Computing Statistics from Poorly Formatted Data (plyr and reshape packages for R)

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I was recently asked to verify the coefficients of a linear model fit to sets of data, where each row of the input file was a “site” and each column contained the dependent variable through time (i.e. column 1 = time step 1, column 2 = time step 2, etc.). This format is cumbersome in that it cannot be directly fed into the R lm() function for linear model fitting. Furthermore, we needed the output formatted with columns containing slope, intercept, and R-squared values for each site (rows). All of the re-formatting, and model fitting can be done by hand, using basic R functions, however this task seemed like a good case study for the use of the reshape and plyr packages for R. The reshape package can be used to convert between “wide” and “long” format– the first step in the example presented below. The plyr package can be used to split a data set into subsets (based on a grouping factor), apply an arbitrary function to the subset, and finally return the combined results in several possible formats. The original input data, desired output, and R code are listed below.

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