6 Lesson 1: R Project Development

6.1 Overview

• Easier to access files (relative path)
  • Relative paths help prevent broken paths
    • In general, DO NOT use setwd()
    • Will work if the project folder is moved to a new location on you local machine or moved to a new machine.
• Designed to easily integrate with version control (GIT)
• In general, all data, scripts, and output should be stored within the project directory.

6.2 Create a New R Project

1. Create a new R project by clicking on the drop-down menu at the top right of RStudio and selecting “New Project”

2. Select “New Directory” within the “Create Project” window

3. Select “New Project” within the “Project Type” window

4. Enter a project name(below I have given the name “new_project”), the project directory (where the project should live), and select “Create Project”
   • Tip: Create a “project” folder that will act as your parent directory for all R projects. This will make it much easier to navigate to and between projects.

5. A new session specific to your R project will start within RStudio
   • There are a number of ways to tell which project is open…

6.3 Navigate Between Projects

Quickly navigate between recently opened R projects by clicking on the drop-down menu at the top right of RStudio and selecting the project of interest.

6.4 Recommended Workflow

1. Set up a GitHub repository.
2. Create an R-project connected to the GitHub repository.
3. Develop R-scripts.
4. Push and pull project changes to and from GitHub.

6.5 Your Turn

1. Open R Studio.
2. Create a new Project.
3. Add an “data” folder and add the example data set to this folder.
4. Create a new R Script (Ctrl + Shift + N) and save the script as “lesson_1.R” to your project root folder.
5. Add the following script and run it (Ctrl + Enter)
   • Notice that the entire file path to CSV of interest (“zms_thesis_metrics.csv”) was not specified just file.path("data", "zms_thesis_metrics.csv").

thesis.df <- read.csv(file.path("data",
                                "zms_thesis_metrics.csv"),
                      stringsAsFactors = FALSE)

head(thesis.df)

6. View thesis.df in the Environment panel
   • How many rows and columns does our data frame have?
   • Click on the drop-down menu (the blue circle to the left of thesis.df).
     • Can you identify the column type (e.g., Character, Numeric, Integer, or Factor) of the columns unique_id, substrate_size_d50, richness, and pct_diptera?
     • What are the first, second, and third unique_id’s represented in the data frame and which richness values are associated with each unique_id?
   • Click on the button to view the data (the small girdded table to the far-right of thesis.df)
     • Filter the data frame to only see data from Onondaga Lake (lake = “onon”)