christrivedi / topic-r

A repository for the lessons and notebooks used for R tutorials.

TOPIC: R

A repository for the lessons and tutorials for the R TOPIC channel of the BVCN.
Each week we will debut a new lesson with a video tutorial and a set time for a live demonstration. Additionally, we will work to hold R Office hours each week to review the material, go into more detail on what we've learned, and try to answer any questions you may have.

For a full list of resources also see the R topic BVCN Wiki page.

Prerequisites

• Access to browser window, RStudio Cloud account
• (Optional)Experience with the command line

Overview

This BVCN topic will cover:

• the fundamentals of coding in the R language
• methods for importing data to R and data wrangling
• methods for data analysis
• troubleshooting your own R code
• how to generate graphs or figures in R
• the use of R in the context of analyzing commonly encountered data types (e.g., amplicon sequence results, differential expression, etc.)

Lesson 1

Title: Introduction to R & Connect to RStudio Cloud

Instructors: Sarah Hu, Liz Suter, Alexis Marshall

Goals:

• Introduce the R language
• Connect to RStudio Cloud
• Copy and move your first R lesson to your RStudio Cloud workspace
• Learn basic operators and R syntax

Lesson 1 agenda

Ahead of time

1. Sign up with a free RStudio Cloud account
2. Find the provided link from Slack or the introduction email to grant you access to the BVCN R lesson workspace in RStudio.
3. Watch this tutorial intro to RStudio and how we will navigate lessons Link to YouTube video

Live lesson 4. Review RStudio navigation and what was covered in the tutorial video 5. Work through basic functions in the Lesson01 R project

Link to live Lesson 1 recording

Orientation in RStudio Cloud

• Workspaces: This is your own personal workspace or “sandbox”. You can make them private or public. The BVCN lessons will be listed each week in the BVCN-R workspace.
• Projects: This is a list of R Projects that you can open in RStudio Cloud. Each week, you can copy the lessons from the shared BCVN Workspace to your personal list of projects
• To execute a command from an R script (which will be sent to the Rconsole), on a Mac: press COMMAND+ENTER and on a PC it is CTRL+ENTER

What is RStudio Cloud? RStudio itself is also an application that can be installed on your computer that is an easy to use interface for writing and executing R code. RStudio Cloud is a cloud-based platform to run RStudio. It circumvents the installation of R on your local computer, to minimize any issues and frustrations that may arise when learning and accessing R remotely. Another reason we are using Rstudio Cloud is that it will be a streamlined way for us to share code with you as we venture into learning the R language.

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Lesson 02

Title: Dealing with data frames

Instructors: Ella Sieradzki

Link to live lesson

Goal: In this lesson we'll see how to manipulate tables in R just like you would in Excel. We will learn how to sort a table, filter it, transpose it, change column names and merge it with another table.

Agenda

• View this recorded lecture for lesson-02 Link to Youtube recording
• Basic data table manipulation
• Import test data into R from .csv format
• Applying basic mathematical functions to a data table
• Merge data table together

Take home messages

• In Excel you are working on a single document and modifying it, while in R, the best practice when transforming data is to create a new object in R. This way you have traces of your previous table. This allows for a more reproducible product and reduces those errors that happen when you copy and paste cells in Excel.

Lesson 3

Title: Introduction to Tidyverse

Instructors: Sarah Hu

Goal: Continue learning how to manipulate data frames in R, but use the functions and syntax available through tidyverse.

Agenda

• Link to recorded intro to the lesson
• Overview of data frames, matrices, and tibbles
• Change column headers and reorder columns in base R
• Repeat column renaming and reorder in tidyverse
• Subset and filter data in base R, repeat in tidyverse
• Wide vs. long format data
• Separating (parsing) column header into multiple columns
• Introduce mutate() and summarise() to calculate averages and relative abundance