If you look at the code from the interactive window, you will notice that the error occurred with trying to run rxSummary. In both cases I didn’t get the error when I changed the compute context to SQL Server from local, but when I tried to run a function which runs on the server. In both cases the R tools where installed prior to installing SQL Server 2016. The Open Source R tools install to C:\Program Files\R\R-3.3.0 (your version number may be higher). The Microsoft R Open installs to C:\Program Files\Microsoft\MRO\R-3.2.5. To use the libraries needed for the RevoScaleR libraries included in R Server, the version of Microsoft R required is Microsoft RRE, which is installed here C:\Program Files\Microsoft\MRO-for-RRE\8.0. Unfortunately, SQL Server 2016 shipped with version 8.0.3 not 8.0.0. If you are getting data and using a local compute context, you will have no problems. However, when you want to change your compute context to run on SQL Server, you will get an error.
While I received a different error on the server than my laptop, the reason for both messages was the same. Neither computer was running version 22.214.171.124 of the R client tools. On the server I was able to fix the error without downloading a thing. After installing a stand-alone version of R Server from the SQL Server Installation Center, the error went away and I got results when trying to run rxSummary. Unfortunately, it was not possible for me to run R Server on my laptop, as R Server is disabled from within the Installation Center. I believe that is because I have SQL Server 2016 developer edition on a laptop, not on a server. I needed to do something else to make it work.
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Microsoft has not one version of R, they have two but two. These two different versions are needed because they have two different purposes in mind. Microsoft R Open, is open source and fully R compatible and is faster than open source R because they rewrote a number of the algorithms to include multi-threaded math libraries. If you want to run R code on SQL Server, this is the not the version you want to use. You want to use the non-open source version designed to run on R Server, which is included with SQL Server 2016, Microsoft RRE Open. This version will run R code not only in memory but swap to disk, to create code which can access SQL Server data without needing to create a file, and can run code on the server from the client. The version of RRE Open which is included in SQL Server 2016 is 8.0.3.
She follows this up with a demo program to pull data from a SQL Server table and generate a histogram. If you have zero R experience, there’s no time like the present to get started.
If you already have R installed on the same system as PowerBI, you just need to paste the R scripts in the code pen. Otherwise you need to install R in the system where you are using the PowerBI desktop like this:
This step-by-step guide features a lot of images and should be pretty easy for a new user.
HIBPwned is a feature complete R package that allows you to use every (currently) available endpoint of the API. It’s vectorised so no need to loop through email addresses, and it requires no fiddling with authentication or keys.
You can use HIBPwned to do things like:
Set up your own notification system for account breaches of myriad email addresses & user names that you have
Check for compromised company email accounts from within your company Active Directory
Analyse past data breaches and produce charts like Dave McCandless’ Breach chart
The regular service is extremely useful and Steph’s wrapper looks like it’s worth checking out.
In previous videos you’ve learned that we can demonstrate R visualization in Power BI, In this video you will learn how R visualization is working interactively with other elements in Power BI report. In fact Power BI works with R charts as a regular visualization and highlighting and selecting items in other elements of report will effect on that. Here is a quick video about this functionality
Check out the five-minute video.
Dimensionality reduction is a common techique to visualize observations in a dataset, by combining all features into two, that can then be used to draw the observation in an scatter plot.
One popular algorithm that implements this technique is PCA (Principal Components Analysis), which is available in R through the prcomp() function.
The algorithm was applied to observations of sthe dataset, and ggplot2’s geom_point() function was used to draw the results in a 2D chart.
I would want to see this done for a couple hundred thousand domains, but I do like the idea of taking advantage of statistical modeling tools to find security threats.
If missing values are something which haunts you then
MICEpackage is the real friend of yours.
When we face an issue of missing values we generally go ahead with basic imputations such as replacing with 0, replacing with mean, replacing with mode etc. but each of these methods are not versatile and could result into a possible data discrepancy.
MICEpackage helps you to impute missing values by using multiple techniques, depending on the kind of data you are working with.
I’d heard of a couple of these, but most of them are new to me.
By analyzing the plot above, we can arrive at the following insights:
The number of crimes steadily decline from midnight and are at the lowest during the early morning hours and then they start increasing and peak around 6 PM in the evening. This is the same insight we arrived in my previous analysis but here we have categorized by the Police district and still see the same pattern.
As seen in the previous plot, Park and Richmond districts have the lowest number of crimes throughout the day.
As highlighted in red in the plot above, the maximum number of crimes happens in Southern district around 6 PM in the evening.
I would prefer to see code here, but it does serve to give you an idea of what R can do.
As more settlements in Texas and France are impacted by severe flooding, this is a good time to thank the hydrologists at the NOAA who forecast river level rises in advance and give residents in affected areas time to move to higher ground. Along with topgraphic, rainfall, and weather data, monitoring stations maintained by NOAA and the USGS along rivers provide critical real-time information about river levels. NOAA scientists access this data using the dataRetrieval package for R, which they then incorporate into flood prediction models and use to generate animations like this one of the flood of the Delaware in February this year
Looks like I’ve got a new blog to follow…
There are probably very few cases for which this is technically a good idea (trying to be a featured author on JunkCharts might very well be one of those reasons). Nonetheless, there are at least a couple of requests for this floating around on stackoverflow; here and here for example. I struggled to find any satisfactory solutions that were in current working order (though perhaps my Google-fu has failed me).
Jonathan is rather against this idea, and it does seem like the answer is a hack. I suppose the real answer is “sometimes an image isn’t worth a thousand words.”