### Your choices

`order`

from `base`

`arrange`

from `dplyr`

`setorder`

and `setorderv`

from `data.table`

`arrange`

from `plyr`

`sort`

from `taRifx`

`orderBy`

from `doBy`

`sortData`

from `Deducer`

Most of the time you should use the `dplyr`

or `data.table`

solutions, unless having no-dependencies is important, in which case use `base::order`

.

I recently added sort.data.frame to a CRAN package, making it class compatible as discussed here: Best way to create generic/method consistency for sort.data.frame?

Therefore, given the data.frame dd, you can sort as follows:

`dd <- data.frame(b = factor(c("Hi", "Med", "Hi", "Low"), levels = c("Low", "Med", "Hi"), ordered = TRUE), x = c("A", "D", "A", "C"), y = c(8, 3, 9, 9), z = c(1, 1, 1, 2)) library(taRifx) sort(dd, f= ~ -z + b ) `

If you are one of the original authors of this function, please contact me. Discussion as to public domaininess is here: https://chat.stackoverflow.com/transcript/message/1094290#1094290

You can also use the `arrange()`

function from `plyr`

as Hadley pointed out in the above thread:

`library(plyr) arrange(dd,desc(z),b) `

Benchmarks: Note that I loaded each package in a new R session since there were a lot of conflicts. In particular loading the doBy package causes `sort`

to return "The following object(s) are masked from 'x (position 17)': b, x, y, z", and loading the Deducer package overwrites `sort.data.frame`

from Kevin Wright or the taRifx package.

`#Load each time dd <- data.frame(b = factor(c("Hi", "Med", "Hi", "Low"), levels = c("Low", "Med", "Hi"), ordered = TRUE), x = c("A", "D", "A", "C"), y = c(8, 3, 9, 9), z = c(1, 1, 1, 2)) library(microbenchmark) # Reload R between benchmarks microbenchmark(dd[with(dd, order(-z, b)), ] , dd[order(-dd$z, dd$b),], times=1000 ) `

Median times:

`dd[with(dd, order(-z, b)), ]`

**778**

`dd[order(-dd$z, dd$b),]`

**788**

`library(taRifx) microbenchmark(sort(dd, f= ~-z+b ),times=1000) `

Median time: **1,567**

`library(plyr) microbenchmark(arrange(dd,desc(z),b),times=1000) `

Median time: **862**

`library(doBy) microbenchmark(orderBy(~-z+b, data=dd),times=1000) `

Median time: **1,694**

Note that doBy takes a good bit of time to load the package.

`library(Deducer) microbenchmark(sortData(dd,c("z","b"),increasing= c(FALSE,TRUE)),times=1000) `

Couldn't make Deducer load. Needs JGR console.

`esort <- function(x, sortvar, ...) { attach(x) x <- x[with(x,order(sortvar,...)),] return(x) detach(x) } microbenchmark(esort(dd, -z, b),times=1000) `

Doesn't appear to be compatible with microbenchmark due to the attach/detach.

`m <- microbenchmark( arrange(dd,desc(z),b), sort(dd, f= ~-z+b ), dd[with(dd, order(-z, b)), ] , dd[order(-dd$z, dd$b),], times=1000 ) uq <- function(x) { fivenum(x)[4]} lq <- function(x) { fivenum(x)[2]} y_min <- 0 # min(by(m$time,m$expr,lq)) y_max <- max(by(m$time,m$expr,uq)) * 1.05 p <- ggplot(m,aes(x=expr,y=time)) + coord_cartesian(ylim = c( y_min , y_max )) p + stat_summary(fun.y=median,fun.ymin = lq, fun.ymax = uq, aes(fill=expr)) `

(lines extend from lower quartile to upper quartile, dot is the median)

Given these results and weighing simplicity vs. speed, I'd have to give the nod to `arrange`

in the `plyr`

package. It has a simple syntax and yet is almost as speedy as the base R commands with their convoluted machinations. Typically brilliant Hadley Wickham work. My only gripe with it is that it breaks the standard R nomenclature where sorting objects get called by `sort(object)`

, but I understand why Hadley did it that way due to issues discussed in the question linked above.