A Fine Slice Of SQL Server
Jordan Witcombe does some slicing and dicing:
Let’s say you’re planning to present a large table of information and you want the user to be able to filter based on the top or bottom X number of records. Due to the size of the dataset we would like the customer to have the ability to filter based on more than one column for their comparison. Making a large dataset much easier to digest.
Read on to see how you can accept user input in Tableau and simplify the viewable data.
Comments closedTomaz Kastrun shows off contributions:
Github is collecting various images on their Instagram and Twitter that looks like a contribution graph.
Well, why not create one in R with randomly generated data and the use of the ggplot library.
It’s surprisingly easy to create a contribution graph using R.
Comments closedJamie Owen kicks the tires on Py-shiny:
We would posit (see what we did there) that R-{shiny} has been a boon for data science practitioners using the R language over the last decade. We know that in our Python work, we have certainly been clamouring for something of the same ilk. And whilst there are other frameworks that we also like, streamlit and dash to name a couple, neither of them has filled us with the same excitement and confidence that shiny did in R to build both simple and complex bespoke web applications. With
RStudioPosit conf in action the big news from July 27th was the alpha release of Py-{shiny} which was a source of great interest for us, so we couldn’t resist installing and starting to build.If you are familiar with R-shiny already, then much of the py-shiny package will feel familiar to you (albeit with a couple of things having been renamed). However we will approach the rest of this post assuming that a reader does not have that prior experience and take you through building a simple shiny application to display plots on subsets of a dataset.
I’m curious how much take-up there will be for the library, given that there are several good competitors on Python.
Comments closedPascal Schmidt builds a Shiny dashboard:
I participated in the R Shiny 2021 contest and published an application similar to the Google Analytics dashboard app. For that, I used the Google Analytics API and the Google Search Console API to pull my own data from my blog directly into the application.
The application uses the
shinyauthrlibrary because the dashboard can be adjusted for each user who has their own username and password. On the first page, there are some visualizations for page views, devices used, etc. On the second page, there is a time-series model that tries to predict my page views two months in advance.
In particular, this post covers a couple of the foundational pieces, with future posts getting into more detail on other components. H/T R-Bloggers.
Comments closedTim Brock continues a series on theming diffify:
It’s difficult to design a website that is “just right” for everyone. For instance, while reds and greens can be difficult to discern for some dichromats and anomalous trichromats, most trichromats have no such problem (peak daylight sensitivity lies in the yellow part of the spectrum, between red and green). Moreover, these colours also have common cultural semantics (though these do, of course, vary by culture). We also care about aesthetics.
Because of this conflict and more besides, we decided the best approach to making the site more accessible was through “theming”.
Click through to see what this entails.
Comments closedMark LItwintschik looks at another charting library:
The Apache eCharts project is a web-based charting library. It was started in 2013 and built using 77.5K lines of TypeScript. It is well documented and has over 200 examples of its API’s usage. The examples allow you to toggle between light/dark mode and there is a cheat sheet and a theme builder with several tasteful presents to choose from.
This is a library I hadn’t heard of before but Mark shows it off a bit.
Comments closedMark Litwintschik provides some recommendations:
There are two major phases of data analysis. The first is building up a basic understanding of a new dataset. Once this is done there is a second phase of understanding what’s changing over time and if there are any new outliers.
For the first phase, I find Tableau to be more productive than writing code in a Jupyter Notebook. For the second phase, I like to build periotic Airflow jobs that send charts and Excel files to operational channels on Slack. These are formatted to be mobile-friendly and allow me to do more of my work on a phone rather than being chained to a laptop. This also means access is controlled via Slack rather than a custom web app.
Mark also covers some examples with Altair.
Comments closedMara Pereira makes a Power BI breadcrumb:
When I’m developing reports, I’m asked multiple times to “hide” the slicers/filters from the report page. Usually this is to make space in the page for other visuals and because customers don’t want to use the filter pane for some reason.
This happened so many times, and only in the last couple of months I decided to try some things out and get a bit creative with Power BI.
I came up with two solutions, which I think work great in these scenarios.
Read on to see the solutions.
Comments closedTim Brock covers color vision deficiency:
In this blog post and a follow up I’m going to describe why and how we used theming to make diffify.com more accessible to users who suffer from some common visual impairments. Here in Part 1 I’ll cover some of the science and the terminology. Part 2 will look at the actual changes we made.
Click through for a brief discussion of contrast sensitivity and a longer one on color vision deficiency, including why color choice matters.
Comments closedMara Pereira adds target bands to a line chart:
At the time I could not really find an easy way to achieve this… Until error bars came out!
Don’t be fooled though, it’s still a bit tricky to build a line chart like this, however I found it way easier now than before.
So, you must be thinking now “how did you do that?”.
Well, let’s find out!
The end result looks really nice, though it takes a lot of work to get there.
Comments closed