A Fine Slice Of SQL Server
Before we start anything, I’d like to mention that most of the hard work came from nsaunders and his great blog post Idle thoughts lead to R internals: how to count function arguments.
Let’s get started.
The aim of this blog is to capture the number of arguments present in each function with packages of the tidyverse.
Click through to see the code, as well as some methods of visualizing the results (methods which you can use in other situations).
Comments closedKasper de Jonge shows how we can use Charticulator to build out custom visuals in Power BI:
Recently I attended an excellent session by Andy Kirk on the state of data visualization for 2019. One of the tools Andy is most excited about is called Charticulator. For those of you that haven’t heard about it, Charticulator is an open source project from Microsoft Research. Using a web UI you can design almost any charts by interactively specifying constraints (NO code).
The best news is that you can use these visuals directly in Power BI. You can even use your own data coming from the PBI data model. This came as news for many at Andy Kirk’s session so decided to do a quick post on it. Building very custom charts entices a lot of data artists but it is often hard to bring this to main stream BI product. The ease of the Charticulator and Power BI integration brings this type of data visualizations to a much broader audience.
This is a lot easier than telling people to learn D3, though Charticulator will necessarily have more limitations than writing all of the code yourself.
Comments closedHolger K. von Jouanne-Diedrich uses fractal math to create realistic-looking artificial islands:
Here we will turn this principle on its head and use it to actually create realistic-looking landmasses with R. The inspiration for this came from chapter 4 “Infinite Detail” of the book “Math Bytes” by my colleague Professor T. Chartier from Davidson College in North Carolina.
The idea is to start with some very simple form, like a square, and add more detail step-by-step. Concretely, we go through every midpoint of our ever more complex polygon and shift it by a random amount. Because the polygon will be getting more and more intricate we have to adjust the absolute amount by which we shift the respective midpoints.
Click through for the code.
Comments closedJon Fletcher has a good tip for snazzing up a Power BI dashboard:
First question, why bother with layouts?
Using layouts in Power BI allows a user to make their visuals stand out better, the page looks professional and more appealing to its audience.Second question, why PowerPoint?
The default page size in Power BI desktop is 16:9, (this trick doesn’t work for other Power BI page sizes), which is identical to a PowerPoint slide.
Therefore whatever is designed in PowerPoint will fit onto a Power BI page perfectly. Also PowerPoint is very easy to use; most people are familiar with it.
Click through for an example. It’s easy to go overboard with this, but Jon does a good job of using a muted color so that the edges don’t overwhelm your eyes. I might knock it down a shade or two further from that, but regardless, this is a nice tip.
Comments closedLaura Ellis shows how to use the usmap package in R:
Today, I’d like to share the package ‘usmap’ which enables incredibly easy and fast creation of US maps in R.
In honor of US Thanksgiving tomorrow, I’m going to make this blog Thanksgiving themed! In this tutorial, we will use the gTrendsR package to pull US Google search results on the keyword “thanksgiving” and plot the popularity by state.
Click through for that demo, as well as links to more demos on map usage.
Comments closedMihalis Tsoukalos extracts, explores, and visualizes data (with D3) from a Kafka topic:
Now that you have your data in JSON format, you will use D3.js in order to visualize it. As JavaScript code is embedded in HTML files, the final version of the D3.js code can be found in
visualize-spatial.html, which contains the following code:
D3 is extremely powerful, though that power comes with a fairly steep learning curve.
Comments closedSebastian Sauer has a two-parter on waffle plots. The first part is an introduction:
A waffle diagram is a variant of (stacked) bar plots or pie plots. They do not have great perceptual properties, I’d suspect, but for some purposes they may be adequate. This is best explored by example. This post draws heavily from the introduction of hrbrmstr to his Waffle package.
The second part uses emojifont to show pictograms as well:
A Pictogram may be defined as a (statistical) diagram using icons or similar “iconic” graphics to illstrate stuff. The waffle plot (see this post) is a nice object where to combine waffle and pictorgrams. Originally, this post was inspired by HRBRMSTR waffle package, see this post, but I could not get it running.
Maybe the easiest way is to work through an example (spoiler: see below for what we’re heading at).
This type of plot doesn’t work for everything, but I can think of a few places where it’d be the right choice.
Comments closedTomaz Kastrun takes us through a new visual in Power BI:
Decomposition tree is a data presentation of slicing and dicing of selected metrics based on the attributes of these metrics or with combination of other metrics. Another great aspect of this visual is to analyze the selected variable with many metrics or attributes (dimensions) as the same time.
It’s not the type of visual I’d want to see on a dashboard, but I can see it as quite useful in exploratory data analysis.
Comments closedAdam Saxton interviews Sarina Stevens on artificial intelligence capabilities in Power BI Desktop:
Power BI Desktop has new Artificial Intelligence (AI) capabilities in the November 2019 release. Sarina Stevens shows us what it is all about!
Click through for the video.
Comments closedCole Nussbaumer Knaflic takes a fairly simple table and creates eight visuals out of it:
STEP 1: When I encounter this table, I start reading and scanning down columns and across rows. In terms of specific observations, I might start by noticing that the majority of accounts are in Tiers B and C, while Tiers A and A+—though they don’t make up a huge number (or percentage) of accounts—do make up a meaningful amount of revenue. In terms of questions, I wonder if the tiers are in order: I would think A+ belongs above A and am confused that they don’t appear that way in the table (perhaps due to alphabetical sorting?).
This is a really nice practical exercise if you want to learn how to apply the right visuals to tell your story.
Comments closed