Linear Regression and group by in R

Question

I want to do a linear regression in R using the lm() function. My data is an annual time series with one field for year (22 years) and another for state (50 states). I want to fit a regression for each state so that at the end I have a vector of lm responses. I can imagine doing for loop for each state then doing the regression inside the loop and adding the results of each regression to a vector. That does not seem very R-like, however. In SAS I would do a 'by' statement and in SQL I would do a 'group by'. What's the R way of doing this?

Answer 1

Here's an approach using the plyr package:

d <- data.frame(   state = rep(c('NY', 'CA'), 10),   year = rep(1:10, 2),   response= rnorm(20) )  library(plyr) # Break up d by state, then fit the specified model to each piece and # return a list models <- dlply(d, "state", function(df)    lm(response ~ year, data = df))  # Apply coef to each model and return a data frame ldply(models, coef)  # Print the summary of each model l_ply(models, summary, .print = TRUE)