Fill missing values in data.frame using dplyr complete within groups

Question

I'm trying to fill missing values in my dataframe, but I do not want all possible combinations of variables - I only want to fill based on a grouping of three variables: coursecode, year, and week.

I've looked into complete() in tidyr library but I can't get it to work, even after looking at Using tidyr::complete with group_by and https://blog.rstudio.org/2015/09/13/tidyr-0-3-0/

I have observers that collect data on given weeks of the year at different courses. For example, data might be collected in my larger dataset for weeks 1-10, but I only care about the missing weeks that occurred in a particular course-year combination. e.g.,

• In course A in year 2000, data were collected on weeks 1, 3, and 4.
• I want to know that week 2 is missing.
• I don't care that week 5 is missing, even though someone else at course B collected data on week 5 in 2000.

Example:

library(dplyr)
library(tidyr)

df <- data.frame(coursecode = rep(c("A", "B"), each = 6),
                 year = rep(c(2000, 2000, 2000, 2001, 2001, 2001), 2), 
                 week = c(1, 3, 4, 1, 2, 3, 2, 3, 5, 3, 4, 5),
                 values = c(1:12),
                 othervalues = c(12:23),
                 region = "Big")

df

   coursecode year week values othervalues region
1           A 2000    1      1          12    Big
2           A 2000    3      2          13    Big
3           A 2000    4      3          14    Big
4           A 2001    1      4          15    Big
5           A 2001    2      5          16    Big
6           A 2001    3      6          17    Big
7           B 2000    2      7          18    Big
8           B 2000    3      8          19    Big
9           B 2000    5      9          20    Big
10          B 2001    3     10          21    Big
11          B 2001    4     11          22    Big
12          B 2001    5     12          23    Big

try with complete: (not my desired output)

    df %>% 
      complete(coursecode, year, region, nesting(week))

# A tibble: 20 x 6
   coursecode  year region  week values othervalues
       <fctr> <dbl> <fctr> <dbl>  <int>       <int>
1           A  2000    Big     1      1          12
2           A  2000    Big     2     NA          NA
3           A  2000    Big     3      2          13
4           A  2000    Big     4      3          14
5           A  2000    Big     5     NA          NA
6           A  2001    Big     1      4          15
7           A  2001    Big     2      5          16
8           A  2001    Big     3      6          17
9           A  2001    Big     4     NA          NA
10          A  2001    Big     5     NA          NA
11          B  2000    Big     1     NA          NA
12          B  2000    Big     2      7          18
13          B  2000    Big     3      8          19
14          B  2000    Big     4     NA          NA
15          B  2000    Big     5      9          20
16          B  2001    Big     1     NA          NA
17          B  2001    Big     2     NA          NA
18          B  2001    Big     3     10          21
19          B  2001    Big     4     11          22
20          B  2001    Big     5     12          23

Desired output

   coursecode  year region  week values othervalues
       <fctr> <dbl> <fctr> <dbl>  <int>       <int>
1           A  2000    Big     1      1          12
2           A  2000    Big     2     NA          NA
3           A  2000    Big     3      2          13
4           A  2000    Big     4      3          14
5           A  2001    Big     1      4          15
6           A  2001    Big     2      5          16
7           A  2001    Big     3      6          17
8           B  2000    Big     2      7          18
9           B  2000    Big     3      8          19
10          B  2000    Big     4     NA          NA
11          B  2000    Big     5      9          20
12          B  2001    Big     3     10          21
13          B  2001    Big     4     11          22
14          B  2001    Big     5     12          23

Answer 1

We can try with expand and left_join

library(dplyr)
library(tidyr)
df %>%
   group_by(coursecode, year, region) %>%
   expand(week = full_seq(week, 1)) %>% 
   left_join(., df)
#   coursecode  year region  week values othervalues
#       <fctr> <dbl> <fctr> <dbl>  <int>       <int>
#1           A  2000    Big     1      1          12
#2           A  2000    Big     2     NA          NA
#3           A  2000    Big     3      2          13
#4           A  2000    Big     4      3          14
#5           A  2001    Big     1      4          15
#6           A  2001    Big     2      5          16
#7           A  2001    Big     3      6          17
#8           B  2000    Big     2      7          18
#9           B  2000    Big     3      8          19
#10          B  2000    Big     4     NA          NA
#11          B  2000    Big     5      9          20
#12          B  2001    Big     3     10          21
#13          B  2001    Big     4     11          22
#14          B  2001    Big     5     12          23

Answer 2

As the OP was using complete() (which is based on expand() and left_join()), one could stick to it and save themselves writing an extra line of code compared to @akrun's solution:

# example data
df <- data.frame(coursecode = rep(c("A", "B"), each = 6),
                 year = rep(c(2000, 2000, 2000, 2001, 2001, 2001), 2), 
                 week = c(1, 3, 4, 1, 2, 3, 2, 3, 5, 3, 4, 5),
                 values = c(1:12),
                 othervalues = c(12:23),
                 region = "Big")

# complete by group
library(dplyr)
library(tidyr)
df %>% 
  group_by(coursecode, year, region) %>% 
  complete(week = full_seq(week, 1))
#> # A tibble: 14 x 6
#> # Groups:   coursecode, year, region [4]
#>    coursecode  year region  week values othervalues
#>    <chr>      <dbl> <chr>  <dbl>  <int>       <int>
#>  1 A           2000 Big        1      1          12
#>  2 A           2000 Big        2     NA          NA
#>  3 A           2000 Big        3      2          13
#>  4 A           2000 Big        4      3          14
#>  5 A           2001 Big        1      4          15
#>  6 A           2001 Big        2      5          16
#>  7 A           2001 Big        3      6          17
#>  8 B           2000 Big        2      7          18
#>  9 B           2000 Big        3      8          19
#> 10 B           2000 Big        4     NA          NA
#> 11 B           2000 Big        5      9          20
#> 12 B           2001 Big        3     10          21
#> 13 B           2001 Big        4     11          22
#> 14 B           2001 Big        5     12          23

^{Created on 2020-10-29 by the reprex package (v0.3.0)}