Remove/collapse consecutive duplicate values in sequence

Question

I have the following dataframe:

a a a b c c d e a a b b b e e d d 

The required result should be

a b c d e a b e d  

It means no two consecutive rows should have same value. How it can be done without using loop.

As my data set is quite huge, looping is taking lot of time to execute.

The dataframe structure is like the following

a 1  a 2 a 3 b 2 c 4 c 1 d 3 e 9 a 4 a 8 b 10 b 199 e 2 e 5 d 4 d 10 

Result:

a 1  b 2 c 4 d 3 e 9 a 4 b 10 e 2 d 4 

Its should delete the entire row.

Answer 1

One easy way is to use rle:

Here's your sample data:

x <- scan(what = character(), text = "a a a b c c d e a a b b b e e d d") # Read 17 items 

rle returns a list with two values: the run length ("lengths"), and the value that is repeated for that run ("values").

rle(x)$values # [1] "a" "b" "c" "d" "e" "a" "b" "e" "d" 

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Update: For a data.frame

If you are working with a data.frame, try something like the following:

## Sample data mydf <- data.frame(   V1 = c("a", "a", "a", "b", "c", "c", "d", "e",           "a", "a", "b", "b", "e", "e", "d", "d"),   V2 = c(1, 2, 3, 2, 4, 1, 3, 9,           4, 8, 10, 199, 2, 5, 4, 10) )  ## Use rle, as before X <- rle(mydf$V1) ## Identify the rows you want to keep Y <- cumsum(c(1, X$lengths[-length(X$lengths)])) Y # [1]  1  4  5  7  8  9 11 13 15 mydf[Y, ] #    V1 V2 # 1   a  1 # 4   b  2 # 5   c  4 # 7   d  3 # 8   e  9 # 9   a  4 # 11  b 10 # 13  e  2 # 15  d  4 

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Update 2

The "data.table" package has a function rleid that lets you do this quite easily. Using mydf from above, try:

library(data.table) as.data.table(mydf)[, .SD[1], by = rleid(V1)] #    rleid V2 # 1:     1  1 # 2:     2  2 # 3:     3  4 # 4:     4  3 # 5:     5  9 # 6:     6  4 # 7:     7 10 # 8:     8  2 # 9:     9  4 

Answer 2

library(dplyr) x <- c("a", "a", "a", "b", "c", "c", "d", "e", "a", "a", "b", "b", "b", "e", "e", "d", "d") x[x!=lag(x, default=1)] #[1] "a" "b" "c" "d" "e" "a" "b" "e" "d" 

EDIT: For data.frame

  mydf <- data.frame(     V1 = c("a", "a", "a", "b", "c", "c", "d", "e",           "a", "a", "b", "b", "e", "e", "d", "d"),     V2 = c(1, 2, 3, 2, 4, 1, 3, 9,           4, 8, 10, 199, 2, 5, 4, 10),    stringsAsFactors=FALSE) 

dplyr solution is one liner:

mydf %>% filter(V1!= lag(V1, default="1")) #  V1 V2 #1  a  1 #2  b  2 #3  c  4 #4  d  3 #5  e  9 #6  a  4 #7  b 10 #8  e  2 #9  d  4 

post scriptum

lead(x,1) suggested by @Carl Witthoft iterates in reverse order.

leadit<-function(x) x!=lead(x, default="what") rows <- leadit(mydf[ ,1]) mydf[rows, ]  #   V1  V2 #3   a   3 #4   b   2 #6   c   1 #7   d   3 #8   e   9 #10  a   8 #12  b 199 #14  e   5 #16  d  10