Tidyverse

• The tidyverse packages work well together because they share common data representations and design principles
  • Rows = observations, columns = variables
• ggplot2, for data visualization.
• dplyr, for data manipulation.
• tidyr, for data tidying.
• readr, for data import.
• purrr, for iteration.
• and more..

dplyr

• Offers a common “grammar” of functions for data manipulation
  • mutate() adds new variables that are functions of existing columns
  • select() picks columns based on their names
  • filter() picks rows based on their values
  • summarise() reduces multiple values down to a single summary
  • arrange() changes the ordering of the rows
  • group_by() allows any operation to be done “by group”

Example Dataframe

• mpg is a dataframe built into the ggplot2 package

head(mpg)

Select Columns

select(.data = mpg,
       year, cty, hwy, manufacturer)

Filter Rows

filter(.data = mpg,
       year == 2008)

Arrange Rows

• desc() is used to arrange rows in descending order, the default is ascending

arrange(.data = mpg,
        desc(cyl))

Summarising data

• The dplyr summarise() function computes a table of summaries for a data frame
• group_by() groups the input data frame by the specified variable(s)
• Combining these two allows us to easily create summaries for different categorical groupings

Group and Summarise

summarise(group_by(.data = mpg,
                   manufacturer),
          mean_cty = mean(cty),
          median_cty = median(cty))

The pipe operator |>

• Allows “chaining” of function calls to make code more readable

mpg |>
  group_by(manufacturer) |>
  summarise(mean_cty = mean(cty),
            median_cty = median(cty))

ggplot2

• The most popular tidyverse package
• Create publication quality, highly customizable plots
  • See the R graph gallery for examples
• ggplots use “layers” to build, modify and overlap visualizations
  • Layers are added using the + symbol and can be added to an existing ggplot
• Many popular packages output ggplots which can then be easily modified by adding layers

Creating ggplots

Plot Example

ggplot(data = mpg,                         # Input dataframe
       mapping = aes(x = cty, y = hwy)) +  # Aesthetic mapping
  geom_point()                             # Point graph

Adding and Modifying Layers

ggplot(data = mpg,
       mapping = aes(x = class, y = cty, fill = class)) +
  geom_violin() +
  geom_boxplot(width = 0.1,
               fill = "white")

Dataset Description

• PanTHERIA
  • A global species-level data set of key life-history, ecological and geographical traits of all known extant and recently extinct mammals compiled from the literature
  • Macroecological and macroevolutionary research projects
  • Data is organized by taxonomic rank

Taxonomic Rank

Data Preview

Hands-on Analysis

• Open part_2.Rmd

General Tips

• Always confirm ChatGPT’s outputs are correct
• Provide as much detail as possible about the problem in the 1st prompt
• Use separate chats for separate tasks/projects
• Try the ‘Custom Instructions’ function that adds additional information to every prompt
• Can visit webpages (GPT 4 only), which can help get more specific answers

Code Tips

• Commented R code yields better responses in my experience
• Provide the code and error message in the same prompt
• ChatGPT can work well to convert syntax and improve your code:
  • “Turn this loop into a function : [your code]”
  • “Is there a better way to do this : [your code]”
• Check out the file: example_code/1_convert_syntax_example.R for an example use case

Key Questions

• What assay was the package designed for?
• When was the last release?
• Is it maintained (frequent updates)?
• Does it work on all operating systems?
• Are other people using it? (citations)
• Do they respond to github issues?
• Is there a benchmarking paper?

BioConductor and CRAN

• Both of these have stringent requirements for packages they host (eg. for BioConductor they have to run on all major operating systems)

• Prefer BioConductor packages if available over CRAN

• Prefer CRAN packages over ones only hosted on GitHub

Start with the Assay

• Click here to go to BioC views
• Pick the assay you want to analyse
• Pick the type of analysis you want to do
• Find a package that does it
• Find benchmarking papers to narrow the list of packages down
• Find the vignette on the package page and refer to the manual for any questions not covered by it

R

• R Markdown: The Definitive Guide : Excellent R markdown reference

• R for Data Science

• ggplot2: elegant graphics for data analysis

• Advanced R

Statistics

• Data Analysis in R : This book has more statistics details than R for Data Science
• Generalized Linear Models
  
• Random Effects

RNA-seq Analysis

• RNA-seqlopedia : Everything you need to know about RNA-seq experiments
• RNA-seq Expression Units : Blog post on understanding common units
• Introduction to Single-Cell Analysis with Bioconductor : Covers the basics of scRNA-seq analysis in R

Dimensional Reduction

• Tutorial on PCA : PCA explained with R code examples
• Understanding UMAP : Short explanation with great visualizations, mainly useful for scRNA-seq analysis

Workshop survey

• Please fill out our workshop survey so we can continue to improve these workshops

Upcoming Workshops

1. Introduction to Statistics, Experimental Design, and Hypothesis Testing
   • Jan 25, 2024 (Session 1 - 10am–12pm) (Session 2 - 1pm–3pm)
   • Jan 26, 2024 (Session 3 - 10am–12pm)
2. Intermediate RNA-Seq Analysis Using R
   • Feb 1, 2024 (9:30am-12:00pm)