Efficient and Reproducible Project Management in R

by Jae Yeon Kim

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Overview

This workshop introduces tools and techniques to make a data science project efficient and reproducible in R. I recommend taking this workshop (1) if you have experienced difficulties organizing your project or (2) intend to share your code with other researchers (in a team or with the public). Science advances through the accumulation of reliable knowledge. A research project should be at the very least reproducible and, ideally, efficiently organized to make replication easy.

Learning objectives

• Part 1: Organizing files and code
• Part 2: Making a project computationally reproducible and self-contained

Prerequisites

Basic familiarity with R required.

Setup

1. Install the following two packages in R.

pacman::p_load(
  tidyverse, # tidyverse 
  here # computational reproducibility 
  )

2. Install RStudio and Git.

3. # a GitHub account (if you haven't) also don't forget to set up your user name and email.

4. In the terminal, type the following command:

git clone https://github.com/dlab-berkeley/efficient-reproducible-project-management-in-R

References

• Berkeley Research Data Management

• RStudio-Project-Management by Evan Muzzall

This work is licensed under a Creative Commons Attribution 4.0 International License.