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.
As you can see, we get the 4 columns of 2016, with a very small Q1, rising through until Q4 is by far the largest value.
This works exactly as I would expect it to, however when I expand down a level my results don’t show as I would expect.
Click through for Mike’s solution.
In this module you will learn how to use the Network Navigator Power BI Custom Visual. You may find the need to use the Network Navigator when you’re trying to find links between different attributes in a dataset. It does this by visualizing each attribute as a node and the strength of activity between those nodes can be represented in multiple ways.
Click through to get to Devin’s video. This visual looks interesting for graphical analysis, like trying to tease out common connections or discovering dependencies.
Here’s a brief explanation of what the query does:
First it reads the times from the Excel table and sets the Time column to be datetime data type
It then creates a new column called UTC and then takes the values in the Time column and converts them to datetimezone values, using the DateTime.AddZone() function to add a time zone offset of 0 hours, making them UTC times
Finally it creates a column called Local and converts the UTC times to my PC’s local time zone using the DateTimeZone.ToLocal() function
There are some limitations to what it does, so you can’t convert to just any time zone while still retaining Daylight Savings Time awareness.
In this module you will learn how to use the Attribute Slicer Power BI Custom Visual. Using the Attribute Slicer you have the ability to filter your entire report while also being able to visibly see a measure value associated with each attribute.
Click through for the video as well as more details. This looks like a very interesting way of integrating a slicer with some important metric, like maybe including dollar amounts per sales region and then filtering by specific regions to show more detailed analyses.
The question: “Why does it say ‘Consume live data sources with full interactivity’ as one feature while the other feature says ‘Access on-premise data using the Data Connectivity Gateways’, while it is obvious that if you need to connect to an on-premise data source to consume live data it has to be through a gateway?”
Okay, this is how I would explain this:
Read on for the explanation.
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.
In this module you will learn how to use the Gantt Power BI Custom Visual. Using the Gantt chart you can easily visualize project timelines and deliverable completion.
Gantt charts have a bad rep in IT mostly because GIGO applies to timelines too. But with that said, I think this is a nicely implemented visual.
Measure Step 2: Dealing with No (aka All) Selections on Slicer
The original measure above is really awkward when the user has made NO selection on a slicer – because it can then return a REALLY long list!
To deal with that case, we add an IF to the measure to detect precisely that case, and then return “All.”
Read on for the code, as well as some caveats and additional hints.
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.