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.
- Access to browser window, RStudio Cloud account
- (Optional)Experience with the command line
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.)
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
Ahead of time
- Sign up with a free RStudio Cloud account
- Find the provided link from Slack or the introduction email to grant you access to the BVCN R lesson workspace in RStudio.
- 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.
Instructors: Ella Sieradzki
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.
Instructors: Sarah Hu
Goal: Continue learning how to manipulate data frames in R, but use the functions and syntax available through tidyverse.
Agenda
- View the recorded lecture for lesson-03 Link to Youtube recording
- 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()andsummarise()to calculate averages and relative abundance
Instructors: Philip Leftwich
Goal: Techniques and tools for dealing with NAs in your data set. And how to find them!
Instructors: Ella Sieradzki
Goal: Learn how to plot an XY scatter in base R and ggplot2 and become familiar with graphic parameters
Agenda
- Simple plotting of the Iris dataset
- Color points by series/group
- Adding margins and placing the legend outside of the plot
- par - the hub of graphic parameters
- Trendlines
Instructors: Sarah Hu & Philip Leftwich
Goal: Go over box plots and bar plots in ggplot. Making use of tidyverse data wrangling and exploring ggplot2 features.
Agenda
- ggplot2 bar plots and box plots
- Discussion of when a box plot vs. bar plot is more appropriate
- Play around with additional features for making figures: ggplot2 themes, adding colors for factors, multi-panel plots, and more!
- Import a test ASV table dataset and use ggplot to make a taxonomy relative abundance bar plot
Instructors: Jacob Cram
Instructors: Elizabeth Suter
- Amplicon crossover tutorial, see topic-amplicons for more. Using the output from qiime2 analysis
- Import ASV table into phyloseq
- Explore functionality of phyloseq: making tree, re-rooting tree, bar plot of taxa
- Ordinations with phyloseq: PCoA, weighted UniFrac PCoA, NMDS
Link to video
This is a cross over lesson with Amplicon lesson 5a
Instructors: Sarah Hu
- Discussion of compositional data, evidence of compositionality in tag-sequence data
- Data transformations: log-ratio transformations, Jaccard, Euclidean
- Screeplots, diagnosing how many dimensions are appropriate for visualizing your data
- PCA, PCoA, NMDS plots (2D and 3D)
- Reference for lesson
Live lessons in two parts:
Lesson 8b.1
Lesson 8b.2