Select 除一列外所有行都是重复的

Question

I want to find rows in a dataset where the values in all columns, except for one, match. After much messing around trying unsuccessfully to get duplicated() to return all instances of the duplicate rows (not just the first instance), I figured out a way to do it (below).

For example, I want to identify all rows in the Iris dataset that are equal except for Petal.Width.

require(tidyverse)
x = iris%>%select(-Petal.Width)
dups = x[x%>%duplicated(),]
answer =  iris%>%semi_join(dups)

> answer 
   Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
1           5.1         3.5          1.4         0.2    setosa
2           4.9         3.1          1.5         0.1    setosa
3           4.8         3.0          1.4         0.1    setosa
4           5.1         3.5          1.4         0.3    setosa
5           4.9         3.1          1.5         0.2    setosa
6           4.8         3.0          1.4         0.3    setosa
7           5.8         2.7          5.1         1.9 virginica
8           6.7         3.3          5.7         2.1 virginica
9           6.4         2.8          5.6         2.1 virginica
10          6.4         2.8          5.6         2.2 virginica
11          5.8         2.7          5.1         1.9 virginica
12          6.7         3.3          5.7         2.5 virginica

As you can see, that works, but this is one of those times when I'm almost certain that lots other folks need this functionality, and that I'm ignorant of a single function that does this in fewer steps or a generally tidier way. Any suggestions?

An alternate approach, from at least two other posts, applied to this case would be:

answer = iris[duplicated(iris[-4]) | duplicated(iris[-4], fromLast = TRUE),]

But that also seems like just a different workaround instead of single function. Both approaches take the same amount of time. (0.08 sec on my system). Is there no neater/faster way of doing this?

eg something like iris%>%duplicates(all=TRUE,ignore=Petal.Width)

Answer 1

iris[duplicated(iris[,-4]) | duplicated(iris[,-4], fromLast = TRUE),]

Of duplicate rows (regardless of column 4) duplicated(iris[,-4]) gives the second row of the duplicate sets, rows 18, 35, 46, 133, 143 & 145, and duplicated(iris[,-4], fromLast = TRUE) gives the first row per duplicate set, 1, 10, 13, 102, 125 and 129. By adding | this results in 12 TRUE s, so it returns the expected output.

Or perhaps with dplyr: Basically you group on all variables except Petal.Width , count how much they occur, and filter those which occur more than once.

library(dplyr)
iris %>% 
  group_by_at(vars(-Petal.Width)) %>% 
  filter(n() > 1)

   Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
          <dbl>       <dbl>        <dbl>       <dbl>    <fctr>
 1          5.1         3.5          1.4         0.2    setosa
 2          4.9         3.1          1.5         0.1    setosa
 3          4.8         3.0          1.4         0.1    setosa
 4          5.1         3.5          1.4         0.3    setosa
 5          4.9         3.1          1.5         0.2    setosa
 6          4.8         3.0          1.4         0.3    setosa
 7          5.8         2.7          5.1         1.9 virginica
 8          6.7         3.3          5.7         2.1 virginica
 9          6.4         2.8          5.6         2.1 virginica
10          6.4         2.8          5.6         2.2 virginica
11          5.8         2.7          5.1         1.9 virginica
12          6.7         3.3          5.7         2.5 virginica

Answer 2

I think janitor can do this somewhat directly.

library(janitor)

get_dupes(iris, !Petal.Width)

# get_dupes(iris, !Petal.Width)[,names(iris)] # alternative: no count column

   Sepal.Length Sepal.Width Petal.Length   Species dupe_count Petal.Width
1           4.8         3.0          1.4    setosa          2         0.1
2           4.8         3.0          1.4    setosa          2         0.3
3           4.9         3.1          1.5    setosa          2         0.1
4           4.9         3.1          1.5    setosa          2         0.2
5           5.1         3.5          1.4    setosa          2         0.2
6           5.1         3.5          1.4    setosa          2         0.3
7           5.8         2.7          5.1 virginica          2         1.9
8           5.8         2.7          5.1 virginica          2         1.9
9           6.4         2.8          5.6 virginica          2         2.1
10          6.4         2.8          5.6 virginica          2         2.2
11          6.7         3.3          5.7 virginica          2         2.1
12          6.7         3.3          5.7 virginica          2         2.5

Answer 3

I looked into the source of duplicated but would be interested to see if anyone can find anything faster. It might involve going to Rcpp or something similar though. On my machine, the base method is the fastest but your original method is actually better than the most readable dplyr method. I think that wrapping a function like this for your own purposes ought to be sufficient, since your run times don't seem excessively long anyway you can simply do iris %>% opts("Petal.Width") for pipeability if that's the main concern.

library(tidyverse)
library(microbenchmark)

opt1 <- function(df, ignore) {
  ignore = enquo(ignore)
  x <- df %>% select(-!!ignore)
  dups <- x[x %>% duplicated(), ]
  answer <- iris %>% semi_join(dups)
}

opt2 <- function(df, ignore) {
  index <-  which(colnames(df) == ignore)
  df[duplicated(df[-index]) | duplicated(df[-index], fromLast = TRUE), ]
}

opt3 <- function(df, ignore){
  ignore <-  enquo(ignore)
  df %>%
    group_by_at(vars(-!!ignore)) %>%
    filter(n() > 1)
}


microbenchmark(
  opt1 = suppressMessages(opt1(iris, Petal.Width)),
  opt2 = opt2(iris, "Petal.Width"),
  opt3 = opt3(iris, Petal.Width)
)
#> Unit: milliseconds
#>  expr      min       lq     mean   median       uq       max neval cld
#>  opt1 3.427753 4.024185 4.851445 4.464072 5.069216 12.800890   100  b 
#>  opt2 1.712975 1.908130 2.403859 2.133632 2.542871  7.557102   100 a  
#>  opt3 6.604614 7.334304 8.461424 7.920369 8.919128 24.255678   100   c

Created on 2018-07-12 by the reprex package (v0.2.0).