constructing variance-covariance matrix from pairwise correlation data in R

Question

I have all the pairwise correlations and would like to construct the var-covariance matrix in order to do some standard analysis on that matrix. Here's a sample data for the covariances, the first two columns are the "ids" while the third column shows the covariance between the "ids".

data<-data.frame("id1" = c("a","b","c","a","a","b"),
                 "id2" = c("a","b","c","b","c","c"),
                 "cov"=c(1,1,1,0.1,0.3,0.4))

Answer 1

Base-R solution:

nm <- unique(data$id1)   ## row/col names
v <- matrix(NA,length(nm),length(nm),dimnames=list(nm,nm))  ## set up template
v[cbind(data$id1,data$id2)] <- data$cov  ## fill in upper triangle
v[is.na(v)] <- t(v)[is.na(v)]            ## symmetrize

Answer 2

An easy dplyr solution is to make the data.frame wider with the help of pivot_wider , ie

data<-data.frame("id1" = c("a","b","c","a","a","b"),
                 "id2" = c("a","b","c","b","c","c"),
                 "cov"=c(1,1,1,0.1,0.3,0.4))

tidyr::pivot_wider(data, 
                   id_cols = c(cov, id2), 
                   names_from = id1, 
                   values_from = cov)

which yields the output

id2       a     b     c
      <fct> <dbl> <dbl> <dbl>
    1 a   1    NA      NA
    2 b   0.1   1      NA
    3 c   0.3   0.4     1

Since a covariance-matrix is symmetric, it's done.