Correlations for pairs of combinations

Question

I have 22 variables, and I'd like to get the correlation scores, not as a matrix of correlation, but in a data frame, by pairs...

I mean... Not like this

    v1  v2  v3  v4
v1  1   x   x   x
v2  x   1   x   x
v3  x   x   1   x
v4  x   x   x   1

but like this:

var1  var2 cor
v1    v2   x
v1    v3   x
v1    v4   x
v2    v3   x
v2    v4   x
v3    v4   x

I'm new to R and I have been researching a lot, and I end up with a code that, sincerely, Is not efficient at all... My code creates a huge data frame with all the possible combinations for 22 variables (which is 4194304 combinatios... a lot!!! ) ... And then the code assigns the correlations just for the first 211 rows, which are the combinations with only 2 variables... Then I exclude everything I'm not interested in. Well... I get what I need. But I'm sure this is a very dumb way to do this and I'd like to learn a better way... Any tips?

My code:

#Getting the variable names from the data frame
av_variables<-variable.names(data.1)

#Creating a huge data frame for all possible combinations
corr_combinations <- as.data.frame(matrix(1,0,length(av_variables)))
for (i in 1:length(av_variables)){
  corr_combinations.i <- t(combn(av_variables,i))
  corr_combinations.new <- as.data.frame(matrix(1,length(corr_combinations.i[,1]),length(av_variables)))
  corr_combinations.new[,1:i] <- corr_combinations.i
  corr_combinations <- rbind(corr_combinations,corr_combinations.new)

#How many combinations for 0:2 variables?
comb_par_var<-choose(20, k=0:2)
##211

#A new column to recieve the values
corr_combinations$cor <- 0


  #Getting the correlations and assigning to the empty column
 for (i in (length(av_variables)+1):(length(av_variables)+ sum(comb_par_var) +1)){
  print(i/length(corr_combinations[,1]))
  corr_combinations$cor[i] <- max(as.dist(abs(cor(data.1[,as.character(corr_combinations[i,which(corr_combinations[i,]!=0&corr_combinations[i,]!=1)])]))))
  # combinations$cor[i] <- max(as.dist(abs(cor(data.0[,as.character(combinations[i,combinations[i,]!=0&combinations[i,]!=1])]))))
  }

#Keeping only the rows with the combinations of 2 variables
corr_combinations[1:(length(av_variables)+ sum(comb_par_var) +2),21]
corr_combinations<-corr_combinations[1:212,]
corr_combinations<-corr_combinations[21:210,]

#Keeping only the columns var1, var2 and cor
corr_combinations<-corr_combinations[,c(1,2,21)]

#Ordering to keep only the pairs with correlation >0.95, 
#which was my purpose the whole time
corr_combinations <- corr_combinations[order(corr_combinations$cor),]
corr_combinations<-corr_combinations[corr_combinations$cor >0.95, ] 
}

Answer 1

You can calculate the full correlation matrix in one go. Then you just need to reshape. An example,

cr <- cor(mtcars)
# This is to remove redundancy as upper correlation matrix == lower 
cr[upper.tri(cr, diag=TRUE)] <- NA
reshape2::melt(cr, na.rm=TRUE, value.name="cor")