Merge dataframes without duplicate rows with NA values

I have data from multiple files that have data hourly observations for different variables. I'm trying to combine them to a single dataframe where I have every observation for each variable for the whole time interval. Some variables have data in multiple dataframes for different time intervals. Each dataframe has a time column that can be used for joining.

The problem is that full_join creates more rows than my data has hours (df1). Instead I would like to get a dataframe (df2) without NA values and extra rows. One solution is to join the dataframes in specific order but I'm hoping for a more general solution that works with larger scale for combining datasets for this type of data.

library(tidyverse)
a <- data.frame(hour = 1:10, x = 5)
b <- data.frame(hour = 1:15, y = 15)
c <- data.frame(hour = 11:20, x = 10)

list <- list(a,b,c)

df1 <- list %>%
  reduce(full_join)

df1
   hour  x  y
1     1  5 15
2     2  5 15
3     3  5 15
4     4  5 15
5     5  5 15
6     6  5 15
7     7  5 15
8     8  5 15
9     9  5 15
10   10  5 15
11   11 NA 15
12   12 NA 15
13   13 NA 15
14   14 NA 15
15   15 NA 15
16   11 10 NA
17   12 10 NA
18   13 10 NA
19   14 10 NA
20   15 10 NA
21   16 10 NA
22   17 10 NA
23   18 10 NA
24   19 10 NA
25   20 10 NA

df2 <- full_join(a,c) %>%
  full_join(b)

df2
   hour  x  y
1     1  5 15
2     2  5 15
3     3  5 15
4     4  5 15
5     5  5 15
6     6  5 15
7     7  5 15
8     8  5 15
9     9  5 15
10   10  5 15
11   11 10 15
12   12 10 15
13   13 10 15
14   14 10 15
15   15 10 15
16   16 10 NA
17   17 10 NA
18   18 10 NA
19   19 10 NA
20   20 10 NA

EDIT:
I got the correct result with tidyverse with following code:

df <- list %>%
  reduce(full_join) %>%
  group_by(hour) %>%
  summarise_all(mean, na.rm = T)

With data.table meltand dcast also seem to work

dt1 <- as.data.table(df1)
m <- as.data.table(melt(dt1, id.vars = "hour"))
dc <- dcast.data.table(m, hour~variable, fun.aggregate = mean, na.rm = T)
df <- as.data.frame(dc)

I guess mean can be replaced with some other function but I couldn't figure which.

First, you need to specify the by keyword in join.
Here is a solution using coalesce_join from E. Visel blog post. I slightly modified it to use coalesce only in the case when there are common columns in the two dataframes to join, otherwise it fails because the coalesced dataframe is empty.

library(tidyverse)

a <- data.frame(hour = 1:10, x = 5)
b <- data.frame(hour = 1:15, y = 15)
c <- data.frame(hour = 11:20, x = 10)

l <- list(a,b,c)

coalesce_join <- function(x, y, 
                              by = NULL, suffix = c(".x", ".y"), 
                              join = dplyr::full_join, ...) {
        joined <- join(x, y, by = by, suffix = suffix, ...)
        # names of desired output
        cols <- union(names(x), names(y))
        common_cols <- setdiff(intersect(names(x), names(y)), by)
        if(length(common_cols) > 0) {
          # if there are common columns, we need to coalesce them
          to_coalesce <- names(joined)[!names(joined) %in% cols]
          suffix_used <- suffix[ifelse(endsWith(to_coalesce, suffix[1]), 1, 2)]
          # remove suffixes and deduplicate
          to_coalesce <- unique(substr(
              to_coalesce, 
              1, 
              nchar(to_coalesce) - nchar(suffix_used)
          ))
          
          coalesced <- purrr::map_dfc(to_coalesce, ~dplyr::coalesce(
              joined[[paste0(.x, suffix[1])]], 
              joined[[paste0(.x, suffix[2])]]
          ))
          names(coalesced) <- to_coalesce

          joined <- dplyr::bind_cols(joined, coalesced)[cols]
      }
      return(joined)
}

reduce(l, coalesce_join, by="hour")

This snippet gives me the desired output without caring for the column names.

NB: According to the documentation, coalesce finds the first non-missing element, so you have to be aware of this behaviour if there are overlapping values of x in several dataframes.

NB: I renamed l to be your list object.
I believe it is not good for code clarity to overload language-specific keywords.

Here is a version where the primary key can be a composite (for example hour and group). It doesn't work with aggregating like your own solution you added to the question.

## Identify (composite) primary key 
mykeys <- c("hour")

## Using the data.table package below
list %>%
    lapply(setDT)

## Create a dataset with all unique combinations of the keys to join onto
full <- lapply(list, function(dt) dt[,..mykeys]) %>%
    rbindlist %>%
    unique

## Can make the keys as actual keys on the full dataset but not necessary
# setkeyv(full,mykeys)

## Join all variables onto the full dataset.
## The preserved values of duplicates are from the last dataset in the order of 
## the list object. This step is done by reference.
list %>%
    lapply(function(dt) {
        n <- names(dt)
        full[dt,
             on = mykeys,
             (n) := mget(paste0("i.", n))]
    })
full

Here is an example dataset with a composite primary key and more columns to help identify what is going on. Again, note that the order of the datasets in list matter.

mykeys <- c("hour"
            ,"group"
)

a <- data.frame(hour = 1:10, 
                group = 1,
                x = 5, z = 1, a = 1)
b <- data.frame(hour = 1:15, 
                group = 1,
                y = 15, z = 2, b = 1)
c <- data.frame(hour = 11:20, 
                group = 1,
                # group = 2,
                x = 10, z = 3, c = 1)