Monthly Archives: April 2016

Using Data Frames in Feather format (Apache Arrow)

Triggered by the RStudio blog article about feather I did the one line install and compared the results on a data frame of 19 million rows. First results look indeed promising:

# build the package
> devtools::install_github("wesm/feather/R")

# load an existing data frame (19 million rows with batch job execution results)
> load("batch-12-2015.rda")

# write it in feather format...
> write_feather(dt,"batch-12-2015.feather")

# ... which is not compressed, hence larger on disk
> system("ls -lh batch-12-2015.*")
-rw-r--r-- 1 dirkd staff 813M 7 Apr 11:35 batch-12-2015.feather
-rw-r--r-- 1 dirkd staff 248M 27 Jan 22:42 batch-12-2015.rda

# a few repeat reads on an older macbook with sdd
> system.time(load("batch-12-2015.rda"))
user system elapsed
8.984 0.332 9.331
> system.time(dt1 <- read_feather("batch-12-2015.feather"))
user system elapsed
1.103 1.094 7.978
> system.time(load("batch-12-2015.rda"))
user system elapsed
9.045 0.352 9.418
> system.time(dt1 <- read_feather("batch-12-2015.feather"))
user system elapsed
1.110 0.658 3.997
> system.time(load("batch-12-2015.rda"))
user system elapsed
9.009 0.356 9.393
> system.time(dt1 <- read_feather("batch-12-2015.feather"))
user system elapsed
1.099 0.711 4.548

So, around half the elapsed time and about 1/10th of the user cpu time (uncompressed) ! Of course these measurements are from file system cache rather than the laptop SSD, but the reduction in wall time is nice for larger volume loads.

More important though is the cross-language support for R, Python, Scala/Spark and others, which could make feather the obvious exchange format within a team or between workflow steps that use different implementation languages.

Setting up an RStudio server for iPad access

Sometimes it can be convenient to run RStudio remotely from an iPad or another machine with little RAM or disk space. This can be done quite easily using the free RStudio Server on OSX via docker. To do this:

  • Find the rocker/rstudio image on docker hub and follow the setup steps here [github].
  • Once the image is running, you should be able to connect with Safari on the host Mac to the login page eg at
    $ open
  • Now there is is only a small last step needed. You need to expose the server port from the host on the local network using the OSX firewall. In the somewhat explicit language of the “new” OSX firewall this can be done using:

    $ echo "rdr pass inet proto tcp from any to any port 8787 -> port 8787" | sudo pfctl -ef -

    At this point you should be able to connect remotely from your iPad to


    and continue your R session where you left it before eg on your main machine.

    BTW: If your network can not be trusted then you should probably change the default login credentials as described in the image docs.