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Introduction To RStudio For Life Sciences

Prerequisites

Learning objectives

Today we are going to learn about

  • project organization
  • R packages and how to access them on the tufts HPC
  • working with variables and data frames
  • visualizing data
  • and finally writing a markdown report of our findings

Once you have an account and are connected to the VPN/Tufts Network, navigate to the OnDemand Website and log in with your tufts credentials. Once you are logged in you'll notice a few navigation options:

Click on Interactive Apps > RStudio Pax and you will see a form to fill out to request compute resources to use RStudio on the Tufts HPC cluster. We will fill out the form with the following entries:

  • Number of hours : 3
  • Number of cores : 1
  • Amount of memory : 4GB
  • R version : 4.0.0
  • Reservation for class, training, workshop : Bioinformatics Workshops---> NOTE: This reservation closed on Nov 9, 2022, use Default if running through the materials after that date.
  • Load Supporting Modules: boost/1.63.0-python3 java/1.8.0_60 gsl/2.6

Click Launch and wait until your session is ready. Click Connect To RStudio Server, and you will notice a new window will pop up with RStudio.

Are you connected to RStudio?
  • Yes (put up a green check mark in zoom)
  • No (raise hand in zoom)

Introduction To RStudio

RStudio is what is known as an Integrated Development Environment or IDE. Here you can write scripts, run R code, use R packages, view plots, and manage projects. This pane is broken up into three panels:

  • The Interactive R console/Terminal (left)
  • Environment/History/Connections (upper right)
  • Files/Plots/Packages/Help/Viewer (lower right)

Project Management

Before we dive into R it is worth taking a moment to talk about project management. Often times data analysis is incremental and files build up over time resulting in messy directories:

Sifting through a non-organized file system can make it difficult to find files, share data/scripts, and identify different versions of scripts. To remedy this, It is reccomended to work within an R Project. Before we make this project, we should make sure you are in your home directory. To do this click on the three dots in the files tab:

Then enter in a ~ symbol to go home!

R Project

To Create a new R project:

  1. Go to File > New Project
  2. New Directory
  3. New Project
  4. Create a name for your project (e.g. R-Practice)
  5. Create Project

You will notice that your RStudio console switches to this project directory. When you log out of RStudio you can open this project again by clicking the .Rproj file in the project directory.

Note

The paths will be relative to this project directory as a safe guard against referencing data from outside sources.

Have you created the project?
  • Yes (put up a green check mark in zoom)
  • No (raise hand in zoom)

File Organization

  • You noticed now that you are inside your project folder
  • Let's start by creating some folders to you organize our files
  • In the files window click new folder and enter scripts
  • Let's do this again to create a data folder and a results folder

Data Principles

  • Treat data as read-only
  • Store raw data separately from cleaned data if you do need to manipulate it
  • Ensure scripts to clean data are kept in a separate scripts folder
  • Treat reproducible results as disposable

Tip

Result files are good candidate files to cut if you are getting low on storage.

Getting Data

  • Today we will be using a fake dataset assessing the taxa count on the mouse microbiome before and after antibiotic usage.
  • To copy over this data we will use an R function called file.copy.
  • A function takes some input and delivers an output.
  • In this case we specify two inputs the location of our file and where we want to copy it to.
  • The function's output is copying over this file. So let's try it copy over using the following commands:
file.copy(from="/cluster/tufts/bio/tools/training/intro-to-r/data/meta.tsv", to="./data/")
file.copy(from="/cluster/tufts/bio/tools/training/intro-to-r/data/meta2.tsv", to="./data/")

So here you'll note we copied over the file metadata.tsv to the data folder. Let's copy over our script:

file.copy(from="/cluster/tufts/bio/tools/training/intro-to-r/scripts/intro-to-r.Rmd", to="./scripts")

Here we copy over our script intro-to-r to the scripts folder.

Opening the Script

Now let's start by opening our script. Go to scripts and then double click on intro-to-r.Rmd!