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JUICE-R is a way how to run R scripts using data directly exported from JUICE. JUICE-R scripts are basically R scripts written in the way that they can be directly launched from JUICE program, using species and header data exported from JUICE.
JUICE is a freeware Windows OS program for editing and analysis of ecological (mainly vegetation) data, while R is a freeware multiplatform program environment for data analysis and visualization. Connection between these two programs extends JUICE's functionality in the way that data stored in JUICE could be directly exported into R and analyzed using appended R script.
This wiki is intended to be an open platform for sharing R scripts, which are tailored to be run directly from JUICE program in order to analyze, visualize or otherwise process ecological data stored in JUICE.
The program JUICE has been developed since 1998 by Lubomír Tichý from Masaryk University in Brno, Czech Republic. It was designed as Windows application for editing, classification and analysis of large phytosociological tables and other ecological data. With current maximum capacity of 1.000.000 relevés and sufficient number of species in one table, JUICE utilises many functions for easy manipulation of table and header data. Various options include classification using COCKTAIL, divisive (TWINSPAN) and aglomerative methods, calculation of interspecific associations, fidelity measures, average Ellenberg indicator values, preparation of synoptic tables, automatic sorting of relevé tables, and export of table data into other applications (text editors, table processors or mapping packages).
R is a programming language and freeware software environment for statistical analysis and data visualization. It's based on original S language, which was developed by John Chambers and his colleagues in Bell Laboratories and which is the heart of commercial S-PLUS package. R implementation of S language was invented by Ross Ihaka and Robert Gentleman and currently is maintained by multinational group of R Core Development Team. As a strong aspect of R program could be recognized its library based structure, which allows extending the basic R functionality by additional customized libraries, such as vegan, labdsv or ecodist for ecological data analysis.