Nested pipe chain in dplyr / left_join

Question

In the process of trying to get a grouped lag variable (which isn't possible just using lag), the suggested solution was to pull the data out, lag the distinct rows, and then re-join it.

I prefer to do this without creating intermediate objects, and would like to do it in the midst of a chain. However it doesn't seem to work as I would expect, and the problem seems to be some interaction between using the . and the nested chain inside the left_join.

require(tidyverse)
#> Loading required package: tidyverse
df <- data.frame(Team = c("A", "A", "A", "A", "B", "B", "B", "C", "C", "D", "D"),
                 Date = c("2016-05-10","2016-05-10", "2016-05-10", "2016-05-10",
                          "2016-05-12", "2016-05-12", "2016-05-12",
                          "2016-05-15","2016-05-15",
                          "2016-05-30", "2016-05-30"), 
                 Points = c(1,4,3,2,1,5,6,1,2,3,9)
)


#This works:
df %>% left_join(x = ., y = df %>% 
                   distinct(Team, Date) %>% 
                   mutate(Date_Lagged = lag(Date)))
#> Joining, by = c("Team", "Date")
#>    Team       Date Points Date_Lagged
#> 1     A 2016-05-10      1        <NA>
#> 2     A 2016-05-10      4        <NA>
#> 3     A 2016-05-10      3        <NA>
#> 4     A 2016-05-10      2        <NA>
#> 5     B 2016-05-12      1  2016-05-10
#> 6     B 2016-05-12      5  2016-05-10
#> 7     B 2016-05-12      6  2016-05-10
#> 8     C 2016-05-15      1  2016-05-12
#> 9     C 2016-05-15      2  2016-05-12
#> 10    D 2016-05-30      3  2016-05-15
#> 11    D 2016-05-30      9  2016-05-15

#And this works:
df %>% left_join(x = ., y = .)
#> Joining, by = c("Team", "Date", "Points")
#>    Team       Date Points
#> 1     A 2016-05-10      1
#> 2     A 2016-05-10      4
#> 3     A 2016-05-10      3
#> 4     A 2016-05-10      2
#> 5     B 2016-05-12      1
#> 6     B 2016-05-12      5
#> 7     B 2016-05-12      6
#> 8     C 2016-05-15      1
#> 9     C 2016-05-15      2
#> 10    D 2016-05-30      3
#> 11    D 2016-05-30      9

#This doesn't work despite the fact that `.` is df.  
df %>% left_join(x = ., y = . %>% 
                   distinct(Team, Date) %>% 
                   mutate(Date_Lagged = lag(Date)))
#> Error in UseMethod("tbl_vars"): no applicable method for 'tbl_vars' applied to an object of class "c('fseq', 'function')"



#Desired output
distinct(df, Team, Date) %>%
  mutate(Date_Lagged = lag(Date)) %>%
  right_join(., df) %>%
  select(Team, Date, Points, Date_Lagged)
#> Joining, by = c("Team", "Date")
#>    Team       Date Points Date_Lagged
#> 1     A 2016-05-10      1        <NA>
#> 2     A 2016-05-10      4        <NA>
#> 3     A 2016-05-10      3        <NA>
#> 4     A 2016-05-10      2        <NA>
#> 5     B 2016-05-12      1  2016-05-10
#> 6     B 2016-05-12      5  2016-05-10
#> 7     B 2016-05-12      6  2016-05-10
#> 8     C 2016-05-15      1  2016-05-12
#> 9     C 2016-05-15      2  2016-05-12
#> 10    D 2016-05-30      3  2016-05-15
#> 11    D 2016-05-30      9  2016-05-15

Created on 2018-06-12 by the reprex package (v0.2.0).

Answer 1

Though this isn't an answer to my question (Onyambo provided that!), I wanted to share that I found an alternative way to accomplish the same thing. Basically you use group_by() and nest() to squish the tibble and get the repeated vars out of the way, do the lag, and then unnest().

df %>% 
  group_by(Team, Date) %>% 
  nest() %>% 
  mutate(Date_Lagged = lag(Date)) %>% 
  unnest()
#> # A tibble: 11 x 4
#>    Team  Date       Date_Lagged Points
#>    <fct> <fct>      <fct>        <dbl>
#>  1 A     2016-05-10 <NA>             1
#>  2 A     2016-05-10 <NA>             4
#>  3 A     2016-05-10 <NA>             3
#>  4 A     2016-05-10 <NA>             2
#>  5 B     2016-05-12 2016-05-10       1
#>  6 B     2016-05-12 2016-05-10       5
#>  7 B     2016-05-12 2016-05-10       6
#>  8 C     2016-05-15 2016-05-12       1
#>  9 C     2016-05-15 2016-05-12       2
#> 10 D     2016-05-30 2016-05-15       3
#> 11 D     2016-05-30 2016-05-15       9

Created on 2018-06-14 by the reprex package (v0.2.0).