R Plyr - Ordering results from DDPLY?

Does anyone know a slick way to order the results coming out of a ddply summarise operation?

This is what I'm doing to get the output ordered by descending depth.

  ddims <- ddply(diamonds, .(color), summarise, depth = mean(depth), table = mean(table))
  ddims <- ddims[order(-ddims$depth),]

With output...

> ddims
  color    depth    table
7     J 61.88722 57.81239
6     I 61.84639 57.57728
5     H 61.83685 57.51781
4     G 61.75711 57.28863
1     D 61.69813 57.40459
3     F 61.69458 57.43354
2     E 61.66209 57.49120

Not too ugly, but I'm hoping for a way do it nicely within ddply(). Anyone know how?

Hadley's ggplot2 book has this example for ddply an开发者_如何学JAVAd subset but it's not actually sorting the output, just selecting the two smallest diamonds per group.

ddply(diamonds, .(color), subset, order(carat) <= 2)

---

I'll use this occasion to advertise a bit for data.table, which is faster to run and (in my perception) at least as elegant to write:

library(data.table)
ddims <- data.table(diamonds)
system.time(ddims <- ddims[, list(depth=mean(depth), table=mean(table)), by=color][order(depth)])

   user  system elapsed 
  0.003   0.000   0.004 

By contrast, without ordering, your ddply code already takes 30 times longer:

  user  system elapsed 
 0.106   0.010   0.119

With all the respect I have for Hadley's excellent work, e.g. on ggplot2, and general awesomeness, I must confess that for me, data.table entirely replaced ddply -- for speed reasons.

---

Yes, to sort you can just nest the ddply in another ddply. Here's how you would use ddply to sort on one column, for example your table column:

ddimsSortedTable <- ddply(ddply(diamonds, .(color), 
  summarise, depth = mean(depth), table = mean(table)), .(table))

  color    depth    table
1     G 61.75711 57.28863
2     D 61.69813 57.40459
3     F 61.69458 57.43354
4     E 61.66209 57.49120
5     H 61.83685 57.51781
6     I 61.84639 57.57728
7     J 61.88722 57.81239

---

If you are using dplyr, I would recommend taking advantage of the %.% operator, which reads to more intuitive code.

data(diamonds, package = 'ggplot2')
library(dplyr)
diamonds %.%
  group_by(color) %.%
  summarise(
    depth = mean(depth),
    table = mean(table)
  ) %.%
  arrange(desc(depth))

---

A bit late to the party, but things might be a bit different with dplyr. Borrowing crayola's solution for data.table:

dat1 <- microbenchmark(
dtbl<- data.table(diamonds)[, list(depth=mean(depth), table=mean(table)), by=color][order(-   depth)],
dplyr_dtbl <- arrange(summarise(group_by(tbl_dt(diamonds),color), depth = mean(depth) , table =  mean(table)),-depth),
dplyr_dtfr <- arrange(summarise(group_by(tbl_df(diamonds),color), depth = mean(depth) , table = mean(table)),-depth),
times = 20, 
unit = "ms"
)

The results show that dplyr with tbl_dt is a bit slower than the data.table approach. However, dplyr with data.frame is faster:

         expr       min        lq    median        uq       max neval
      data.table  9.606571 10.968881 11.958644 12.675205 14.334525    20
dplyr_data.table 13.553307 15.721261 17.494500 19.544840 79.771768    20
dplyr_data.frame  4.643799  5.148327  5.887468  6.537321  7.043286    20

Note: I have obviously changed the names so the microbenchmark results are more readable