I wanted to execute R code from SSIS package. How can I add a data control step that executes R-code? SSIS supports only vb.net and asp.net.
SSIS has many data transformations available but R is very friendly when it comes to data manipulations.
I want to run a R-code from SSIS scripts or some other way.Basically, I'm trying to integrate R in ETL process.
I wanted to extract data(E) from from a CSV file.
Transform (T) it in R and load (L) it in Microsoft database.
Is it possible to get this workflow done in SSIS package by executing R-script using SSIS data control items? Thanks!
Here are a couple of ways you could integrate R into your ETL process.
Crude, fast and dirty - Execute Process Task in the Control Flow. This would be similar to calling RScript from the command line. You would likely make your transformation, save it to a file on disk, and get that filename from your Execute Process Task so you can feed it into a Data Flow task. Upside is you're keeping your R clean and separate from your C#/VB.
Integrated via Rdotnet - You could use the RDotNet library (I believe, haven't tried to integrate it). You would need to register the DLLs in the GAC, and then you can either work with .NET objects in your SSIS scripts or call R scripts directly.
Integrated in SQL Server 2016 - Microsoft has added R support via extended stored procedures. You call the R script via stored proc and use a sql query for input data and can store the output. See more detail here. This would mean utilizing an Execute SQL task in SSIS.
I hope it helps you or someone else, since you want data processing you might bring your dataset into a CSV file (throught a data flow task), execute the file using: "Rscript " (it might be executed as a command with the execute process task), inside the file you have to upload the dataset into a dataframe ( calling it with readLines() function), then do all the math/Calculation you request, write the data or calculation results into a CSV file an reading again it from SSIS.
It is not an elegant solution, but it works :), At least till microsoft integrates R as a control/data flow process.
CYA
PS. here you go how to execute files from the command line: Run R script from command line
Related
It is possible to run a R script with Pentaho, but instead of export the result as a csv file, insert the result directly into a table on a DB?
Using the Community Edition of Pentaho, you could use a script executor step to execute a shell script in your OS to do all the work, including inserting to the database, which is not much Pentaho related, all the work is done by the shell script and you just use Pentaho to call the execution of that script.
There's also a very old plugin available in Github that I don't know if it would work with modern versions of Pentaho and R, to execute R code within Pentaho and then continue the stream of data to "normal" steps like the table output to insert the data to a table.
These are the details to configure that plugin from the developers:
http://dekarlab.de/wp/?p=5
I am trying to use an R script as a data source for Power BI. I am a regular user of R but am new to Power BI. When all the datasets that are imported by the R script are from SQL databases I can import the resulting dataframes from the R script fine, however I have a script that uses a .csv file that Power BI's R session can't find which results in the error:
Error: 'times_of_day_grid.csv' does not exist in current working directory ('C:/Users/MyUserName/RScriptWrapper_ac2d4ec7-a4f6-4977-8713-10494f4b0c4f').
The .pbix file and the R script are both stored in the same folder as the csv
I have tried manually setting the wd by inserting into the script
setwd("C:/Users/MyUserName/Documents/R/Projects/This Project Folder")
But this just results in the message
"Connecting - Please wait while we establish a connection to R"
And later if I leave it running:
Unable to connect
We encountered an error while trying to connect.
Details: "ADO.NET: R execution timeout. The script execution was
terminated, since it was running for more than 1800000 miliseconds."
I have also tried specifying the full addresses of the csv files in read_csv(), but this results in the same timeout warning.
Any ideas as to how I can edit my script (or the settings in Power BI) to get around this? (The script only takes a minute or so to run in RStudio.)
Don't forget that you can load your csv file using the built-in functionalities in PowerBI Get Data > Text/CSV and then go to Edit Queries and handle the R scripting from there. That way you won't have to worry about setting the working directory in the R script at all.
You can even load multiple files and work on each and everyone of them using the approach described in Operations on multiple tables / datasets with Edit Queries and R in Power BI
Please let me know how this works out for you-
I have an R-script that does stuff with a bunch of tweets and I would like to use the same script on the same data but saved in an Hadoop file system. According to this Hortonworks tutorial I could use R code with data from my HDFS, but it is not quite clear.
Can I use the very same R-script, taking advantage of the mapreduce paradigm, by using this Revolution R? Should I change my code or is there a way to execute the same functions optimized for an Hadoop architecture?
My wish would be to write my code on a standard R IDE like R-Studio and then use it, or use the most of it, on my cloud services (such as Microsoft Azure) with mapreduce on the base.
Yes, you can run any R script across different data platform from Hadoop to Spark to Teradata and SQL Server by using environment specific compute context.
Following two links should help you get started on how to use Revolution R / Microsoft R Server on Hadoop:
https://msdn.microsoft.com/en-us/microsoft-r/scaler-hadoop-getting-started
https://github.com/Azure/Azure-MachineLearning-DataScience/blob/master/Misc/MicrosoftR/Samples/NYCTaxi/NYC2013_MRS_LinearBinary.Rmd
Recently tableau gave the functionality of R connection in their release 8.1. I want to know if there is any way i can call an entire table created in R to tableau. Or an .rds object which contains the dataset into Tableau?
There is a tutorial on the Tableau website for this and a blog on r-bloggers which discuss. The tutorial has a number of comments and one of them (in early Dec I think) asks how to get an rds file in. You need to start Rserve and then execute a script on it to get your data.
Sorry I can't be more help as I only looked into it briefly and put it on the back-burner but if you get stuck they seem to come back quickly if you post a comment on the page:
http://www.tableausoftware.com/about/blog/2013/10/tableau-81-and-r-25327
Just pointing out that the Tableau Data Extract API might be useful here, even if the current version of R integration doesn't yet meet your needs. (Note, that link is to the version 8.1 docs released in late 2013 - so look for the latest version to see what functionality they've added since)
If what you want to do is to manipulate data in R and then send a table of data to Tableau for visualization, you could first try the simple step of exporting the data from R as a CSV file and then visualizing that data in Tableau. I know that's not sexy, but its always good to make sure you've got a way to get the output result you need before investing time in optimizing the process.
If that gets the effect you want, but you just want to automate more of the steps, then take a look at the Tableau Data Extract API. You could use that library to generate a Tableau Data Extract instead of a CSV file. If you have something in production that needs updates, then you could presumably create a python script or JVM program to read your RDS file periodically and generate a revised extract.
Let us assume your data.frame/ tibble etc (say dataset object) is ready in R/ RStudio and you want to connect it with Tableau
1. In RStudio (or R terminal), execute the following steps:
install.packages("Rserve")
library(Rserve)
Rserve() ##This gets the R connection service up and running
2. Now go to Tableau (I am using 10.3.2):
Help > Settings and Performances > Manage External Service Connection
Enter localhost in the Server field and click on Test Connection.
You have now established a connection between R and Tableau.
3. Come back to RStudio. Now we need a .rdatafile that will consist of our R object(s). In this case, dataset. This is the R object that we want to use in Tableau. Enter this in the R console:
save(dataset, file="objectName.rdata")
4. Switch to Tableau now.
Connect To a File > Statistical File
Go to your working directory where the newly created objectName.rdata resides. From the drop down list of file type, select R files (*.rdata, *.rda) and select your object. This will open the object you created in R in Tableau. Alternatively, you can drag and drop your object directly to Tableau's workspace.
Is there a way to pass commands (from a shell) to an already running R-runtime/R-GUI, without copy and past.
So far I only know how to call R via shell with the -f or -e options, but in both cases a new R-Runtime will process the R-Script or R-Command I passed to it.
I rather would like to have an open R-Runtime waiting for commands passed to it via whatever connection is possible.
What you ask for cannot be done. R is single threaded and has a single REPL aka Read-eval-print loop which is, say, attached to a single input as e.g. the console in the GUI, or stdin if you pipe into R. But never two.
Unless you use something else as e.g. the most excellent Rserve which (when hosted on an OS other than Windoze) can handle multiple concurrent requests over tcp/ip. You may however have to write your custom connection. Examples for Java, C++ and R exist in the Rserve documentation.
You can use Rterm (under C:\Program Files\R\R-2.10.1\bin in Windows and R version 2.10.1). Or you can start R from the shell typing "R" (if the shell does not recognize the command you need to modify your path).
You could try simply saving the workspace from one session and manually loading it into the other one (or any kind of variation on this theme, like saving only the objects you share between the 2 sessions with saveRDS or similar). That would require some extra load and save commands but you could automatise this further by adding some lines in your .RProfile file that is executed at the beginning of every R session. Here is some more detailed information about R on startup. But I guess it all highly depends on what are you doing inside the R sessions. hth