non-equi join" on row numbers. In x[i, on=.(r > r)] the left-hand r refers to the row in x and the right-hand one to a row of i. The columns named like i.* are taken from i.

Data.table joins, which are of the form x[i], use i to look up rows of x. The nomatch=0 option drops rows of i that find no matches.

Answer 2

In the tidyverse, you can save a bit of work by doing the self-join with tidyr::crossing. If you add row indices pre-join, reducing is a simple filter call:

library(tidyverse)

df %>% mutate(i = row_number()) %>%    # add row index column
    crossing(., .) %>%    # Cartesian self-join
    filter(i < i1) %>%    # reduce to lower indices
    select(-i, -i1)    # remove extraneous columns

##   x y x1 y1
## 1 1 6  2  3
## 2 1 6  3  7
## 3 1 6  4  5
## 4 2 3  3  7
## 5 2 3  4  5
## 6 3 7  4  5

or in all base R,

df$m <- 1
df$i <- seq(nrow(df))
df <- merge(df, df, by = 'm')
df[df$i.x < df$i.y, c(-1, -4, -7)]

##    x.x y.x x.y y.y
## 2    1   6   2   3
## 3    1   6   3   7
## 4    1   6   4   5
## 7    2   3   3   7
## 8    2   3   4   5
## 12   3   7   4   5