A Fine Slice Of SQL Server
Mara Pereira does a bit of conditional formatting:
In my example, I created a report where you could switch the measure the charts were showing, and with that, the color of the charts and KPI cards changed to.
The first thing you need to do is create your base measures.
Click through for a step-by-step walkthrough with plenty of GIFs to provide a visual explanation.
Comments closedMike Cisneros cleans up a complex visual made up of less complex visuals:
There are opportunities to improve this visual, but I appreciate that the graph is appropriately titled, that the legend is clear and easy to find, and that the most recent data point is the only one that is labeled. On its own, it’s an acceptable view of the data, albeit one that could be strengthened.
When this visual is considered in its greater context, however, the need to improve the legibility of this graph becomes obvious.
These charts are generated at multiple levels of geographic and thematic detail. Each one is then shared as part of one large report, with almost no visual variation from region to region, level to level, or week to week:
There’s a lot going for that redesign.
Comments closedThomas Williams wants to take control:
It is possible – though not the default – set set keyboard focus on load to a control in an R Markdown web page rendered with Shiny.
Setting keyboard focus when an R Markdown page is loaded is beneficial to users, who can start interacting with the page without having to first click the control.
Click through to learn how. This is one of the biggest losses we have in the GUI era: that you have a mouse necessitates using the mouse for everything. But that’s a “get off my lawn” rant for another day.
Comments closedThomas Williams wants you to wait patiently:
Waits are inevitable, whether getting data from an API or database, or manipulating data in an interactive R Markdown document. Showing a “loading” or “updating” message is a beneficial incremental improvement to users’ experience.
The code at https://github.com/thomasswilliams/r-markdown-snippets/blob/main/loading-message-datatable.Rmd, when run from RStudio, demonstrates using CSS and pseudo-elements to display text in a DataTables (
DTpackage).
Read on to see an example of how to use this.
Comments closedJeff Hill has fun with terminals:
If you have followed along with me before, chances are you know I like color. Color helps me zoom in on the information I want faster. Color can help me know if I have the right number of curly braces. Modern browsers and word processors show a red squiggly line when you misspell a word. Your eye is drawn to that bit of color that is different than the rest of the page. Keep reading and I’ll show you how to add some spice to your PowerShell experience.
I heartily endorse the mention of Windows Terminal later on. If you haven’t tried it before, it’s a worthy successor to CMD and the basic Powershell terminal.
Comments closedSimon Rowe answers describes one of my favorite often-inappropriate visuals:
Dr Shneiderman developed the “treemap” in order to visualise this large amount of data—with multiple levels of folders and subfolders—in an efficient way, without taking up too much screen real estate. The treemap uses a series of nested rectangles, sized proportionally to the corresponding data value, to deliver an organised and multi-level view into any hierarchical data set.
Treemaps get misused a lot but are really valuable in specific scenarios. Click through to learn when (and when not) to use a treemap.
Comments closedBenjamin Smith looks for delays:
Any Torontonian who has commuted regularly on the TTC has probably experienced their fair share of delays on the subway. Having experienced a few recently I was inspired to visualize the average delay times across all stops on the subway. What are the stations with the longest delays on average this past year? Could we make a nice visual with it?
Click through for the end result as well as the process to get there.
Comments closedJordan 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 closed