The main goal of developing this package is to construct an R-based front-end to connect to a variety of highly used TASSEL methods and analytical tools. By using R as a front-end, we aim to utilize a unified scripting workflow that exploits the analytical prowess of TASSEL in conjunction with R’s popular data handling and parsing capabilities without ever having the user to switch between these two environments.
If you want to test out what this package does but do not want to install it locally, we have set up an interactive Jupyter notebook detailing the walkthrough of
rTASSEL on Binder. The Binder link can be accessed through the Binder icon on this page or by clicking here.
If you do not have experience working with and setting up
rJava with your R installation, it is recommended that you read the long-form documentation. This walkthrough can be found here. If you are already fairly comfortable working with Java JDK and
rJava, you can follow the following commands.
Package source code can be installed directly from this BitBucket repository using the
if (!require("devtools")) install.packages("devtools") devtools::install_bitbucket( repo = "bucklerlab/rTASSEL", ref = "master", build_vignettes = FALSE )
build_vignettes) are optional since there are constantly updated article links on our website. If you do want to build vignettes locally, make sure you have the required packages and programs available.
For an overview of available functions, use the following command:
If you need a walkthrough for potential pipelines, long-form documentation can be found on our website. If you prefer to compile a vignette locally, you can set the
build_vignettes parameter to
TRUE when you download from Bitbucket (Note: compiling the vignette may take some time to process and analyze test data.)
If you would like to study a function in full, refer to the R documentation by using
?<function> in the console, where
<function> is an