This document summarizes Hadley Wickham's wrap up of the plyr package. It discusses using plyr functions like rlply and mlply to fit multiple models to data with varying parameters. It also covers reporting progress, handling errors, the overall structure of plyr functions, and plans for future improvements to work with larger data. Wickham invites feedback via a web form.

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