/2020_07_Assembling-experimental-datasets_Stuart-Allen

Code and data relating to the MQ R Users Group workshop on assembling experimental datasets

Primary LanguageR

2020_07_Assembling-experimental-datasets_Stuart-Allen

Workshop description

For a researcher, data is a valuable resource - especially if you have gone to the time and expense of collecting it yourself. Assembling datasets from your experiments is a crucial step towards answering your research questions. A well-constructed dataset makes analysis easier and increases the future utility of your data. This workshop will cover the nuts and bolts of assembling a dataset - a topic relevant to students and experienced researchers alike. Using relevant R functions and packages we will look at how to avoid common pitfalls, and how to implement assertive data validation to keep your data shipshape.

Code

R/assertr_demo.R

Demonstration of the assertr package for data validation.

R/dataspice_demo.R

Demonstration of the dataspice package for quick and easy creation of lightweight metadata.

Note: The dataspice package must be installed from GitHub, so you'll need to first install the devtools package:

#install.packages("devtools")
devtools::install_github("ropenscilabs/dataspice")

See the dataspice GitHub repository for more information.

Links

assertr

GitHub / vignette / reference

dataspice

GitHub / documentation

General

NHMRC Code for the Responsible Conduct of Research 2018

1,500 scientists lift the lid on reproducibility