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- 1. plyr Wrap up Hadley Wickham Tuesday, 7 July 2009
- 2. 1. Fitting multiple models to the same data 2. Reporting progress & dealing with errors 3. Overall structure & correspondence to base R functions 4. Plans 5. Feedback Tuesday, 7 July 2009
- 3. Multiple models May need to ﬁt multiple models to the same data, with varying parameters or many random starts. Two plyr functions make this easy: rlply & mlply Example: ﬁtting a neural network Tuesday, 7 July 2009
- 4. 1.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.8 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● 0.6 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● class ● ● ● ● ● ● ● A y ● ● ● ● ●● ● ● ● ● ● ● ● ● ● B ● ● ● ● ● 0.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 ● 0.0 0.2 0.4 0.6 0.8 1.0 x Tuesday, 7 July 2009
- 5. library(nnet) library(ggplot2) w <- read.csv("wiggly.csv") qplot(x, y, data = w, colour = class) accuracy <- function(mod, true) { pred <- factor(predict(mod, type = "class"), levels = levels(true)) tb <- table(pred, true) sum(diag(tb)) / sum(tb) } nnet(class ~ x + y, data = w, size = 3) Tuesday, 7 July 2009
- 6. rlply A little different to the other plyr functions: ﬁrst argument is number of times to run, second argument is an expression (not a function). Automatically adds run number (.n) to labels. Tuesday, 7 July 2009
- 7. models <- rlply(50, nnet(class ~ x + y, data = w, size = 3, trace = FALSE)) accdf <- ldply(models, "accuracy", true = w$class) accdf qplot(accuracy, data = accdf, binwith = 0.02) Tuesday, 7 July 2009
- 8. mlply What if we want to systematically vary the input parameters? mlply allows us to vary all of the arguments to the applicator function, not just the ﬁrst argument Input is a data frame of parameter values Tuesday, 7 July 2009
- 9. wiggly_nnet <- function(...) { nnet(class ~ x + y, data = w, trace = FALSE, ...) } rlply(5, wiggly_nnet(size = 3)) # Unfortunately need 2+ parameters because of bug opts <- data.frame(size = 1:10, maxiter = 50) opts models <- mlply(opts, wiggly_nnet) ldply(models, "accuracy", true = w$class) # expand.grid() useful if you want to explore # all combinations Tuesday, 7 July 2009
- 10. Progress & errors Tuesday, 7 July 2009
- 11. Progress bars Things seem to take much less time when you are regularly updated on their progress. Plyr provides convenient method for doing this: all arguments accept .progress = "text" argument Tuesday, 7 July 2009
- 12. Error handling Helper function: failwith Takes a function as an input and returns a function as output, but instead of throwing an error it will return the value you specify. failwith(NULL, lm)(cty ~ displ, data = mpg) failwith(NULL, lm)(cty ~ displ, data = NULL) Tuesday, 7 July 2009
- 13. Overall structure Tuesday, 7 July 2009
- 14. array data frame list nothing array aaply adply alply a_ply data frame daply ddply dlply d_ply list laply ldply llply l_ply n replicates raply rdply rlply r_ply function maply mdply mlply m_ply arguments Tuesday, 7 July 2009
- 15. No output Useful for functions called purely for their side effects: write.table, save, graphics. If .print = TRUE will print each result (particularly useful lattice and ggplot2 graphics) Tuesday, 7 July 2009
- 16. Your turn With your partner, using your collective R knowledge, come up with all of the functions in base R (or contributed packages) that do the same thing. Tuesday, 7 July 2009
- 17. array data frame list nothing array apply adply alply a_ply data frame daply aggregate by d_ply list sapply ldply lapply l_ply n replicates replicate rdply replicate r_ply function mapply mdply mapply m_ply arguments Tuesday, 7 July 2009
- 18. Plans Deal better with large and larger data: trivial parallelisation & on-disk data (sql etc) Stay tuned for details. Tuesday, 7 July 2009
- 19. http://hadley.wufoo.com/ forms/course-evaluation/ Tuesday, 7 July 2009

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