We’re going to be using the cars dataset that is built in R. To follow along with real code, here’s an interactive R Notebook. Feel free to copy it and play around with the code as you read along.
So if we were to simply plot the dataset using just the data as the only parameter, it’d look like this:plot(dataset)
The plot function is great for cases where you don’t much care how the visual looks, and the simplicity is great for throwaway visuals.
Leila Etaati has a three-part series on displaying R visuals in Power BI. Part 1 shows how to create a scatter plot:
so in the above picture we can see that we have 3 different fields that has been shown in the chart :highway and city speed in y and x axis. while the car’s cylinder varibale has been shown as different cycle size. However may be you need a bigger cycle to differentiate cylinder with 8 to 4 so we able to do that with add another layer by adding a function name
now I want to add other layer to this chart. by adding year and car drive option to the chart. To do that first choose year and drv from data field in power BI. As I have mentioned before, now the dataset variable will hold data about speed in city, speed in highway, number of cylinder, years of cars and type of drive.
I am going to use another function in the ggplot packages name “facet_grid” that helps me to show the different facet in my scatter chart. in this function, year and drv (driver) will be shown against each other.
Now I have to merg the data to get the location information from “sPDF” into “ddf”. To do that I am going to use” merge” function. As you can see in below code, first argument is our first dataset “ddf” and the second one is the data on Lat and Lon of location (sPDF). the third and forth columns show the main variables for joining these two dataset as “ddf” (x) is “country” and in the second one “sPDF” is “Admin”. the result will be stored in “df” dataset
Aside from my strong dislike of bar/pie charts on maps, this is good to know, particularly if there is not a built-in or customer Power BI visual to replicate something you can do in R.
Extensibility Log will store information about the session but it will not store the R or R environment information or data, just session information and data. Navigate to:
C:\Program Files\Microsoft SQL Server\MSSQL13.MSSQLSERVER\MSSQL\LOG\ExtensibilityLog
to check the content and to see, if there is anything useful for your needs.
It’s not a great answer today.
Microsoft R Open (MRO), Microsoft’s enhanced distribution of open source R, has been upgraded to version 3.3.3, and is now available for download for Windows, Mac, and Linux. This update upgrades the R language engine to R 3.3.3, upgrades the installer, and updates the bundled packages.
R 3.3.3 makes just a few minor fixes compared to R 3.3.2 (see the full list of changes here), so you shouldn’t encounter any compatibility issues when upgrading from MRO 3.3.2. For CRAN packages, MRO 3.3.3 points to CRAN snapshot taken on March 15, 2017 but as always, you can use the built-in checkpoint package to access packages from an earlier date (for compatibility) or a later date (to access new and updated packages).
Click through for more details. As a side note, CRAN R 3.4 is scheduled for release this month, so given their recent cadence, I’d guess MRO 3.4 to be out late this year.
Now we have a form that
lmcan work with. We just need to specify a set of inputs that are powers of
x(as in a traditional polynomial fit), and a set of inputs that are
ytimes powers of
x. This may seem like a strange thing to do, because we are making a model where we would need to know the value of
yin order to predict
y. But the trick here is that we will not try to use the fitted model to predict anything; we will just take the coefficients out and rearrange them in a function. The
fit_padefunction below takes a dataframe with
yvalues, fits an
lmmodel, and returns a function of
xthat uses the coefficents from the model to predict
The lm function does more than just fit straight lines.
In addition to having an SQL Server 2016 instance with R Server installed, the following components are needed on a client machine
This list is a change from the previous list I have provided as RTVS contains an installation of R Client, there is no need to download that as well. You do not need to download Microsoft R Open if you are using R Server either. Once RTVS is installed, there is a menu option on the R Tools window. Selecting Install R Client from the menu will handle the information. Unfortunately, there is no change to the menu option once R Client is installed, it always looks like you should install it. To find out if R Client has been installed, look in the Workspaces window.
In other words, fewer dependencies and an easier installation process. Read the whole thing to avoid RevoScaleR errors in your code post-upgrade.
If you’ve created an R function (say, a routine to clean up missing values in a data set, or a function to make forecasts using a machine learning model), and you want to make it easy for DBAs to use it, it’s now possible to publish R functions as a SQL Server 2016 stored procedure. The sqlrutils package provides tools to convert an existing R function to a stored procedure which can then be executed by anyone with authenticated access to the database — even if they don’t know any R.
To use an R function as a stored procedure, you’ll need SQL Server 2016 with R Services installed. You’ll also need to use the sqlrutils package to publish the function as a stored procedure: it’s included with both Microsoft R Client (available free) and Microsoft R Server (included with SQL Server 2016), version 9.0 or later.
Compare this against R Tools for Visual Studio, with which you can generate stored procedures from the IDE.
ggedit is an R package that is used to facilitate ggplot formatting. With ggedit, R users of all experience levels can easily move from creating ggplots to refining aesthetic details, all while maintaining portability for further reproducible research and collaboration.
ggedit is run from an R console or as a reactive object in any Shiny application. The user inputs a ggplot object or a list of objects. The application populates Bootstrap modals with all of the elements found in each layer, scale, and theme of the ggplot objects. The user can then edit these elements and interact with the plot as changes occur. During editing, a comparison of the script is logged, which can be directly copied and shared. The application output is a nested list containing the edited layers, scales, and themes in both object and script form, so you can apply the edited objects independent of the original plot using regular ggplot2 grammar.
This makes modifying ggplot2 visuals a lot easier for people who aren’t familiar with the concept of aesthetics and layers—like, say, the marketing team or management.
The tutorial covers many different techniques for training predictive models at scale, and deploying the trained models as predictive engines within production environments. Among the technologies you’ll use are Microsoft R Server running on Spark, the SparkR package, the sparklyr package and H20 (via the rsparkling package). It also touches on some non-Spark methods, like the bigmemory and ff packages for R (and various other packages that make use of them), and using the foreach package for coarse-grained parallel computations. You’ll also learn how to create prediction engines from these trained models using the mrsdeploy package.
Check out the post as well as the tutorial David links.
For users of the R language, scaling up their work to take advantage of cloud-based computing has generally been a complex undertaking. We are therefore excited to announce doAzureParallel, a lightweight R package built on Azure Batch that allows you to easily use Azure’s flexible compute resources right from your R session. The doAzureParallel package complements Microsoft R Server and provides the infrastructure you need to run massively parallel simulations on Azure directly from R.
The doAzureParallel package is a parallel backend for the popular foreach package, making it possible to execute multiple processes across a cluster of Azure virtual machines with just a few lines of R code. The package helps you create and manage the cluster in Azure, and register it as a parallel backend to be used with foreach.
It’s an interesting alternative to building beefy R servers.