Replacement for "rename" in dplyr

dplyr version 0.3 added a new rename() function that works just like plyr::rename().

df <- rename(df, new_name = old_name)

The next version of dplyr will support an improved version of select that also incorporates renaming:

> mtcars2 <- select( mtcars, disp2 = disp )
> head( mtcars2 )
                  disp2
Mazda RX4         160
Mazda RX4 Wag     160
Datsun 710        108
Hornet 4 Drive    258
Hornet Sportabout 360
Valiant           225
> changes( mtcars, mtcars2 )
Changed variables:
      old         new
disp  0x105500400
disp2             0x105500400

Changed attributes:
      old         new
names 0x106d2cf50 0x106d28a98

You can actually use plyr's rename function as part of dplyr chains. I think every function that a) takes a data.frame as the first argument and b) returns a data.frame works for chaining. Here is an example:

library('plyr')
library('dplyr')

DF = data.frame(var=1:5)

DF %>%
    # `rename` from `plyr`
    rename(c('var'='x')) %>%
    # `mutate` from `dplyr` (note order in which libraries are loaded)
    mutate(x.sq=x^2)

#   x x.sq
# 1 1    1
# 2 2    4
# 3 3    9
# 4 4   16
# 5 5   25

UPDATE: The current version of dplyr supports renaming directly as part of the select function (see Romain Francois post above). The general statement about using non-dplyr functions as part of dplyr chains is still valid though and rename is an interesting example.

It is not listed as a function in dplyr (yet): http://cran.rstudio.org/web/packages/dplyr/dplyr.pdf

The function below works (almost) the same if you don't want to load both plyr and dplyr

rename <- function(dat, oldnames, newnames) {
  datnames <- colnames(dat)
  datnames[which(datnames %in% oldnames)] <- newnames
  colnames(dat) <- datnames
  dat
}

dat <- rename(mtcars,c("mpg","cyl"), c("mympg","mycyl"))
head(dat)

                  mympg mycyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4          21.0     6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag      21.0     6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710         22.8     4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive     21.4     6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout  18.7     8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant            18.1     6  225 105 2.76 3.460 20.22  1  0    3    1

Edit: The comment by Romain produces the following (note that the changes function requires dplyr .1.1)

> dplyr:::changes(mtcars, dat)
Changed variables:
          old         new        
disp      0x108b4b0e0 0x108b4e370
hp        0x108b4b210 0x108b4e4a0
drat      0x108b4b340 0x108b4e5d0
wt        0x108b4b470 0x108b4e700
qsec      0x108b4b5a0 0x108b4e830
vs        0x108b4b6d0 0x108b4e960
am        0x108b4b800 0x108b4ea90
gear      0x108b4b930 0x108b4ebc0
carb      0x108b4ba60 0x108b4ecf0
mpg       0x1033ee7c0            
cyl       0x10331d3d0            
mympg                 0x108b4e110
mycyl                 0x108b4e240

Changed attributes:
          old         new        
names     0x10c100558 0x10c2ea3f0
row.names 0x108b4bb90 0x108b4ee20
class     0x103bd8988 0x103bd8f58

While not exactly renaming, dplyr::select_all() can be used to reformat column names. This example replaces spaces and periods with an underscore and converts everything to lower case:

iris %>%  
  select_all(~gsub("\\s+|\\.", "_", .)) %>% 
  select_all(tolower) %>% 
  head(2)
  sepal_length sepal_width petal_length petal_width species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa