Is there an efficient way to check whether an R character vector contains the same elements?

Question

Let's say I have an R vector of type character:

vector1 = c("dog", "cat", "fish")
vector2 = c("fish", "fish", "fish")

The first vector vector1 contains three distinct elements: dog, cat, fish

However, vector2 contains all the same elements.

I'm looking for an efficient way to check this property in an R vector, preferably using base R.

My idea would be to use the following:

Check length(unique(vector1))==1. If TRUE, then there's only 1 element. If FALSE, there are many.

Answer 1

If you're concerned with speed is looks like either @AshOfFire's all(vec == vec[1]) or uniqueN(vec) == 1 is the best. all(...) has better performance when the elements are different, but worse when they're the same. There are other posts on this topic here as well: Test for equality among all elements of a single vector

Since you explicitly ask about speed, I don't know if this should be flagged as a dupe or not. Doing some quick microbenchmarking you can see the relative speeds:

library(data.table)
benchmark_vec <- function(vec){
  microbenchmark::microbenchmark(length(unique(vec)) == 1,
                                 all(vec == vec[1]),
                                 isTRUE(max(vec) == min(vec)),
                                 uniqueN(vec)==1,
                                 unit = "relative")
}

It looks like your best bet might be to use uniqueN(vec) == 1 since isTRUE(max(vec) == min(vec)) is quite slow when the elements are not the same.

Vector of same elements:

benchmark_vec(rep("a", 1e4))
#Unit: relative
#                         expr      min       lq     mean   median        uq      max neval
#     length(unique(vec)) == 1 6.059888 8.980080 8.807812 9.057240 10.131907 6.538035   100
#           all(vec == vec[1]) 2.039980 2.117614 2.517966 2.769089  2.820726 2.147200   100
# isTRUE(max(vec) == min(vec)) 1.000000 1.000000 1.000000 1.000000  1.000000 1.000000   100
#            uniqueN(vec) == 1 1.679993 1.794075 2.148665 2.442206  2.547134 1.385782   100

benchmark_vec(rep("a", 1e5))
#Unit: relative
#                         expr      min       lq     mean   median       uq       max neval
#     length(unique(vec)) == 1 5.732161 6.898531 7.935981 7.098417 6.776363 52.733981   100
#           all(vec == vec[1]) 2.084232 2.416316 2.366826 2.482896 2.454888  2.025258   100
# isTRUE(max(vec) == min(vec)) 1.000000 1.000000 1.000000 1.000000 1.000000  1.000000   100
#            uniqueN(vec) == 1 1.254857 1.653767 1.632287 1.707769 1.755275  1.401932   100

benchmark_vec(rep("a", 1e6))
#Unit: relative
#                         expr      min       lq     mean   median       uq      max neval
#     length(unique(vec)) == 1 5.993300 6.057139 6.478008 6.083278 6.104486 9.124542   100
#           all(vec == vec[1]) 2.249211 2.182959 2.261631 2.182345 2.224141 6.421657   100
# isTRUE(max(vec) == min(vec)) 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000   100
#            uniqueN(vec) == 1 1.325453 1.451214 1.869176 1.457588 1.470810 6.657961   100

Vector of different elements:

benchmark_vec(sample(LETTERS, 1e4, replace = TRUE))
#Unit: relative
#                         expr       min        lq      mean    median        uq  #     max neval
#     length(unique(vec)) == 1  2.989151  2.999928  3.233178  3.031249  3.260122  4.471498   100
#           all(vec == vec[1])  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   100
# isTRUE(max(vec) == min(vec)) 92.619106 91.765377 89.899963 92.227913 93.951104 64.507502   100
#            uniqueN(vec) == 1  1.478271  1.494703  1.608562  1.531235  1.637707  2.528502   100

benchmark_vec(sample(LETTERS, 1e5, replace = TRUE))
#Unit: relative
#                         expr       min        lq      mean    median        uq       max neval
#     length(unique(vec)) == 1  3.142858  3.010220  2.945582  2.887597  2.925129  4.482119   100
#           all(vec == vec[1])  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   100
# isTRUE(max(vec) == min(vec)) 97.273487 79.818135 73.850769 75.442748 71.441439 41.772795   100
#            uniqueN(vec) == 1  1.280180  1.431999  2.009661  1.431815  1.447287 32.446761   100

benchmark_vec(sample(LETTERS, 1e6, replace = TRUE))
#Unit: relative
#                         expr       min        lq      mean    median        uq       max neval
#     length(unique(vec)) == 1  3.228670  2.898367  2.799075  2.941651  2.914313  1.360938   100
#           all(vec == vec[1])  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   100
# isTRUE(max(vec) == min(vec)) 92.506220 79.923456 67.347683 78.362151 75.028194 13.611664   100
#            uniqueN(vec) == 1  2.263843  2.031869  1.861165  2.058220  2.051759  1.129074   100