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+ Introduction to R Data Analysis - Part 1
+Natalie Elphick
+January 22nd
+Introductions
+Natalie Elphick
+Bioinformatician I
+Bioinformatics Core
Poll 1
+What is your level of experience with coding/data +analysis?
+-
+
- I am fluent in another data analysis programming language (Python, +Matlab etc.) +
- I am use Excel to do linear regression +
- I know some R +
- All of the above +
- None of the above +
Part 1:
+-
+
What is R and why should you use it?
+The RStudio interface
+File types
+Error messages
+Variables
+Types & data structures
+10 min break
+Math and logic operations
+Functions and libraries
+Reading data into R
+
What is R?
+ +R
+-
+
- An open source language developed for statistical computing by +Ross Ihaka and Robert Gentleman +
- Inspired by the S language developed at Bell labs +in 1976 to make interactive data analysis easier +
- The first official version was released in 2000 +
Why use R for data analysis?
+-
+
- R is and will always be free +
- Can easily implement any statistical analysis +
- Code serves as a record which enables reproducibility with minimal +effort +
- As of March 2023, there were over 19,000 open source packages to +extend its functionality + +
How does it work?
+
+RStudio
+ +RStudio
+-
+
- RStudio is an integrated development environment (IDE)
+
+ - It makes R code easier to write by providing a feature rich +graphical user interface (GUI) +
+Layout
+
File types
+-
+
- Rscript files that end in
.R+-
+
- The most basic, a file that contains R code +
+ - RMarkdown files that end in
.Rmd
+ - Let’s create a blank Rscript to see how they work, open RStudio and
+click:
+
-
+
- File -> New File -> R Script +
+
R Markdown
+-
+
- A file format combining
Rcode with Markdown for text +formatting.
+ - Designed for creating reproducible research reports in various +formats (HTML, PDF, Word). +
- Let’s create an
Rmdfile inRStudioto +explore the basics of how they work:
+ - File -> New File -> R Markdown +
R Markdown Advanced Usage
+ +Variables
+ +Variable definition
+-
+
- Variables store information that is referenced and manipulated in a +computer program +
- In contrast to the mathematical definition of a variable, variables +in computer science are mutable +
- There are 3 ways to define variables in R, but one is +preferred: +
## [1] 1Variable naming
+-
+
- Variables names must start with a letter and can contain underscores +and periods +
- It is best practice to use descriptive variable names and stick to +one style of names +
Poll 2
+Which variable name is not valid in R?
+-
+
- cat_dog +
- CatDOG +
- cat.dog +
- catD0g +
Excercise 1
+-
+
- Open Rscript file part_1.R in Rstudio +
Data Types and Structures
+ +Data Types
+-
+
- Integer
+
-
+
- Whole numbers +
+ - Numeric
+
-
+
- Decimal numbers +
+ - Logical
+
-
+
- Boolean (TRUE, FALSE) +
- NA +
+ - Character
+
-
+
- Letters and strings of letters +
- “A”, “Labrador Retriever” +
+
Data Structures
+-
+
- Vectors
+
-
+
- Atomic vectors - one dimensional lists that store values of the +same type +
- Lists - can be multidimensional and contain different +types/structures (ex. nested lists) +
+ - Factors
+
-
+
- Ordered list with assigned levels +
+ - Matrix
+
-
+
- Columns and rows of the same type +
+ - Data frames
+
-
+
- Columns and rows of mixed types +
+
Exercise 2: Data Types and Structures
+-
+
- Reopen Rscript file part_1.R in Rstudio +
10 min break
+
+
+
+10:00
+Math and Logic Operations
+ +Math & Logic
+-
+
- Built in functions to get common mathematical summaries of data (eg. +mean( ), median( ), mode( ) ) +
- Relational comparison operators to compare values +
Logical Operators
+-
+
- Logical operators can compare TRUE or FALSE values +
Conditional execution
+-
+
- Relational and logical operations allow for conditional execution of +code +
if ("Akita" %in% dog_breeds) {
+ print("dog_breeds already contains Akita")
+} else {
+ dog_breeds <- c("Akita", dog_breeds)
+}## [1] "dog_breeds already contains Akita"Functions
+ +Functions
+-
+
- A function is block of organized, reusable code that is used to +perform a single action +
- R has many built in functions, these are called base +R functions +
- Not all arguments are required and some have default values +
Defining a function
+-
+
- To define a function we use the function keyword, the output is +specified with the return keyword: +
Example
+ +## Already contains this dog## [1] "German Shepard" "Labrador Retriever" "Akita"
+## [4] "Bulldog"Packages
+ +Packages
+-
+
- Packages are collections of functions that are specialized to a +specific task (plotting, data manipulation etc.) +
- The tidyverse is a collection of commonly used data analysis +packages - Learning curve is less steep - Lots of useful packages for +data analysis +
End of Part 1
+ +Workshop survey
+-
+
- Please fill out our workshop survey so we +can continue to improve these workshops +
Upcoming Workshops
+-
+
- 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) +
+ - Intermediate
+RNA-Seq Analysis Using R
+
-
+
- Feb 1, 2024 (9:30am-12:00pm) +
+
ChatGPT Tips for R
+ +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.Rfor an example use +case
+
Finding R Packages
+ +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 +
Additional Resources
+ +R
+-
+
R +Markdown: The Definitive Guide : Excellent R markdown +reference
+- +
- +
- +
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 +