Merge data frames without Merge Function - r

Question

I have two data frames called height.txt
ID: 1 2 3 4 5
Height: 67 60 62 55 69
and next data frame is weight.txt
ID: 1 2 4 5 6
Weight: 110 123 150 170 185
The goal is to merge these two data frames together, without using the merge() function in R, and the output should be in the image. How would I do this? This is for practice, I know merge() does the trick, but I am supposed to do this without it, it is for a class.
Edit:
Data in a copy&paste format.
ID <- scan(text = "1 2 3 4 5")
Height <- scan(text = "67 60 62 55 69")
df1 <- data.frame(ID, Height)
ID <- scan(text = "1 2 4 5 6")
Weight <- scan(text = "110 123 150 170 185")
df2 <- data.frame(ID, Weight)

Answer 1

It's a simple repeated use of match.
Create a data.frame with all the elements of the common column, ID, with no repetitions.
match the ID's of each of the dataframes with the ID of the result res.
Assign the other columns.
Remember to create each of the other columns before assigning values to them.
res <- data.frame(ID = unique(c(df1$ID, df2$ID)))
i <- match(df1$ID, res$ID)
j <- na.omit(match(res$ID, df1$ID))
res$Height <- NA
res$Height[i] <- df1$Height[j]
i <- match(df2$ID, res$ID)
j <- na.omit(match(res$ID, df2$ID))
res$Weight <- NA
res$Weight[i] <- df2$Weight[j]
res
# ID Height Weight
#1 1 67 110
#2 2 60 123
#3 3 62 NA
#4 4 55 150
#5 5 69 170
#6 6 NA 185
identical(res, merge(df1, df2, all = TRUE))
#[1] TRUE
Edit.
Answering to a question in a comment about how general this solution is. From help("merge"):
Details
merge is a generic function whose principal method is for data frames:
the default method coerces its arguments to data frames and calls the
"data.frame" method.
The method merge.data.frame in R 3.6.2 is 158 code lines long, this solution is not general at all.
Edit 2.
A function generalizing the code above could the following.
merge_by_one_col <- function(X, Y, col = "ID"){
common <- unique(c(X[[col]], Y[[col]]))
res <- data.frame(common)
names(res) <- col
i <- match(X[[col]], res[[col]])
j <- na.omit(match(res[[col]], X[[col]]))
for(new in setdiff(names(X), col)){
res[[new]] <- NA
res[[new]][i] <- X[[new]][j]
}
i <- match(Y[[col]], res[[col]])
j <- na.omit(match(res[[col]], Y[[col]]))
for(new in setdiff(names(Y), names(res))){
res[[new]] <- NA
res[[new]][i] <- Y[[new]][j]
}
res
}
merge_by_one_col(df1, df2)

Answer 2

I used cbind after rbinding the missing IDs from each data frame and sorting by ID.
df1_ <- rbind(df1, data.frame(ID=setdiff(df2$ID, df1$ID), Height=NA))
df2_ <- rbind(df2, data.frame(ID=setdiff(df1$ID, df2$ID), Weight=NA))
cbind(df1_[order(df1_$ID),], df2_[order(df2_$ID), -1, drop=FALSE])
ID Height Weight
1 1 67 110
2 2 60 123
3 3 62 NA
4 4 55 150
5 5 69 170
6 6 NA 185
Edit:
Generalizing so that no column names are required (except the "by" column "ID")
n1 <- setdiff(df1$ID, df2$ID); n1
n2 <- setdiff(df2$ID, df1$ID); n2
df1a <- df1[rep(nrow(df1)+1, length(n1)),]; df1a
df2a <- df2[rep(nrow(df2)+1, length(n2)),]; df2a
df1a$ID <- n2
df2a$ID <- n1
df1_ <- rbind(df1, df1a)
df2_ <- rbind(df2, df2a)
res <- cbind(df1_[order(df1_$ID),], df2_[order(df2_$ID), -1, drop=FALSE])
rownames(res) <- 1:nrow(res)
res
ID Height Weight
1 1 67 110
2 2 60 123
3 3 62 NA
4 4 55 150
5 5 69 170
NA 6 NA 185
Edit 2: Using rbind.fill from the plyr package:
library(plyr)
df1_ <- rbind.fill(df1, data.frame(ID=setdiff(df2$ID, df1$ID)))
df2_ <- rbind.fill(df2, data.frame(ID=setdiff(df1$ID, df2$ID)))
res <- cbind(df1_[order(df1_$ID),], df2_[order(df2_$ID), -1, drop=FALSE])
identical(res, merge(df1, df2, all=TRUE))
# TRUE