Pivot by group for unequal data size

I have the following DF:

DF = structure(list(ID = c(21785L, 21785L, 21785L), V1 = c(0.828273303, 
                                                  6.404590021, 0.775568448), V2 = c(2L, 3L, 2L), V3 = c(NA, 1.122899914, 
                                                                                                        0.850113234), V4 = c(NA, 4L, 3L), V5 = c(NA, 0.866757168, 0.868943246
                                                                                                        ), V6 = c(NA, 5L, 4L), V7 = c(NA, 0.563804788, 0.728656328), 
           V8 = c(NA, 6L, 5L), V9 = c(NA, 0.888109208, 0.823803733), 
           V10 = c(NA, 7L, 6L), V11 = c(NA, 0.578834113, 0.863467391
           ), V12 = c(NA, 1L, 7L), V13 = c(NA, NA, 0.939920869)), class = "data.frame", row.names = c(5L, 
                                                                                                      163L, 167L))

Output: 
Row      ID        V1 V2        V3 V4        V5 V6        V7 V8        V9 V10       V11 V12       V13
5   21785 0.8282733  2        NA NA        NA NA        NA NA        NA  NA        NA  NA        NA
163 21785 6.4045900  3 1.1228999  4 0.8667572  5 0.5638048  6 0.8881092   7 0.5788341   1        NA
167 21785 0.7755684  2 0.8501132  3 0.8689432  4 0.7286563  5 0.8238037   6 0.8634674   7 0.9399209

The data can be broken down into 3 parts:

1. ID per participant
2. Odd Columns representing standardized heart rate
3. Even columns representing day number of week (1 = sunday)

I have 100 plus unique participants and 3000 rows of data with unequal data per day, hence the NAs.

I would like to pivot the data into one column per part

• so that: col1 = ID, col2 = HR, col3 = Weekday

I have tried several methods based on similar questions such as:

    # melt the data frame to put all the metrics in a single column
    DF2 = reshape2::melt(DF, id.vars = c("ID"))

    # split the data by ID
    DF3 = split(DF2, DF2$ID)

    # allocate empty DF with 3 columns for future appending
    DF_Organized = data.frame()[1,3]

    # make the data into 3 new columns, 1 for ID, HR, weekday
    for (m in 1:length(DF3)){

    DF_tmp = DF3[m] %>%
      data.frame %>% na.omit() # convert to DF, remove NAs
      setNames(., c("ID","colx","Value")) %>% # set names for clarity
      mutate(ind = rep(c(1, 2),length.out = n())) %>% # assign 1 to amplitude and 2 to day values in each row
      group_by(ind) %>% # group by value type
      mutate(id = row_number()) %>% # make new column that determines location of data by previous assignment
      spread(ind, Value) %>% # organize data by new ID
      select(-id) #clean 

    # reorganize the NAs to the bottom
DF_tmp2 = setNames(do.call(function(...) rowr::cbind.fill(..., fill = NA),
                          lapply(DF_tmp, na.omit)),colnames(DF_tmp)) %>% 
  na.omit() %>% 
  select(-colx) %>% 
  setNames(., c("ID","HR","Weekday")) # set names for clarity

I get close but not accurate:

Actual Output:

> DF_tmp2
      ID HR        Weekday
1  21785 0.8282733 6.4045900
2  21785 0.7755684 2.0000000
3  21785 3.0000000 2.0000000
4  21785 1.1228999 0.8501132

. . . There's misalignment and inaccurate combinations. Any help is appreciated.

Expected Output:

> DF_tmp2
          ID HR        Weekday
    1  21785 0.8282733 2.0000000
    2  21785 6.4045900 3.0000000
    3  21785 1.1228999 4.0000000
    4  21785 0.8667572 5.0000000
    5  21875 0.5638048 6.0000000
.
.
.