https://jonjup.netlify.app/tags/r/ Recent content in R on Amaltheia Hugo -- gohugo.io en-us <a href="https://creativecommons.org/licenses/by-nc/4.0/" target="_blank" rel="noopener">CC BY-NC 4.0</a> Thu, 07 Jul 2022 00:00:00 +0000 https://jonjup.netlify.app/post/introduction-to-sf/ Thu, 07 Jul 2022 00:00:00 +0000 https://jonjup.netlify.app/post/introduction-to-sf/ When using R as a GIS, there is no way past the sf package(Pebesma, 2018). Theoretically you could use the quasi predecessor sp, but beyond some old packages requiring it, there really is no reason to do so. With this script, you will learn to use sf to load spatial data in R, to perform standard data wrangling procedures on them, to make (interactive) maps, and lastly, to perform basic geospatial operations with them. https://jonjup.netlify.app/post/the-rayshader-r-package/ Wed, 10 Feb 2021 00:00:00 +0000 https://jonjup.netlify.app/post/the-rayshader-r-package/ In this entry we will go through the basics of rayshader. Rayshader is one of those packages that you see again and again when you follow people from the R-spatial community on twitter. Especially the package’s creator Tyler Morgan-Wall (see here for his website and here for his Twitter) posts videos and images of things he did with it on an almost daily basis. And he has all the reasons to do so. https://jonjup.netlify.app/post/r-package-of-the-week-dataexplorer/ Tue, 09 Feb 2021 00:00:00 +0000 https://jonjup.netlify.app/post/r-package-of-the-week-dataexplorer/ install.packages(&quot;DataExplorer&quot;) library(DataExplorer) data = readRDS(&quot;collected_site_scores.RDS&quot;) This weeks package is similar to last weeks. Just like xray Seibelt (2017), DataExplorer Cui (2020) is used for exploratory data analysis. To highlight the features and capabilities of the package we will use a data set of different diatom metrics derived from a large data set of diatoms, which I unfortunately am not able to share with you. These metrics were computed with the diaThor package, which I will cover in a later post. https://jonjup.netlify.app/post/joint-species-distribution-modelling-with-hmsc/ Tue, 05 Jan 2021 00:00:00 +0000 https://jonjup.netlify.app/post/joint-species-distribution-modelling-with-hmsc/ body { text-align: justify} Introduction In this short example, we will use the Hierarchical Modeling of species communities (HMSC, Ovaskainen and Abrego (2020)) approach through its implementation in the HMSC-R package (Tikhonov et al. 2020) to analyze and predict the distribution of the several bird species in Finland. This is a multi-species example of HMSC - if you are interested in a single-species example see here. This post is meant to accompany the advanced methods in multivariate statistics lecture at the University of Koblenz-Landau. https://jonjup.netlify.app/post/a-copula-model-for-mutlivariate-ecological-data/ Thu, 31 Dec 2020 00:00:00 +0000 https://jonjup.netlify.app/post/a-copula-model-for-mutlivariate-ecological-data/ body { text-align: justify} In this blog post, we will go through the copula model for ecological data that was proposed by Anderson et al. (2019). While roughly the first half of this code is available in the supplementary information of that paper, the second half is only described in the paper and I am not aware of any other place where comparable code has been published. https://jonjup.netlify.app/post/analyzing-the-anttrait-data-with-boral/ Thu, 31 Dec 2020 00:00:00 +0000 https://jonjup.netlify.app/post/analyzing-the-anttrait-data-with-boral/ body { text-align: justify} In this post we will, analyze the antTraits data with generalized linear latent variable models fit with the BORAL R package (Hui 2016). Elsewhere on the blog you can find an analysis of the same data using mvabund, gllvm and CAO/CQO. First of, we will setup the analysis by loading the required libraries. If you haven’t already done so, you will need to install the pacman R package before running this code. https://jonjup.netlify.app/post/analyzing-the-anttraits-data-with-cqo-and-cao/ Thu, 31 Dec 2020 00:00:00 +0000 https://jonjup.netlify.app/post/analyzing-the-anttraits-data-with-cqo-and-cao/ body { text-align: justify} In this script we will analyze the antTraits data with CAO and CQO. Elsewhere on the blog you can find an analysis of the same data using mvabund, boral and gllvm. First of we will setup the analysis by loading the required libraries. If you haven’t already done so, you will need to install the pacman R package before running this code. Additionally I will load my custom made residual plot function. https://jonjup.netlify.app/post/analyzing-the-anttraits-data-with-gllvm/ Thu, 31 Dec 2020 00:00:00 +0000 https://jonjup.netlify.app/post/analyzing-the-anttraits-data-with-gllvm/ body { text-align: justify} In this post we will analyze the antTraits data with generalized linear latent variable models fit with the gllvm R package (Niku et al. 2020). Elsewhere on the blog you can find an analysis of the same data using mvabund and boral. First of we will setup the analysis by loading the required libraries. If you haven’t already done so, you will need to install the pacman R package before running this code. https://jonjup.netlify.app/post/analyzing-the-anttraits-data-with-mvabund/ Tue, 08 Dec 2020 00:00:00 +0000 https://jonjup.netlify.app/post/analyzing-the-anttraits-data-with-mvabund/ body { text-align: justify} In this post, we will use the mvabund R-package to analyze the antTraits data set. Elsewhere on the blog you can find an analysis of the same data using gllvm, boral and CAO/CQO. Preparing the analysis First we will setup the analysis by loading the required libraries. If you haven’t already done so, you will need to install the pacman R package before running this code. https://jonjup.netlify.app/post/species-distribution-model-with-hmsc/ Mon, 26 Oct 2020 00:00:00 +0000 https://jonjup.netlify.app/post/species-distribution-model-with-hmsc/ body { text-align: justify} Introduction In this short example, we will use the Hierarchical Modeling of Species Communities (HMSC, Ovaskainen and Abrego (2020)) approach through its implementation in the HMSC-R package (Tikhonov et al. 2020) to analyze and predict the distribution of the Western jackdaw (Corvus monedula) in Finland. This post works through a univariate example of HMSC - if you are interested in a multi-species example, see here.