I've been having the same problem with the pander package and managed a long-winded work around. I installed pander from Github to an earlier version of R (3.1.2 worked for me). I then copied across the package from library pander into my new R v3.2.0 installation. So far it seems to be working fine. I've had a go at doing this for the sparkr package, but unfortunately it requires a more.
![Connect Connect](http://www.informit.com/content/images/chap3_9780672338519/elementLinks/03fig01.jpg)
R has a simple and easy-to-use syntax and supports a huge library of capabilities; this makes it a top Data Science language. But, the biggest limitation of R is the amount of data it can process. Has fast parallel computing capabilities that can extend over hundreds of nodes. On the other, it is an easy-to-use program.Together R with Spark can provide a distributed DataFrame implementation that supports operations like selection, filtering, aggregation, etc.Readers who want to install R in their systems can follow our blog:In this blog post, we will learn how to integrate R with Spark. We will also insert a TABLE in Spark and perform filtration operations. Integrating R with SparkInstalling the Required Packages and Software.
install.packages(“sparklyr”)To check if the package has been installed correctly, we will load the Sparklyr package by using the following command:library(sparklyr)Install Spark Via R StudioTo install Spark, run the following command:sparkinstall(version = “1.6.2”)It may take some time to download the file and install it.Once done, the “Installation Complete” message will be shown. Refer to the screenshot below:To UpgradeTo upgrade to the latest version of Sparklyr, run the following command and restart your R session:install.packages(“devtools”)devtools::installgithub(“rstudio/sparklyr”)Note: It may take some time to download the dependency files and install them. Do not close the R studio. In my case, the complete Rtools was also installed.Reboot the Rconsole, and remember to save your work. Connecting to SparkSpark has both the local instance and remote engine. Here, we will connect to the local instance via the sparkconnect function.A remote dplyr data source is provided to the Spark cluster in return of the Spark connection.After sparklyr is installed, you may find a new tab for Spark. Inside of which, you will find a sub-tab for New Connection.
Now establish a new connection.Refer to the following image and keep the settings to whatever have been displayed below.Now click on Connect.This will initiate 3 commands in the R console and a connection will be established. Wait for a while for the connection to get established. Do not close your Rconsole.After the connection is successful, you will find that the new connection tab no more pops up. Refer to the screenshot below:TroubleshootWhen connecting, you might get an error: “Path Not Found.” This shows up because winutils.exe is not in place.
This is a file which will help Hadoop use the Windows platform to Run.You may find it present in an undesired location, i.e.C:UsersPrateekAppDataLocalrstudiosparkCachespark-1.6.2-bin-hadoop2.6tmphadoopbin.Simply copy the file from the temp directory into the bin. (See the path described below.)C:UsersPrateekAppDataLocalrstudiosparkCachespark-1.6.2-bin-hadoop2.6binwinutils.exeReboot your RStudio, and establish the connection again.Also, this may lead to some problems in some systems. To counter this, set the Java path from inside Rconsole.javapath% filter(depdelay 2)This is how we integrate R with Spark.Enroll for conducted by for a successful career.