Finding ALL duplicate rows, including “elements wi

问题:

R's duplicated returns a vector showing whether each element of a vector or data frame is a duplicate of an element with a smaller subscript. So if rows 3, 4, and 5 of a 5-row data frame are the same, duplicated will give me the vector

FALSE, FALSE, FALSE, TRUE, TRUE

But in this case I actually want to get

FALSE, FALSE, TRUE, TRUE, TRUE

that is, I want to know whether a row is duplicated by a row with a larger subscript too.

回答1:

duplicated has a fromLast argument. The "Example" section of ?duplicated shows you how to use it. Just call duplicated twice, once with fromLast=FALSE and once with fromLast=TRUE and take the rows where either are TRUE.

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Some late Edit: You didn't provide a reproducible example, so here's an illustration kindly contributed by @jbaums

vec <- c("a", "b", "c","c","c") 
vec[duplicated(vec) | duplicated(vec, fromLast=TRUE)]
## [1] "c" "c" "c"

回答2:

You need to assemble the set of duplicated values, apply unique, and then test with %in%. As always, a sample problem will make this process come alive.

> vec <- c("a", "b", "c","c","c")
> vec[ duplicated(vec)]
[1] "c" "c"
> unique(vec[ duplicated(vec)])
[1] "c"
>  vec %in% unique(vec[ duplicated(vec)]) 
[1] FALSE FALSE  TRUE  TRUE  TRUE

回答3:

I've had the same question, and if I'm not mistaken, this is also an answer.

vec[col %in% vec[duplicated(vec$col),]$col]

Dunno which one is faster, though, the dataset I'm currently using isn't big enough to make tests which produce significant time gaps.

回答4:

Duplicated rows in a dataframe could be obtained with dplyr by doing

df = bind_rows(iris, head(iris, 20)) # build some test data
df %>% group_by_all() %>% filter(n()>1) %>% ungroup()

To exclude certain columns group_by_at(vars(-var1, -var2)) could be used instead to group the data.

If the row indices and not just the data is actually needed, you could add them first as in:

df %>% add_rownames %>% group_by_at(vars(-rowname)) %>% filter(n()>1) %>% pull(rowname)

回答5:

If you are interested in which rows are duplicated for certain columns you can use a plyr approach:

ddply(df, .(col1, col2), function(df) if(nrow(df) > 1) df else c())

Adding a count variable with dplyr:

df %>% add_count(col1, col2) %>% filter(n > 1)  # data frame
df %>% add_count(col1, col2) %>% select(n) > 1  # logical vector

For duplicate rows (considering all columns):

df %>% group_by_all %>% add_tally %>% ungroup %>% filter(n > 1)
df %>% group_by_all %>% add_tally %>% ungroup %>% select(n) > 1

The benefit of these approaches is that you can specify how many duplicates as a cutoff.