Linear Regression and group by in R

JD Long picture JD Long · Jul 23, 2009 · Viewed 80.9k times · Source

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

hadley picture hadley · Jul 31, 2009

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)