Nearly 4 years ago I wrote on this blog about an R package solr for working with the database Solr. Since then we’ve created a refresh of that package in the solrium package. Since solrium first hit CRAN about two years ago, users have raised a number of issues that required breaking changes. Thus, this blog post is about a major version bump in solrium.

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What is Solr?

Solr is a “search platform” - a NoSQL database - data is organized by so called documents that are xml/json/etc blobs of text. Documents are nested within either collections or cores (depending on the mode you start Solr in). Solr makes it easy to search for documents, with a huge variety of parameters, and a number of different data formats (json/xml/csv). Solr is similar to Elasticsearch (see our Elasticsearch client elastic) - and was around before it. Solr in my opinion is harder to setup than Elasticsearch, but I don’t claim to be an expert on either.

• data
• solr
• solrium
• databases
• tech notes

This week magick 1.5 appeared on CRAN. The latest update adds support for using images in knitr documents and shiny apps. In this post we show how this nicely ties together a reproducible image workflow in R, from source image or plot directly into your report or application.

library(magick)
stopifnot(packageVersion('magick') >= 1.5)

Also the magick intro vignette has been updated in this version to cover the latest features available in the package.

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Magick in Knitr / RMarkdown Documents

Magick 1.5 is now fully compatible with knitr. To embed magick images in your rmarkdown report, simply use standard code chunk syntax in your Rmd file. No special options or packages are required; the image automatically appears in your documents when printed!

• packages
• magick
• images
• tech notes

Release 1.4 of the magick package introduces a new feature called image convolution that was requested by Thomas L. Pedersen. In this post we explain what this is all about.

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Kernel Matrix

The new image_convolve() function applies a kernel over the image. Kernel convolution means that each pixel value is recalculated using the weighted neighborhood sum defined in the kernel matrix. For example lets look at this simple kernel:

library(magick)

kern <- matrix(0, ncol = 3, nrow = 3)
kern[1, 2] <- 0.25
kern[2, c(1, 3)] <- 0.25
kern[3, 2] <- 0.25
kern
##      [,1] [,2] [,3]
## [1,] 0.00 0.25 0.00
## [2,] 0.25 0.00 0.25
## [3,] 0.00 0.25 0.00

This kernel changes each pixel to the mean of its horizontal and vertical neighboring pixels, which results in a slight blurring effect in the right-hand image below:

• packages
• magick
• images
• tech notes

Just last week we organised the 2nd rOpenSci ozunconference, the sibling rOpenSci unconference, held in Australia. Last year it was held in Brisbane, this time around, the ozunconf was hosted in Melbourne, from October 26-27, 2017.

At the ozunconf, we brought together 45 R-software users and developers, scientists, and open data enthusiasts from academia, industry, government, and non-profits. Participants travelled from far and wide, with people coming from 6 cities around Australia, 2 cities in New Zealand, and one city in the USA. Before the ozunconf we discussed and dreamt up projects to work on for a few days, then met up and brought about a bakers dozen of them into reality.

• Australia
• community
• meetings
• ozunconf
• ozunconf17
• unconf