dplyr: lead() and lag() wrong when used with group_by()

I want to find the lead() and lag() element in each group, but had some wrong results.

For example, data is like this:

library(dplyr)
df = data.frame(name=rep(c('Al','Jen'),3),
                score=rep(c(100, 80, 60),2))
df

Data:

  name score
1   Al   100
2  Jen    80
3   Al    60
4  Jen   100
5   Al    80
6  Jen    60

Now I try to find out lead() and lag() scores for each person. If I sort it using arrange(), I can get the correct answer:

df %>%
  arrange(name) %>%
  group_by(name) %>%
  mutate(next.score = lead(score),
         before.score = lag(score) )

OUTPUT1:

Source: local data frame [6 x 4]
Groups: name

      name score next.score before.score
    1   Al   100         60           NA
    2   Al    60         80          100
    3   Al    80         NA           60
    4  Jen    80        100           NA
    5  Jen   100         60           80
    6  Jen    60         NA          100

Without arrange(), the result is wrong:

df %>%
  group_by(name) %>%
  mutate(next.score = lead(score),
         before.score = lag(score) )

OUTPUT2:

Source: local data frame [6 x 4]
Groups: name

  name score next.score before.score
1   Al   100         80           NA
2  Jen    80         60           NA
3   Al    60        100           80
4  Jen   100         80           60
5   Al    80         NA          100
6  Jen    60         NA           80

E.g., in 1st line, Al's next.score should be 60 (3rd line).

Anybody know why this happened? Why arrange() affects the result (the values, not just about the order)? Thanks~

Solution 1:

It seems you have to pass additional argument to lag and lead functions. When I run your function without arrange, but with order_by added, everything seems to be ok.

df %>%
group_by(name) %>%
mutate(next.score = lead(score, order_by=name),
before.score = lag(score, order_by=name))

Output:

  name score next.score before.score
1   Al   100         60           NA
2  Jen    80        100           NA
3   Al    60         80          100
4  Jen   100         60           80
5   Al    80         NA           60
6  Jen    60         NA          100

My sessionInfo():

R version 3.1.1 (2014-07-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=Polish_Poland.1250  LC_CTYPE=Polish_Poland.1250        LC_MONETARY=Polish_Poland.1250
[4] LC_NUMERIC=C                   LC_TIME=Polish_Poland.1250    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_0.4.1

loaded via a namespace (and not attached):
[1] assertthat_0.1  DBI_0.3.1       lazyeval_0.1.10 magrittr_1.5                parallel_3.1.1  Rcpp_0.11.5    
[7] tools_3.1.1

Solution 2:

It may happen that stats::lag is used instead (e.g. when restoring environments with the session package). This can easly slip through unnoticed as it it won't throw an error when being used as in the question. Double-check by simply typing lag, use conflicted package, or disambiguate the function call by calling dplyr::lag instead.

The same could happen for plyr::mutate, in case you might have loaded plyr package in your session. So make sure you're also calling dplyr::mutate.

Solution 3:

Using order_by is good when you have only one grouping variable. In case of multiple grouping variable, I could not find any solution except, writing and reading the table to get rid of grouping variables. It worked pretty well for me, but its efficiency depends on the size of table.

dplyr: lead() and lag() wrong when used with group_by()

Solution 1:

Solution 2:

Solution 3:

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