A Fine Slice Of SQL Server
Devin Knight shows off a Power BI custom visual which displays ratings with stars:
The Stars visual has the ability to use symbols instead of the star.
If you have multiple rows in your dataset then you may need to use a Slicer to toggle back and forth between each record.
I haven’t used the stars visual, but it seems that it’d make intuitive sense, given how many major sites use stars for ratings.
Comments closedSalvino Salvaggio uses R to analyze The Ramones:
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.
Comments closedReza Rad shows how to generate a shape map in Power BI:
I have previously written some blog posts about Map visuals in Power BI. One of them was specifically about Filled Map, titled as Filled Map; the Good, the Bad, the Ugly! Why? you need to read that post to find the reason. In this post I want to explain the power of Shape Map which is one of the visuals Power BI team published recently. This visual is still at preview mode at the time of writing this post. This visual is much more powerful than what it looks. The actual power behind it is that you can have your own map added to it. Let’s take a closer look at this visual with an example. If you want to learn more about Power BI; read Power BI from Rookie to Rock Star.
It’s an interesting look at a new visual.
Comments closedPeter Prevos wants to create a pretty map of flights he’s taken:
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.
Comments closedDevin Knight continues his Power BI custom visuals series with the image viewer:
In this module you will learn how to use the Image Viewer Power BI Custom Visual. The Image Viewer visual helps in displaying images based on an image URL stored in your data.
This is an interesting visual.
Comments closedGogula Aralingam notes that Power BI can now support basic skinning:
The March 2017 update of Power BI Desktop comes with a preview of Themes. Right now it is in its simplest of forms: You manually create a JSON file that has a very few attributes that can set basic color themes to your reports. So all you have to do is create file that looks like this:
Click through for an example. This isn’t a true fix for the lack of Color Vision Deficiency support, but you can plug in safe colors (for example, this article includes some) and skirt the issue until there’s real support.
Comments closedDevin Knight continues his Power BI custom visuals series:
In this module you will learn how to use the Long Text Viewer Power BI Custom Visual. The Long Text Viewer visual helps in displaying longer text fields that you have in your dataset.
I am happy that this exists, but not sure how frequently I would use it.
Comments closedDavid Smith has a post on a new R package to display graphs:
A graph, a collection of nodes connected by edges, is just data. Whether it’s a social network (where nodes are people, and edges are friend relationships), or a decision tree (where nodes are branch criteria or values, and edges decisions), the nature of the graph is easily represented in a data object. It might be represented as a matrix (where rows and columns are nodes, and elements mark whether an edge between them is present) or as a data frame (where each row is an edge, with columns representing the pair of connected nodes).
The trick comes in how you represent a graph visually; there are many different options each with strengths and weaknesses when it comes to interpretation. A graph with many nodes and edges may become an unintelligible hairball without careful arrangement, and including directionality or other attributes of edges or nodes can reveal insights about the data that wouldn’t be apparent otherwise. There are many R packages for creating and displaying graphs (igraph is a popular one, and this CRAN task view lists many others) but that’s a problem in its own right: an important part of the data exploration process is trying and comparing different visualization options, and the myriad packages and interfaces makes that process difficult for graph data.
Click through for more information as well as a mesmerizing animated image.
Comments closedAki Ariga continues his sparklyr series with some analysis of US flight data:
In this post, we will show you a visualization and build a predictive model of US flights with sparklyr. Flight visualization code is based on this article.
This post assumes you already have the following tables:
- Airlines data as
airlines_bi_pq. It is assumed to be on S3, but you can put it into HDFS. See also the Ibis project.- Airports data converted into Parquet format as
airports_new_pq. See also 2009 ASA Data Expo.You should make these tables available through Apache Hive or Apache Impala (incubating) with Hue.
There’s some setup work to get this going, but getting a handle on sparklyr looks to be a good idea if you’re in the analytics space.
Comments closedThe folks at Sharp Sight Labs show how to create high-quality map visuals in R:
Maps are great for practicing data visualization. First of all, there’s a lot of data available on places like Wikipedia that you can map.
Moreover, creating maps typically requires several essential skills in combination. Specifically, you commonly need to be able to retrieve the data (e.g., scrape it), mold it into shape, perform a join, and visualize it. Because creating maps requires several skills from data manipulation and data visualization, creating them will be great practice for you.
And if that’s not enough, a good map just looks great. They’re visually compelling.
With that in mind, I want to walk you through the logic of building one step by step.
Read on for a step by step process.
Comments closed