A Fine Slice Of SQL Server
Kasper de Jonge shows us different ways of sorting a visual by some unrelated measure:
So lets start with the simple one, I want to sort a chart on a measure not part of the visual. Let’s take this visual:
Now instead of sorting by OrderQuantity I want to sort by the ListPrice. The trick here is to make the measure part of the query, and one way you can do that is by adding it to the tooltip
Read on for examples for charts as well as matrices.
Comments closedSebastian Sauer shows us how to build a gantt chart in R:
Of importance are only Task, Previous_Evnet and Duration. In addition, we need an overall start date (“2019-03-01” in this case). Each subsequent task is assumed to follow neatly its predecessing event.
Our job is to compute the start date and end date of task given that we know the initial start date and the durations. As said, this procedure is based on the assumption that there is a frictionless and gapless sequence of tasks.
Read on for a code-heavy example. I’ve always had a soft spot in my heart for gantt charts.
Comments closedDavid Smith is following the kerfuffle that Edward Tufte unleashed on Twitter recently:
While graphics guru Edward Tufte recently claimed that “R coders and users just can’t do words on graphics and typography” and need additonal tools to make graphics that aren’t “clunky”, data journalists at major publications beg to differ. The BBC has been creating graphics “purely in R” for some time, with a typography style matching that of the BBC website. Senior BBC Data Journalist Christine Jeavans offers several examples, including this chart of life expectancy differences between men and women:
I think Tufte’s off base here.
Comments closedThe Plotly folks show off scatterplot matrices in Python:
The scatterplot matrix, known acronymically as SPLOM, is a relatively uncommon graphical tool that uses multiple scatterplots to determine the correlation (if any) between a series of variables.
These scatterplots are then organized into a matrix, making it easy to look at all the potential correlations in one place.
SPLOMs, invented by John Hartigan in 1975, allow data aficionados to quickly realize any interesting correlations between parameters in the data set.
In this post, we’ll go over how to make SPLOMs in Plotly with Python. For extra insights, check out our SPLOM tutorial in Python and R.
fff
Comments closedStephanie Evergreen really cuts to the chase:
Here’s the hard truth: Your report probably sucks. Mine sure did. The heart of your content is likely fine, maybe even helpful. But, if you are anything like the hundreds of reports I see every year, the entire set of cultural norms we have somehow developed around reporting is just setting us up for failure, writing a destiny where no one is reading the report.
Why? Let me lay out the most common issues I see and propose some strategic solutions.
There’s an emphasis here on academic papers but it also applies to corporate work too.
Comments closedAbdul Majed Raja shows us how to take screenshots of webpages using R:
webshot package provides one simple function webshot() that takes a webpage url as its first argument and saves it in the given file name that is its second argument. It is important to note that the filename includes the file extensions like ‘.jpg’, ‘.png’, ‘.pdf’ based on which the output file is rendered. Below is the basic structure of how the function goes:
library(webshot)
#webshot(url, filename.extension)
webshot(“https://www.listendata.com/”, “listendata.png”)
If no folder path is specified along with the filename, the file is downloaded in the current working directory which can be checked with getwd().
Now that we understood the basics of the webshot() function, It is time for us to begin with our cases – starting with downloading/converting a webpage as a PDFcopy.
This isn’t something I’d expect to do every day, but I could see it being useful as part of a notebook to give the user a sanity check, like if a webpage or data set has a last updated timestamp that you want to check. H/T R-Bloggers
Comments closedJohn Mount announces WVPlots 1.0.0:
Nina Zumel and I have been working on packaging our favorite graphing techniques in a more reusable way that emphasizes the analysis task at hand over the steps needed to produce a good visualization. We are excited to announce the WVPlots is now at version 1.0.0 on CRAN!
The idea is: we sacrifice some of the flexibility and composability inherent to ggplot2 in R for a menu of prescribed presentation solutions. This is a package to produce plots while you are in the middle of another task.
I like this idea: I know the kind of plot I need and just want to throw something together for myself to give me an idea of the underlying data.
Comments closedMeagan Longoria helps us choose a color palette for Power BI reports:
A color palette is simply a collection of colors applied to the visual elements in your report. What we typically refer to as color is a combination of three main properties: hue (base color on the color wheel), intensity (brightness or gray-ness) and value (lightness or darkness). You can build an engaging and professional looking report with just 6 colors. It’s possible to have fewer colors or more colors, but 6 should cover many common visualization needs. If you are using more than 6 colors, you might want to check that you are optimizing engagement and cognitive load.
Main color – default color on graphs
Color 2 – used when multiple colors are needed in a graph or report
Color 3 – used when multiple colors are needed in a graph or report and Color 2 has already been used
Highlight color – a color used to highlight important data points to make them stand out from other points on the page
Border color – a light color used for borders on tables and KPIs where necessary
Title color – color used for visual titles and axis labels as appropriate
There’s a lot of good advice in here.
Comments closedShant Hovsepian shows off a data visualization tool which can read Kafka Streams data:
KSQL is a game-changer not only for application developers but also for non-technical business users. How? The SQL interface opens up access to Kafka data to analytics platforms based on SQL. Business analysts who are accustomed to non-coding, drag-and-drop interfaces can now apply their analytical skills to Kafka. So instead of continually building new analytics outputs due to evolving business requirements, IT teams can hand a comprehensive analytics interface directly to the business analysts. Analysts get a self-service environment where they can independently build dashboards and applications.
Arcadia Data is a Confluent partner that is leading the charge for integrating visual analytics and BI technology directly with KSQL. We’ve been working to combine our existing analytics stack with KSQL to provide a platform that requires no complicated new skills for your analysts to visualize streaming data. Just as they will create semantic layers, build dashboards, and deploy analytical applications on batch data, they can now do the same on streaming data. Real-time analytics and visualizations for business users have largely been a misnomer until now. For example, some architectures enabled visualizations for end users by staging Kafka data into a separate data store, which added latency. KSQL removes that latency to let business users see the most recent data directly in Kafka and react immediately.
Click through for a couple repos and demos.
Comments closedSebastian Sauer has a quick example of using ggcounty to plot data on a map of US counties:
This posts shows how easy it can be to build an visually pleasing plot. We will use hrbrmster’s
ggcounty, which is an R package at this Github repo. Graphics engine is as mostly in my plots, Hadley Wickhamsggplot. All build on R. Standing on shoulders…Disclaimer: This example heavily draws on hrbrmster example on this page. All credit is due to Rudy, and those on whose work he built up on.
In just a few lines of code, you can have a pretty nice map.
Comments closed