Norway's International Climate and Forest Initiative (NICFI) using Planet Satellite Imagery in R

In this blog post I’ll explain the functionality of the PlanetNICFI R package based on a change detection use case. The official website of NICFI (Norway’s International Climate and Forest Initiative) includes all the details about the initiative against global deforestation. This initiative was also covered extensively on the web especially from the provider of the free Satellite Imagery. Users have the opportunity to download high-resolution imagery of forests globally using a simple sign up form.

To take advantage of the PlanetNICFI R package you will need also an API key which you can receive once you are registered. For more details see the Getting Started with Planet APIs website.

Digital Elevation Models using the 'CopernicusDEM' R package

In this blog post I’ll explain how to use the CopernicusDEM R package based on a use case of the Movebank animal tracking data. I picked animal tracking data because there is an abundance in the Movebank archive from all over the world. In this specific vignette I’ll use data of Wolves from the northeastern Alberta and Caribou from the British Columbia (see the reference papers at the end of the blog post for more information).

Fitbit Visualizations using the 'fitbitViz' R package

This blog post explains the functionality of the fitbitViz R package. If you own any of the Fitbit activity trackers you can take advantage of this package to visualize your data using ‘ggplot2’, ‘Leaflet’ and 3-dimensionsal ‘Rayshader’ Maps. The 3-dimensional Rayshader Map requires the installation of the CopernicusDEM R package which includes the 30- and 90-meter elevation data. You can read more about the Fitbit Web API and how to create an application to receive a token and the user-id in the README.md file of the package. In the README.md file you will find information on how to,

• Register an Application and receive the token
• Keep track of your activities using ‘fitbitViz’, ‘Blogdown’ and ‘Github Actions’ (for instance you can see my personal setup which runs on a weekly basis)

Language Identification using the 'fastText' package (a Benchmark)

In this blog post, I’ll explain how to perform Language Identification with the fastText R package and I’ll create a benchmark by including other language identification R packages, i.e.

• cld2, R Wrapper for Google’s Compact Language Detector 2
• cld3, Bindings to Google’s Compact Language Detector 3
• textcat, N-Gram Based Text Categorization
• franc, Port of the JavaScript ‘franc’, Detect the Language of Text

fastText ( updated version )

In this blog post, I’ll explain the updated version of the fastText R package. This R package is an interface to the fasttext library for efficient learning of word representations and sentence classification. Back in 2016 I ported for the first time the fasttext library but it had restricted functionality. This updated version allows the user to take advantage of all current features and it adds new ones such as plotting of loss, learning-rate and word-counts.