I installed org-mode seperately, since I had troubles with its default setting (similar to the problems described here). sudo apt-get install org-mode Next I download and installed ESS cd ~/.emacs.d/ wget http://ess.r-project.org/downloads/ess/ess-12.09-1.zip unzip ess-12.09-1.zip rm ess-12.09-1.zip Finally I had to … Continue reading →
On Ubuntu 12.04, I took to the following steps to install Vim-R-plugin: First I had to install timux: sudo apt-get installl tmux Having already installed vim-pathogen, I used git to clone to install vim-r-plugin and vim-screen plugin, which is also … Continue reading →
Over the last few weeks I was trying to optimise my workflow using markdow in combination with knitr and pandoc. Knitr is a grea new package by Yihui, expanding R’s capabilities for reproducible research. I will illustrate my work flow … Continue reading →
Working with date and time in R is sometimes tricky. Today I gave lubridate a try and was surprised on how easy it can be. Lubridate is a available on git and on CRAN. There is also a good introduction … Continue reading →
Sometimes I do not want to unzip files before reading them to R. There is a nice way of reading zipped file (via a tmp dir) into R. Where the file test.csv is actually located in the: ~/files/myzip.zip/test.csv.
With ggplot2 there is a possibility to create divide bars of a histogram into different categories:
There are many implantation in R already of ROC plots (e.g. in the packages PresenceAbsence, ROCR). I just wrote my own very simple script just to get a better understanding of it.
I decided to go use ggplot2 more frequently and go through everything. For future reference I will start a series of blog posts on ggplot2. Scatter plot: In my next post, I will change the axis labels.
Getting presence data of species is often not easy and can be a major obstacle when attempting to model the distribution of species. One way is using the GBIF data base. Here I show one way how to obtain presence … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
