EVENTS

Fairbanks, Oct 2018: Reproducible analysis with R

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Dates: October 16-17, 2018
Location: Fairbanks, AK
Venue: UAF, International Arctic Research Center, room 417

This event will cover techniques for building reproducible analysis workflows using the R programming language through a series of hands-on coding sessions. We will use examples from integrating salmon brood data across the state of Alaska to show how heterogeneous data can be cleaned, integrated, and documented through workflows written in RMarkdown. After an overview of the use of RMarkdown for literate analysis, we will dive into critical topics in data science, including version control, data modeling, cleaning, and integration, and then data visualization both for publications and the web.

Prerequisites and Topics:

  • Prerequisite: familiarity with R and statistical analysis For more detailed information on how to prepare for the workshop, see Preparing for the Workshop
  • Open Science Tools to support synthesis of salmon data and management
  • Effective data management for salmon data management, analysis, and synthesis
  • Practical open science skills for salmon management

Registration instructions

  • Register online
  • Space is limited to 20-25 people, so higher priority to earlier registrants
  • If interest exceeds capacity, we will create a waitlist

Agenda
Tuesday, Oct. 16
8:30 - 9:30 Introduction and Motivation
9:30 - 10:00 R, RStudio, git Setup
10:00 - 10:30 Break
10:30 - 11:30 RMarkdown
11:30 - 12:30 git/GitHub
12:30 - 1:30 Lunch
1:30 - 2:30 Data Modeling
2:30 - 3:30 Tidyverse
3:30 - 4:00 Break
4:00 - 5:00 Tidyverse contd.
Wednesday, Oct. 17
8:30 - 10:00 Hands-On Data Tidying
10:00 - 10:30 Break
10:30 - 12:00 Data Visualization
12:00 - 1:00 Lunch
1:00 - 2:00 Git: Collaboration and Conflict Management
2:00 - 3:00 Data Documentation/Publishing
3:00 - 3:30 Break
3:30 - 5:00 Publishing Analysis to the Web

Preparing for the Workshop

Required software

We will primarily be using a web browser, R, RStudio, and git. Please be sure these are all installed on your laptop, as follows:

  • R: We will use R version 3.5.1, which you can download and install from CRAN

  • RStudio: To download RStudio, visit RStudio’s download page. If you don’t know how up to date your version of RStudio is, please download an updated copy and install it

  • R packages: Please be sure you have installed or updated the following packages:

    • datapack
    • devtools
    • dplyr
    • DT
    • ggplot2
    • ggpmisc
    • kableExtra
    • leaflet
    • readxl
    • tidyr
    • ggmap
    • sf
    • raster

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

    
packages <- c( "devtools", "dplyr", "DT", "ggplot2", "ggpmisc", 
               "kableExtra", "leaflet", "readxl", "remotes", "tidyr",
               "ggmap", "sf", "raster")

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

Organizers

Name Email
Matthew Jones jones@nceas.ucsb.edu
Jeanette Clark jclark@nceas.ucsb.edu
Jorge Cornejo cornejo@nceas.ucsb.edu

Acknowledgements

These training events were supported by:

The Gordon and Betty Moore Foundation, Salmon Data Task Force award to M. B. Jones and I. Dutton.

Additional support was provided for collaboration by the National Center for Ecological Analysis and Synthesis, a Center funded by the University of California, Santa Barbara, and the State of California.