Revolution R and DPHS Cluster Statistical Computing 4914
- Slides: 40
Revolution R and DPHS Cluster Statistical Computing 4/9/14
Revolution R
What is it? Front End Back End
What is it?
Single Versus Multi Threading R uses a single thread Revolution R uses multiple threads
The Standard R Interface
Revolution R Environment Script(s) Console
Revolution R Environment Running R Script
Revolution R Environment Running Line or Section
Revolution R Environment Available Objects Installed/ Loaded Packages Object Details
Revolution R Environment Project Manager
Project Manager • Solutions o Corresponds to one R workspace/working directory o Storage for common files for projects • Project o Independent set of R scripts • Scripts and Documentation o Single Files that can be sourced, run, viewed, etc.
Scripts • R syntax checking and parenthesis highlighting • Intelli. Sense word completion o CTRL+Space • Setting Breakpoints o Breakpoints tell the R debugger where to stop execution, so that you can examine the state of the computation at that point. o Place the cursor anywhere on the desired line and press F 9. o Click the gray bar to the left of the desired line. o Repeat the procedure to delete the breakpoint. • Run all or part of a script in the command window • Set Bookmarks o Bookmarks make it easy to move from place to place within a large script, and also to move from script to script. You set bookmarks using any of the following methods: o Place the cursor anywhere on the desired line and press CTRL-K, CTRL-K. o Repeat the procedure to delete the bookmark. • Automatically comment out sections
Snippets • A predefined template for common R idioms o Insert snippet, then fill in the blanks o Right Click ->Insert Snippet • Code Snippet Manager o Create and Share your own snippets (Using XML and Visual Studio) o Allows you to automate programming frequent tasks o NOT the same as a SAS macro • Standardize analyses and enforce coding standards
Snippets in Revolution R
Snippets in Revolution R
Snippets in Revolution R
Snippets in Revolution R
Debugging • Revolution R has a debugging feature o Helpful to identify reasons code won’t run o Excellent way to check more complicated code • Complex analyses • Simulation Studies o Also extremely helpful if you are writing an R package
Debugging in Revolution R Debugging
Debugging in Revolution R Debugging
Available Objects • Inspect objects in current environment • List installed and loaded packages • Browse objects in packages • Inspect and edit data • Plot data objects
Other • Interactive Debug Feature o Debug vs. Release mode: control whether breakpoints are used o Step Execution
Shortcut Action Ctrl-A Select All Ctrl-B New Breakpoint Ctrl-C Copy Ctrl-F Find Ctrl-L Cut current line or selection to clipboard Ctrl-N New File Ctrl-O Open File Ctrl-P Print Ctrl+R, Ctrl+W - View white space Ctrl+R, Ctrl+S Run Selection Ctrl+R, Ctrl+C Run Current Script Ctrl-T Transpose characters Ctrl-U Changes selected text to lowercase Ctrl-V Paste Ctrl-W Selects the word containing the cursor or to the right of the cursor Ctrl-X Cut Ctrl-Y Redo Ctrl-Z Undo
DPHS Cluster This only works for Revolution R Enterprise
Accessing The Cluster PHSCLUSTER <- Rx. Hpc. Server( #Location of revolution R Enterprise on each node revo. Path="C: \Revolution\R-Enterprise-Node-6. 1\R 2. 14. 2\bin\x 64", #Location of big Data files on each node data. Path="c: \data", #User Directory for read/write share. Dir="\Clustershare\cne 2" , ) #Sets Compute Context to the Cluster rx. Options( compute. Context = PHSCLUSTER ) #Sets Compute Context to the Local Machine rx. Options( compute. Context = Rx. Local. Seq())
Accessing The Cluster PHSCLUSTER <- Rx. Hpc. Server( #Location of revolution R Enterprise on each node revo. Path="C: \Revolution\R-Enterprise-Node-6. 1\R 2. 14. 2\bin\x 64", #Location of big Data files on each node data. Path="c: \data", #User Directory for read/write share. Dir="\Clustershare\cne 2" , ) #Sets Compute Context to the Cluster rx. Options( compute. Context = PHSCLUSTER ) #Sets Compute Context to the Local Machine rx. Options( compute. Context = Rx. Local. Seq())
Accessing The Cluster PHSCLUSTER <- Rx. Hpc. Server( #Location of revolution R Enterprise on each node revo. Path="C: \Revolution\R-Enterprise-Node-6. 1\R 2. 14. 2\bin\x 64", #Location of big Data files on each node data. Path="c: \data", #User Directory for read/write share. Dir="\Clustershare\cne 2" , ) #Sets Compute Context to the Cluster rx. Options( compute. Context = PHSCLUSTER ) #Sets Compute Context to the Local Machine rx. Options( compute. Context = Rx. Local. Seq())
Accessing The Cluster PHSCLUSTER <- Rx. Hpc. Server( #Location of revolution R Enterprise on each node revo. Path="C: \Revolution\R-Enterprise-Node-6. 1\R 2. 14. 2\bin\x 64", #Location of big Data files on each node data. Path="c: \data", #User Directory for read/write share. Dir="\Clustershare\cne 2" , ) #Sets Compute Context to the Cluster rx. Options( compute. Context = PHSCLUSTER ) #Sets Compute Context to the Local Machine rx. Options( compute. Context = Rx. Local. Seq())
Accessing The Cluster PHSCLUSTER <- Rx. Hpc. Server( #Location of revolution R Enterprise on each node revo. Path="C: \Revolution\R-Enterprise-Node-6. 1\R 2. 14. 2\bin\x 64", #Location of big Data files on each node data. Path="c: \data", #User Directory for read/write share. Dir="\Clustershare\cne 2" , ) #Sets Compute Context to the Cluster rx. Options( compute. Context = PHSCLUSTER ) #Sets Compute Context to the Local Machine rx. Options( compute. Context = Rx. Local. Seq())
Accessing The Cluster PHSCLUSTER <- Rx. Hpc. Server( #Location of revolution R Enterprise on each node revo. Path="C: \Revolution\R-Enterprise-Node-6. 1\R 2. 14. 2\bin\x 64", #Location of big Data files on each node data. Path="c: \data", #User Directory for read/write share. Dir="\Clustershare\cne 2" , ) #Sets Compute Context to the Cluster rx. Options( compute. Context = PHSCLUSTER ) #Sets Compute Context to the Local Machine rx. Options( compute. Context = Rx. Local. Seq())
Accessing The Cluster PHSCLUSTER <- Rx. Hpc. Server( #Location of revolution R Enterprise on each node revo. Path="C: \Revolution\R-Enterprise-Node-6. 1\R 2. 14. 2\bin\x 64", #Location of big Data files on each node data. Path="c: \data", #User Directory for read/write share. Dir="\Clustershare\cne 2" , ) #Sets Compute Context to the Cluster rx. Options( compute. Context = PHSCLUSTER ) #Sets Compute Context to the Local Machine rx. Options( compute. Context = Rx. Local. Seq())
Setting a Progress Timer #Create a Progress Bar Object pb <- win. Progress. Bar(title = "progress bar", min = 0, max = total, width = 300) for(i in 1: total){ data 1<-rnorm(n=100, mean=0, sd=1) #Update the Progress Bar with the Current Unit set. Win. Progress. Bar(pb, i, title=paste( round(i/total*100, 0), "% done")) #Pause the system in order to update the progress bar (may not be necessary) #Sys. sleep(0. 1) } close(pb)
Other Features
Revo. Scale. R Package
Revo. Scale. R Package
Revo. Scale. R Package
Deploy. R
Deploy. R
- Dphs graduation requirements
- Cluster in parallel and distributed computing
- Distributed, parallel, and cluster computing
- Statistical computing environment
- Cluster computing architecture
- Cluster computing advantages
- Spark: cluster computing with working sets
- Scc bu
- Conclusion of cluster computing
- High performance computing linux
- Shared computing cluster
- Cluster computing architecture
- Bushared
- Bus access module for scc
- Conventional computing and intelligent computing
- Russian revolution vs french revolution
- Did american revolution cause french revolution
- Modern commercial agriculture
- Statistical symbols and meanings
- Chebyshev's inequality
- Probability and statistical inference 9th solution pdf
- Six sigma statistics cheat sheet
- Multiplexing and spreading
- Analogical evidence example
- Kruskal wallis spss
- Statistical infrequency strengths and weaknesses
- A visual aid used to show statistical trends and patterns
- A visual aid used to show statistical trends and patterns
- Statistical institute for asia and the pacific
- Statistical product and service solutions
- Statistical concepts and market returns
- Dulong petit law
- Thermodynamics and statistical mechanics
- Advanced and multivariate statistical methods
- Statistical product and service solutions
- Microstate and macrostate examples
- Volunteer sample vs convenience sample
- Cluster and stratified sampling
- Radar and sonar technician career cluster
- Multistage sampling example
- Cluster analysis: basic concepts and algorithms