SNAPverse R package: snapgrid, continued

This post expands upon the brief introduction to the snapgrid package given earlier.


SNAPverse R package: snapgrid

This post introduces the snapgrid R package, a data package in the SNAPverse R package ecosystem. It contains a number of convenient raster objects sourced from spatially explicit maps commonly used at SNAP. snapgrid contains a collection of rasterized maps focused on Alaska and Canada, including vegetation input for the ALFRESCO wildfire model, fire management options map layers, domain template layers for the Alaska “statewide” classic ALFRESCO domain, and 1-km ALFRESCO and 2-km climate templates for the Alaska/western Canada domain. This is the second R package member of the SNAPverse introduced here, following snappoly, and like snappoly it is one of the simplest. It provides auxiliary data rather than SNAP data end products. snapgrid is essentially the rasterized map complement to snappoly.


SNAPverse R package: snappoly

This post introduces the snappoly R package, a data package in the SNAPverse R package ecosystem. It contains a number of convenient SpatialPolygonsDataFrame objects sourced from shapefiles commonly used at SNAP. This is the first R package member of the SNAPverse introduced here and is one of the simplest. It is a data package, but is best thought of as providing auxiliary data rather than SNAP data end products. Access to and analysis of common SNAP data sets will be topics of later posts on other SNAPverse packages. The purpose of snappoly is to offer convenient access to common spatial polygons map layers used across various SNAP projects.


Introducing the SNAPverse R package ecosystem

This post introduces the SNAPverse R package ecosystem. A series of followup posts will introduce different SNAPverse R packages that offer functionality to meet specific researcher and analyst needs regarding:

  • easy, instant access and interfacing to curated publicly available SNAP data sets hosted in the cloud, right from your R console, ready for export or analysis.
  • stock functions and procedures for statistical analysis of SNAP data.
  • graphing of SNAP data using common techniques and consistent formatting while offering multiple standardization options and flexible presentation.
  • reporting with reproducible static and interactive documents and accessing curated collections of prior works.


Mix ggplot2 graphs with your favorite memes. memery 0.4.2 released.

Make memorable plots with memery. memery is an R package that generates internet memes including superimposed inset graphs and other atypical features, combining the visual impact of an attention-grabbing meme with graphic results of data analysis. Version 0.4.2 of memery is now on CRAN. The latest development version and a package vignette are available on GitHub.


Assorted Shiny apps collection, full code and data

Here is an assortment of R Shiny apps that you may find useful for exploration if you are in the process of learning Shiny and looking for something different. Some of these apps are very small and simple whereas others are large and complex. This repository provides full code and any necessary accompanying data sets. The repo also links to the apps hosted online at so that you can run apps in your browser without having to download the entire collection repo to run apps locally. That and other details can be found at the repo linked above. This isn’t a tutorial or other form of support, but it’s plenty of R code to peruse if that is what you are looking for.


Custom images for Shiny dashboard valueBox icons

The shinydashboard package provides functions like valueBox that conveniently display basic information like summary statistics. In addition to presenting a value and subtitle on a colored background, an icon may be included as well. However, the icon must come from either the Font Awesome or Glyphicon icon libraries and cannot be image files.

I’ve provided a gist that shows how to achieve the use of custom icons with local image files stored in an app’s www/ directory. It involves overriding a couple functions in shiny and shinydashboard and adding a small bit of custom CSS. Ideally, functionality could be included in future versions of these two packages to allow this in a more robust and complete fashion. But for now, here is a way to do it yourself for value boxes.