How to melt and cast dataframes using dplyr?

Question

Recently I am doing all my data manipulations using dplyr and it is an excellent tool for that. However I am unable to melt or cast a data frame using dplyr. Is there any way to do that? Right now I am using reshape2 for this purpose.

I want 'dplyr' solution for:

require(reshape2) data(iris) dat <- melt(iris,id.vars="Species") 

Answer 1

The successor to reshape2 is tidyr. The equivalent of melt() and dcast() are gather() and spread() respectively. The equivalent to your code would then be

library(tidyr) data(iris) dat <- gather(iris, variable, value, -Species) 

If you have magrittr imported you can use the pipe operator like in dplyr, i.e. write

dat <- iris %>% gather(variable, value, -Species) 

Note that you need to specify the variable and value names explicitly, unlike in melt(). I find the syntax of gather() quite convenient, because you can just specify the columns you want to be converted to long format, or specify the ones you want to remain in the new data frame by prefixing them with '-' (just like for Species above), which is a bit faster to type than in melt(). However, I've noticed that on my machine at least, tidyr can be noticeably slower than reshape2.

Edit In reply to @hadley 's comment below, I'm posting some timing info comparing the two functions on my PC.

library(microbenchmark) microbenchmark(     melt = melt(iris,id.vars="Species"),      gather = gather(iris, variable, value, -Species) ) # Unit: microseconds #    expr     min       lq  median       uq      max neval #    melt 278.829 290.7420 295.797 320.5730  389.626   100 #  gather 536.974 552.2515 567.395 683.2515 1488.229   100  set.seed(1) iris1 <- iris[sample(1:nrow(iris), 1e6, replace = T), ]  system.time(melt(iris1,id.vars="Species")) #    user  system elapsed  #   0.012   0.024   0.036  system.time(gather(iris1, variable, value, -Species)) #    user  system elapsed  #   0.364   0.024   0.387   sessionInfo() # R version 3.1.1 (2014-07-10) # Platform: x86_64-pc-linux-gnu (64-bit) #  # locale: #  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C               #  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8     #  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8    #  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                  #  [9] LC_ADDRESS=C               LC_TELEPHONE=C             # [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C         # attached base packages: # [1] stats     graphics  grDevices utils     datasets  methods   base      #  # other attached packages: # [1] reshape2_1.4         microbenchmark_1.3-0 magrittr_1.0.1       # [4] tidyr_0.1            #  # loaded via a namespace (and not attached): # [1] assertthat_0.1 dplyr_0.2      parallel_3.1.1 plyr_1.8.1     Rcpp_0.11.2    # [6] stringr_0.6.2  tools_3.1.1    

Answer 2

In addition, cast can be using tidyr::spread()

Example for you

library(reshape2) library(tidyr) library(dplyr)  # example data : `mini_iris` (mini_iris <- iris[c(1, 51, 101), ])  # melt (melted1 <- mini_iris %>% melt(id.vars = "Species"))         # on reshape2 (melted2 <- mini_iris %>% gather(variable, value, -Species)) # on tidyr  # cast melted1 %>% dcast(Species ~ variable, value.var = "value") # on reshape2 melted2 %>% spread(variable, value)                        # on tidyr