A Fine Slice Of SQL Server
Benjamin 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 closedTomaz Kastrun has a script for us:
Determining and checking who has access to a particular workspace in your organisation can be time consuming task. You can always retrieve the list of workspaces and access the list of all users with PowerShell cmdlet Get-PowerBIWorkspace.
The results of this cmdlet gives you a great way to get to valuable information easy and fast. And the following script can do just that.
Click through for the script, which also excludes personal workspaces. Tomaz then provides a bonus script creating a graph of how users and workspaces connect.
Comments closedParisa Gregg continues a series on RStudio Connect and Python:
FastAPI is a light web framework and as you can probably tell by the name, it’s fast. It provides a similar functionality to Flask in that it allows the building of web applications and APIs, however it is newer and uses the ASGI (Asynchronous Server Gateway Interface) framework. One of the nice features of FastAPI is it is built on OpenAPI and JSON Schema standards which means it has the ability to provide automatic interactive API documentation with SwaggerUI. You also get validation for most Python data types with Pydantic. FastAPI is therefore another popular choice for data scientists when creating APIs to interact with and visualize data.
In this blog post we will go through how to deploy a simple machine learning API to RStudio Connect.
I’ve taken pretty well to FastAPI for rapid API development. I haven’t had to worry about scaling it out too much, so I’m not sure how well that works in practice. Still, for single-user or few-user apps, FastAPI definitely works well.
Comments closedJoris Chau pivots, unpivots, and unnests data in R:
The previous post showcases the
rrapply()function in the minimalrrapply-package as a revised and extended version of baserapply()in the context of nested list recursion in R. For quick data exploration of a nested list it can make sense to keep the list in its original nested format to reduce the number of processing steps and minimize code complexity. As part of a more elaborate data analysis, if there is no specific reason to keep the nested data structure, it is often more practical to transform the nested list into a more convenient rectangular format and work with the unnested object (e.g. a data.frame) instead. In this follow-up post, we review the available (how) options inrrapply()to unnest or melt nested lists into a rectangular format in more detail and highlight the similarities and differences with respect to several common alternatives in R.
There’s a lot going on in this demo-packed post. H/T R-Bloggers.
Comments closedDavid Schoch has an R package for us:
During the last 5 years, I have accumulated various scripts with (personal) convenience functions for network analysis and I also implemented new methods from time to time which I could not find in any other package in R. The package
netUtilsgathers all these functions and makes them available for anyone who may also needs to apply “non-standard” network analytic tools. In this post, I will briefly highlight some of the most prominent functions of the package. All available functions are listed in the README on github.
Click through to see what’s available in the package. H/T R-Bloggers.
Comments closedParisa Gregg crosses the language barrier:
One of the Python applications you can deploy to RStudio Connect is Flask. Flask is a WSGI (Web Server Gateway Interface) web application framework and provides a Python interface to enable the building of web APIs. It is useful to data scientists, for example for building interactive web dashboards and visualisations of data, as well as APIs for machine learning models. Deploying a Flask app to a publishing platform such as RStudio Connect means it can then be used from anywhere and can be easily shared with clients.
This blog post focuses on how to deploy a Flask app to RStudio Connect. We will use a simple example but won’t go into detail on how to create Flask apps. If you are getting started in Flask you may find this tutorial useful.
Read on for a demo.
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 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 closedBenjamin Smith explains how tapply() works:
While I saw other programmers use this function, I found myself unsure how of how it worked or knew when I would need to use it. In this blog I attempt to change that and explain the cryptic description by showing some applications with my commentary and how it compares to using the “tidy” approach with
tidyverse.My inspiration for writing this blog was from seeing Dr. Norm Matloff’s blog where he mentions the use of
tapply()and his thoughts on the tidyverse. For a more thorough treatment on his critique of the tidyverse and “tidy” methods, check out his formal essay here.
Read on to learn the benefit of learning and using tapply().
Thomas Williams passes along querystring data:
As background, a query string is part of a web page address. Query strings are used to pass information to web pages, in name/value pairs separated by an equals sign – for instance,
user=Andreworcountry=au. Name/value pairs are themselves separated by ampersands, so passing multiple values looks likeuser=Andrew&country=au.
Click through for an example of how it all works.
Comments closed