Add columns from different data frames and stack on two indicators - r

Question

We’d like to merge some columns from a data frame with the matching columns from various different data frames. Our main data frame predict looks as follows:
>predict
x1 x2 x3
1 1 1
0 1 0
1 1 0
1 1 0
0 0 1
(There may be more columns depending on the quantity of prediction runs)
Our goal is to merge this data frame with the y-columns from three different test data frames (df_1 df_2 and df_3) which all have the same structure. The needed columns are accessed through df_1$y[test] ([test] is a logical vector which identifies the 5 values which match our x-values) and have the same structure as the x-columns from predict.
The desired output would look like this:
>predict_test
x1 x2 x3 y1 y2 y3
1 1 1 1 1 1
0 1 0 0 0 0
1 1 0 0 1 0
1 1 0 1 1 1
0 0 1 0 0 1
In the next step we need to stack the x- and the y- columns into one column in order to do evaluations. It is important to stack them in the correct order, i.e. x2 under x1 and x3 under x2. The y-columns respectively.
>predict_test_stack
x_all y_all
1 1
0 0
1 0
1 1
0 0
1 1
1 0
1 1
1 1
0 0
1 1
0 0
0 0
0 1
1 1
This probably works with melt, but we don't know how to apply it while indicating two different id variables.
Thanks for your help.

Answer 1

data
df1 <- read.table(text = "x1 x2 x3
1 1 1
0 1 0
1 1 0
1 1 0
0 0 1",stringsAsFactors = FALSE,header=TRUE)
df2 <- read.table(text = "y1 y2 y3
1 1 1
0 0 0
0 1 0
1 1 1
0 0 1",stringsAsFactors = FALSE,header=TRUE)
solution
we concatenate the data.frames, then unlist the data.frame, keeping the correct number of columns. Finally we set the names by going into the data.frames to find the pattern.
list1 <- list(df1,df2)
side_by_side <- data.frame(list1)
# x1 x2 x3 y1 y2 y3
# 1 1 1 1 1 1 1
# 2 0 1 0 0 0 0
# 3 1 1 0 0 1 0
# 4 1 1 0 1 1 1
# 5 0 0 1 0 0 1
output <- data.frame(matrix(unlist(side_by_side),ncol = length(list1)))
names(output) <- sapply(list1,function(x){sub("[[:digit:]]","",names(x)[1])})
# x y
# 1 1 1
# 2 0 0
# 3 1 0
# 4 1 1
# 5 0 0
# 6 1 1
# 7 1 0
# 8 1 1
# 9 1 1
# 10 0 0
# 11 1 1
# 12 0 0
# 13 0 0
# 14 0 1
# 15 1 1