A Fine Slice Of SQL Server
John Yagecic has a Shiny app which gives a Monte Carlo analysis of Manning’s Equation:
Monte Carlo analysis is a great way to explore the impact of input variable uncertainty on the results of engineering equations, and with vector variables and distribution and sampling functions at its core, R is a natural platform for this analysis.
Check out his app, which has a link to the code. Amazingly, this is only 107 lines of code.
Comments closedDevin Knight has part nine of his custom visualization series:
In this module you will learn how to use the Chord Power BI Custom Visual. Chord diagrams show directed relationships among a group of entities using colored lines (chords); this allows for an easy representation of correlating data.
Chord diagrams, when done right, can be extremely informative. The problem is that they’re also really confusing to the uninitiated.
Comments closedVik Paruchuri walks through exploratory data analysis using New York City schools data:
Heatmaps are good for mapping out gradients, but we’ll want something with more structure to plot out differences in SAT score across the city. School districts are a good way to visualize this information, as each district has its own administration. New York City has several dozen school districts, and each district is a small geographic area.
We can compute SAT score by school district, then plot this out on a map. In the below code, we’ll:
Group full by school district.
Compute the average of each column for each school district.
Convert the school_dist field to remove leading 0s, so we can match our geograpghic district data.
Also check out part 1 if you missed it.
Comments closedThe USGS has a website called EarthExplorer that lets you search through decades of satellite data. I limited my search to Landsat 8 data with less than 10% cloud cover.
(you can do this search on the command line with landsat-util too but i prefer the web interface. In the future I will probably use this online toolfrom Development Seed)
Hat tip to Nathan Yau.
Comments closedDevin Knight continues his custom visualization series:
In this module you will learn how to use the Dot Plot Power BI Custom Visual. The Dot Plot is often used when visualizing a distribution of values or a count of an occurrence across different categorical data you may have. Watch this module to learn more!
This particular visualization seems a bit distracting for my tastes, but check out Devin’s video.
Comments closedDevin Knight continues his Power BI custom visuals class:
The Enhanced Scatter functions very similarly to the standard Power BI scatter chart but with a few new properties added to it including:
Shapes as markers
Background image support
Crosshair interaction
I’ve enjoyed going through this series and getting a chance to dig into custom visuals others have created.
Comments closedJos Dirksen uses Spark and D3 to count and graph swear words in movies:
So how do we do this? Well, the first thing to do is get the number of swearwords per minute. I mentioned that for the original article someone just counted every swearwords, in our case, we’re just going to parse a subtitle file, and extract the swear words from that.
Without going into too much detail, you can find the code I’ve experimtend with in this gist (it’s very ugly code, since I just hacked something together that worked).
Jos includes counts for four movies. This link does contain a few bad words, but if you get past that, it’s a good pattern for analyzing word counts in general.
Comments closedDevin Knight continues his Power BI visualization course:
In this module you will learn how to use the Histogram, a Power BI Custom Visual. A Histogram is a column chart which shows the distribution of occurrences divided into categories, called bins. This type of chart is useful for estimating density and discovering outliers.
Another fine entry in a great series. Check it out.
Comments closedRiddhiman shows how to use R and plotly to build charts of Federal Reserve data sets:
In this post we’ll try to replicate some of the charts created by the Federal Reserve which visualize some well known macroeconomic indicators. We’ll also showcase the new Plotly 4.0 syntax.
This is a very code-heavy blog post and is a good way to learn about plotly.
Comments closedDevin Knight’s Power BI Custom Visuals class continues:
Change the Banding type property to one of the following:
Increasing is better – Increasing is best when you’re measuring things like sales or profit. If you go over your profit target that’s a good thing!
Decreasing is better – Decreasing is probably best when you’re looking at something like budgeting. Staying under budget is usually a good thing. Unless you being too far under budget means you won’t get that money again next year which leads to the last option
Closer is better – This is for when you need your data to land in the middle of a bell curve. Meaning if you go too high or too low that’s a bad thing. This is often useful when looking at medical data. For example, if your blood pressure is too high then that’s a bad thing, but if you’re blood pressure is too low that’s also a bad thing too. You need to land in the middle somewhere, which is what this option allows.
There’s plenty of good advice here, so check out the video.
Comments closed