All posts (Page 95 of 128)
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!
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:
...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.
...A new rOpenSci package provides access to data to which users may already have directly contributed, and for which contribution is fun, keeps you fit, and helps make the world a better place. The data come from using public bicycle hire schemes, and the package is called bikedata. Public bicycle hire systems operate in many cities throughout the world, and most systems collect (generally anonymous) data, minimally consisting of the times and locations at which every single bicycle trip starts and ends. The bikedata package provides access to data from all cities which openly publish these data, currently including London, U.K., and in the U.S.A., New York, Los Angeles, Philadelphia, Chicago, Boston, and Washington DC. The package will expand as more cities openly publish their data (with the newly enormously expanded San Francisco system next on the list)....
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<em>David Smith is a Blogger and Community Lead at Microsoft. I had the chance to interview David last May at rOpenSci unconf17. We spoke about his career, the process of working remote within a team, community development/outreach and his personal methods for discovering great content to share and write about.</em>
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KO: What is your name, job title, and how long have you been using R?
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Working together to push science forward
Happy rOpenSci users can be found at
