simpler SQL with dplyr

comparing dplyr with SQL nested queries – Following on from my last post, where I demonstrated R to some first time R users, I want to do a wee comparison of dplyr V SQL, so that folks, particularly those in the NHS who might be R curious, can see just what the fuss is about. To do so I want to recap on the example I showed at the AphA Scotland event. This,in turn goes back to some work I’ve been doing with Neil Pettinger, where we are looking at ways to visualise patient flow. This relies on a spreadsheet that Neil originally put together. Part of my demo was to explain how to recreate the visualisation in R, but I also showed some of the data transformation steps carried out using dplyr and some fellow tidyverse helpers. In this…
Original Post: simpler SQL with dplyr

Geocomputation with R: workshop at eRum

This is a post by Robin Lovelace, Jakub Nowosad and Jannes Muenchow. Together we’re writing an open source book called Geocomputation with R. The project aims to introducing people to R’s rapidly evolving geographic data capabilities and provide a foundation for developing scripts, functions and applications for geographic data science. We recently presented some contents of the in-progress book at the eRumconference, where Jannes ran a workshop on the topic. In this article we share teaching materials from eRum for the benefit of those who couldn’t be there in person and provide a ‘heads-up’ to the R-Spatial community about plans for the book. We’ll start with an overview of ‘geocomputation’ (and define what we mean by the term) and finish by describing how R can be used as a bridge to access dedicated GIS software. The first thing many people…
Original Post: Geocomputation with R: workshop at eRum

Make Your Models a Competitive Advantage

[unable to retrieve full-text content]The Model Management white paper, based on our experience working with hundreds of model-driven organizations, describes the reasons most organizations have not yet unlocked the transformative potential of models and provides a framework for success.
Original Post: Make Your Models a Competitive Advantage

Overview of Dash Python Framework from Plotly for building dashboards

[unable to retrieve full-text content]Introduction to Dash framework from Plotly, reactive framework for building dashboards in Python. Tech talk covers basics and more advanced topics like custom component and scaling.
Original Post: Overview of Dash Python Framework from Plotly for building dashboards

New round of R Consortium grants announced

Related To leave a comment for the author, please follow the link and comment on their blog: Revolutions. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more… If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook…
Original Post: New round of R Consortium grants announced

On the contribution of neural networks and word embeddings in Natural Language Processing

[unable to retrieve full-text content]In this post I will try to explain, in a very simplified way, how to apply neural networks and integrate word embeddings in text-based applications, and some of the main implicit benefits of using neural networks and word embeddings in NLP.
Original Post: On the contribution of neural networks and word embeddings in Natural Language Processing

Three ways of visualizing a graph on a map

When visualizing a network with nodes that refer to a geographic place, it is often useful to put these nodes on a map and draw the connections (edges) between them. By this, we can directly see the geographic distribution of nodes and their connections in our network. This is different to a traditional network plot, where the placement of the nodes depends on the layout algorithm that is used (which may for example form clusters of strongly interconnected nodes). In this blog post, I’ll present three ways of visualizing network graphs on a map using R with the packages igraph, ggplot2 and optionally ggraph. Several properties of our graph should be visualized along with the positions on the map and the connections between them. Specifically, the size of a node on the map should reflect its degree, the width of…
Original Post: Three ways of visualizing a graph on a map