EVENTS

Reproducible Research Techniques for Synthesis (July 2021)

Dates: July 8-9, 12-14, 2021
Location: Remote

This 5-day workshop will provide researchers with an overview of best data management practices, data science tools, and concrete steps and methods for more easily producing transparent, reproducible workflows. This opportunity is for researchers from across career stages and sectors who want to gain fundamental data science skills that will improve their reproducible research techniques, particularly for the purposes of synthesis science.

Curriculum at a glance:

Enable data reuse through better data management

  • Metadata - what is it and how to write a quality data description
  • Data modeling - tidy data for efficient access and storage
  • Data publishing, citation, and credit

Build reproducible scientific workflows

  • Data munging with R tidyverse
  • Working collaboratively - git and GitHub
  • Writing functions in R
  • Building packages for publishing reproducible research

Communicate results effectively

  • Literate analysis with RMarkdown
  • Publishing analytical web pages with GitHub pages
  • Data visualization with ggplot and leaflet

For more detailed information on how to prepare for the workshop, see preparing for the workshop (below).

Materials

Materials

Organizers

Name Email
Amber Budden aebudden@nceas.ucsb.edu
Jeanette Clark jclark@nceas.ucsb.edu
Bryce Mecum mecum@nceas.ucsb.edu

Preparing for the Workshop

Required software

We will primarily be using a web browser along with an instance of RStudio server set up especially for this course. However, we also recommend setting up R, RStudio, and git on your local system to more easily prepare you to utilize the skills you learned once the course ends.

  • R: We will use R version 4.0.2, which you can download and install from CRAN

  • RStudio: To download RStudio, visit RStudio’s download page.

  • R packages: We will be using the following packages:

    • devtools
    • dplyr
    • DT
    • ggplot2
    • leaflet
    • tidyr
    • scales
    • sf

    You can install these packages quickly by running the following two code snippets:

    packages <- c("devtools", "dplyr", "DT", "ggplot2", "leaflet", "tidyr", "scales", "sf")

    for (package in packages) {
  if (!(package %in% installed.packages())) {
    install.packages(package)
  }
}

  • git: Download git and install it on your system.

  • GitHub: We will be using GitHub so you will need create (or remember your existing) GitHub login

Refresh your skills

This workshop assumes a base level of experience using R for scientific and statistical analyses. However, we realize that not everyone will be at the same place in terms of familiarity with the tools we’ll be using. If you’d like to brush up on your R skills prior to the workshop, check out this list of resources we like:

If you’re a fan of cheat sheets, RStudio provides some fantastic ones on their Cheat Sheets page. Please make sure to print ahead of time if you prefer hard copies. In particular, check out:

  • Base R
  • Data Import
  • Data Transformation with dplyr
  • R Markdown
  • Data Visualization with ggplot2
  • R Markdown Reference Guide
  • The leaflet package