CODEWITHSUNDEEP
rdplyr
Marco
Marco

Reputation: 2807

Why does dplyr 0.8.3 still produce NA with lag operator and grouped data?

I would like to lag a variable in my grouped data with dplyr. I use lag operator. I found similiar issues like this dplyr lag function returns NAs where someone pointed to https://github.com/tidyverse/dplyr/issues/1540 to some bug Hadley had fixed in 2016. So, I assume it's solved. Why does my lag command still throws NA?

I use R version 3.6.1 and dplyr_0.8.3.

library(tidyverse)

data = data.frame(id=c(1,1,1,2,2,2,3,3,3,4,4,4), time=seq(1:3), x=rep(c(5:8), each=3))

data %>%
  group_by(id) %>%
  mutate(x_lag = lag(x, n=1, default=NA, order_by=TRUE)) %>% 
  select(id, time, x, x_lag) 

data %>%
  group_by(id) %>%
  mutate(x_lag = lag(x, n=1, default=NA, order_by=FALSE)) %>% 
  select(id, time, x, x_lag) 

data %>%
  group_by(id) %>%
  arrange(id) %>%
  mutate(x_lag = lag(x, n=1, default=NA, order_by=FALSE)) %>% 
  select(id, time, x, x_lag) 

data %>%
  group_by(id) %>%
  mutate(x_lag = lag(x, n=1, default=0, order_by=TRUE)) %>% 
  select(id, time, x, x_lag) 

# A tibble: 8 x 4
# Groups:   id, time [12]
      id  time     x x_lag
   <dbl> <int> <int> <int>
 1     1     1     5    NA
 2     1     2     5    NA
 3     1     3     5    NA
 4     2     1     6    NA
 5     2     2     6    NA
 6     2     3     6    NA
 7     3     1     7    NA
 8     3     2     7    NA

Upvotes: 1

Views: 76

Answers (1)

Allan Cameron
Allan Cameron

Reputation: 173858

I think you're just not using the order_by argument correctly. It's supposed to take another vector, not a TRUE or FALSE. In your use case, you probably don't want to use it at all.

data %>%
  group_by(id) %>%
  mutate(x_lag = lag(x, n=1, default=0)) %>% 
  select(id, time, x, x_lag) 
#> # A tibble: 12 x 4
#> # Groups:   id [4]
#>       id  time     x x_lag
#>    <dbl> <int> <int> <dbl>
#>  1     1     1     5     0
#>  2     1     2     5     5
#>  3     1     3     5     5
#>  4     2     1     6     0
#>  5     2     2     6     6
#>  6     2     3     6     6
#>  7     3     1     7     0
#>  8     3     2     7     7
#>  9     3     3     7     7
#> 10     4     1     8     0
#> 11     4     2     8     8
#> 12     4     3     8     8

Upvotes: 2

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