
Hello R Users!
I have a data frame that has many variables, some with missing observations, and some that are correlated with each other. I would like to subset the data by dropping one of the variables that is correlated with another variable that I will keep int he data frame. Alternatively, I could also drop both the variables that are correlated with each other. Worry not! I am not deleting data, I am just finding a subset of the data that I can use to impute some missing observations. 
I have tried the following statement 
dfQuc <- dfQ[ , sapply(dfQ, function(x) cor(dfQ, use = "pairwise.complete.obs", method ="pearson")<0.8)]
but it gives me the following error:
Error in `[.data.frame`(dfQ, , sapply(dfQ, function(x) cor(dfQ, use = "pairwise.complete.obs",  : 
  undefined columns selected
Since I have several dozen data frames, it is impractical for me to manually inspect the correlation matrices and select which variables to drop, so I am trying to have R make the selection for me. Does any one have any idea on how to accomplish this? 
Thank you very much!
Rita ===================================== "If you think education is expensive, try ignorance."--Derek Bok


 		 	   		  
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