By clicking on the “R transformation” a new windows will show up. This windows is a R editor that you can past your code here. however there are couple of things that you should consider.
1. there is a error message handling but always recommended to run and be sure your code work in R studio first (in our example we already tested it in Part 1).
2. the all data is holding in variable “dataset”.
3. you do not need to write “install.packages” to get packages here, but you should first install required packages into your R editor and here just call “library(package name)”
Leila takes this step-by-step, leading to a Power BI visual with drill-down.
Next, you’ll practice interactively querying Athena from R for analytics and visualization. For this purpose, you’ll use GDELT, a publicly available dataset hosted on S3.
Create a table in Athena from R using the GDELT dataset. This step can also be performed from the AWS management console as illustrated in the blog post “Amazon Athena – Interactive SQL Queries for Data in Amazon S3.”
This is an interesting use case for Athena.
I was invited to deliver a session for Belgium User Group on SQL Server and R integration. After the session – which we did online using web based Citrix – I got an interesting question: “Is it possible to use RevoScaleR performance computational functions within Power BI?“. My first answer was, a sceptical yes. But I said, that I haven’t used it in this manner yet and that there might be some limitations.
The idea of having the scalable environment and the parallel computational package with all the predictive analytical functions in Power BI is absolutely great. But something tells me, that it will not be that straight forward.
Read on for the rest of the story.
With importing package tools, we get many useful functions to find additional information on packages.
Function package.dependencies() parses and check dependencies of a package in current environment. Function package_dependencies() (with underscore and not dot) will find all dependent and reverse dependent packages.
This probably tilts more toward “fun” than “practical,” but this will let you see the full set of dependencies for a package if, for example, you need to grab all of these packages for upgrading an offline instance.
In R, you can train a simple neural network with just a single hidden layer with the nnet package, which comes pre-installed with every R distribution. It’s a great place to start if you’re new to neural networks, but the deep learning applications call for more complex neural networks. R has several packages to check out here, including MXNet, darch, deepnet, and h2o: see this post for a comparison. The tensorflow package can also be used to implement various kinds of neural networks.
R makes it pretty easy to run one, though it then becomes important to understand regularization as a part of model tuning.
Musical purists always reproached the Ramones for knowing a couple of chords only and making an excessive use of them. Data show that the band knew at least… 11 different chords (out of too-many-to-bother-counting possibilities) although 80% of their songs were built on no more than 6. And there is no evidence of a sophistication of the Ramones’ compositions over time.
It’s a fun analysis with all the R code attached. This fun analysis, however, includes n-gram analysis, sentiment analysis, and token distribution analysis.
The first step was to create a list of all the places I have flown between at least once. Paging through my travel photos and diaries, I managed to create a pretty complete list. The structure of this document is simply a list of all routes (From, To) and every flight only gets counted once. The next step finds the spatial coordinates for each airport by searching Google Maps using the geocode function from the ggmap package. In some instances, I had to add the country name to avoid confusion between places.
The end result is imperfect (as Peter mentions, ggmap isn’t wrapping around), but does fit the bill for being eye-catching.
Suppose we had a large data set hosted on a
Sparkcluster that we wished to work with using
sparklyr(for this article we will simulate such using data loaded into
We will work a trivial example: taking a quick peek at your data. The analyst should always be able to and willing to look at the data.
It is easy to look at the top of the data, or any specific set of rows of the data.
Read on for more details.
The R core group announced today the release of R 3.3.3 (code-name: “Another Canoe”). As the wrap-up release of the R 3.3 series, this update mainly contains minor bug-fixes. (Bigger changes are planned for R 3.4.0, expected in mid-April.) Binaries for the Windows version are already up on the CRAN master site, and binaries for all platforms will appear on your local CRAN mirror within the next couple of days.
For now, I’m holding out until R 3.4.0.