# NOT RUN {
# This function relies on script templates which can be found at:
# 'https://github.com/JDench/SHAPE_library/tree/master/SHAPE_templates'
# Once these have been downloaded you can pass the appropriate filepath values
# to the first two arguments. For this example, I'll assume you've installed
# them to a folder position that is now just under the root of your
# R-environment working directory.
# However, before runing the function we need to parameterise your run of SHAPE,
# here I call the default parameters:
defineSHAPE()
# Now using the default templates we design an experiment folder complete with
# shell scripts to submit our work programatically.
# NOTE: Again, this example assumes you've downloaded the templates and placed
# them at the next filepath and directory-path locations
# }
# NOT RUN {
shapeExperiment(func_filepath_toDesign = "~/SHAPE_templates/SHAPE_experimentalDesign.v.1.r",
func_templateDir = "~/SHAPE_templates/")
# }
# NOT RUN {
# You should be greeted with a message suggesting your experiment is built.
# You can find the files now at that script's SHAPE workingDirectory.
list.files(getOption("shape_workDir"))
# Voila! You can go see the spread of variable evolutionary parameters that were
# considered by looking at -- yourJob_parameterCombos.table -- which is a tab
# delimated file.
# Lastly, you may have R installed elsewhere and so want to have that noted while
# your experiment is built because the shell scripts will need to point to the correct place.
# }
# NOT RUN {
shapeExperiment(func_filepath_toDesign = "~/SHAPE_templates/SHAPE_experimentalDesign.v.1.r",
func_templateDir = "~/SHAPE_templates/",
func_filePath_R = "~/your_R_folder/R_app/bin/R")
# }
# NOT RUN {
# Now obviously the above location likely is not where you installed R,
# but ideally you get the point. The difference is in how the shell scripts were written.
# }
Run the code above in your browser using DataLab