Hello, Here is a general question that I think many of you will have insight to.
I have 40 independent dataframes. Each has a dimension of 1359 x 15. For example >dim(meanAnnualWaterDepth) > 1539 15 I need to calculate the average value for each dataframe but only for columns 7: 15. I can do this with apply as follows: meanAnnualWaterDepth$Average <- apply(meanAnnualWaterDepth[7:15], 1, mean) So far so good. Next I need to generate a pairwise correlation matrix for each dataframe using the calculated average value . My approach has been to consider calculating the average value first and then creating a "list" using code similar to the following hydrometrics_list <- list(meanAnnualWaterDepth$Average, sevenDayWaterDepth$Average, discontinuousHydroperiod$Average) # abbreviated here, it will have 40 objects when done After this I need to run the pairwise correlations. So is it more efficient to build the list first and calculate the averages for each component of the list, or would it be more efficient to calculate the averages first and then generate the list followed by the pairwise correlations? Can anyone suggest code to accomplish this? I'm using R 2.10.1 on a windows machine. Thanks for the feedback, much appreciated Steve Steve Friedman Ph. D. Spatial Statistical Analyst Everglades and Dry Tortugas National Park 950 N Krome Ave (3rd Floor) Homestead, Florida 33034 steve_fried...@nps.gov Office (305) 224 - 4282 Fax (305) 224 - 4147 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
