CODEWITHSUNDEEP
rduplicates
Noori11
Noori11

Reputation: 73

Selecting only duplicates based on multiple columns in r

I have a dataframe in this format: 

   mpg cyl  disp  hp drat    wt  qsec vs am gear carb
1  21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
2  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
3  22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
4  21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
5  18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
6  18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
7  14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
8  24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
15 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
16 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4

I want to select all the duplicates conditioned on they are duplicated both in mpg and carb.

This would result in something as in: 

        mpg cyl  disp  hp drat    wt  qsec vs am gear carb
    1  21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
    2  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
    15 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
    16 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4

Upvotes: 2

Views: 71

Answers (3)

akrun
akrun

Reputation: 887881

With data.table, we can do

library(data.table)
as.data.table(mtcars)[, .SD[.N > 1], .(mpg, carb)]

Upvotes: 0

sbha
sbha

Reputation: 10432

Here's another dplyr option:

library(dplyr)

mtcars %>% 
  group_by(mpg, carb) %>% 
  filter(n()>1)

Upvotes: 1

AntoniosK
AntoniosK

Reputation: 16121

A dplyr solution: 

library(dplyr)

mtcars %>% 
  add_count(mpg, carb) %>% # count how many times the combinations of those variables exist and add those counts in a new column
  filter(n > 1) %>%        # keep only rows where the combination appears multiple times
  select(-n)               # remove counts

# # A tibble: 6 x 11
#    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1  21       6 160     110  3.9   2.62  16.5     0     1     4     4
# 2  21       6 160     110  3.9   2.88  17.0     0     1     4     4
# 3  10.4     8 472     205  2.93  5.25  18.0     0     0     3     4
# 4  10.4     8 460     215  3     5.42  17.8     0     0     3     4
# 5  30.4     4  75.7    52  4.93  1.62  18.5     1     1     4     2
# 6  30.4     4  95.1   113  3.77  1.51  16.9     1     1     5     2

Upvotes: 1

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