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closes #20
This commit is contained in:
Natalie Elphick
parent
094d41cd0d
commit
fd2fc5b190
6 changed files with 1040 additions and 1502 deletions
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@ -1,7 +1,8 @@
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---
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title: "Introduction to R Data Analysis - Part 1"
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title: "Introduction to R Data Analysis"
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subtitle: "Part 1"
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author: "Natalie Elphick"
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date: "January 22nd, 2024"
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date: "May 20th, 2024"
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knit: (function(input, ...) {
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rmarkdown::render(
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input,
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@ -16,6 +17,7 @@ output:
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```{r, setup, include=FALSE}
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library(tidyverse)
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knitr::opts_chunk$set(comment = "")
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```
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##
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@ -25,10 +27,10 @@ library(tidyverse)
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## Introductions
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**Natalie Elphick**
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Bioinformatician I
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Bioinformatician I
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**Michela Traglia (TA)**
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Senior Statistician
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**Yihang Xin (Online TA)**
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Software Engineer III
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## Poll 1
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@ -36,7 +38,7 @@ Senior Statistician
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**What is your level of experience with coding/data analysis?**
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1. I know another data analysis programming language (Python, Matlab etc.)
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2. I can use Excel to do linear regression
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2. I can use Excel
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3. I know some R
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4. All of the above
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5. None of the above
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@ -50,8 +52,8 @@ Senior Statistician
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1. What is R and why should you use it?
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2. The RStudio interface
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3. File types
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4. Error messages
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5. Variables
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4. Variables
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5. Error and warning messages
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6. Types & data structures
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7. Math and logic operations
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8. Functions and packages
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@ -86,9 +88,9 @@ functionality
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# RStudio
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## RStudio
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- RStudio is an integrated development
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- RStudio is an integrated development
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environment (IDE)
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- It makes R code easier to write by providing a
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- It is an app that makes R code easier to write by providing a
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feature rich graphical user interface (GUI)
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<br>
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@ -131,8 +133,6 @@ feature rich graphical user interface (GUI)
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## Variable definition
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- Variables store information that is referenced and manipulated
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in a computer program
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- In contrast to the mathematical definition of a variable,
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variables in computer science are _mutable_
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- There are 3 ways to define variables in R, but one is preferred:
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```{r}
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x <- 1 # Preferred way
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@ -141,7 +141,59 @@ x = 1
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print(x)
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```
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## Variable naming
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## Example
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- Run the following in the R console:
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```{r}
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x <- 1
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y <- 4
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z <- y
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x + y + z
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```
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# Error and Warning Messages
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## Errors
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- **Errors**: Stop the execution of your code and must be fixed for the code to run successfully
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```{r, eval=FALSE}
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x <- 5
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y <- 10
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z <- x + a
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```
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```{r,echo=FALSE}
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message("Error: object 'a' not found")
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```
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## Common Errors
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- **Syntax Error:** Invalid R code syntax (e.g. misplaced parentheses)
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```{r,echo=FALSE}
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message('Error: unexpected ")"')
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```
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- **Object not found:** This variable is not defined (e.g. misspelled variables)
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```{r,echo=FALSE}
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message('Error: object "a" not found')
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```
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See this [article](https://statsandr.com/blog/top-10-errors-in-r/) for more common errors and how to fix them.
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## Warnings
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- Do not stop the execution but indicate potential issues that you should be aware of and might need to address
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```{r}
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a <- c(1, 2, 3, 4, 5)
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b <- c(6, 7, 8, 9)
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result <- a + b
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```
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## Variable Naming
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- Variables names must start with a letter and can contain
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underscores and periods
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@ -176,7 +228,7 @@ DogBreeds <- c("Labrador Retriever", "Akita", "Bulldog")
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## Data Types
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- Integer
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- Whole numbers (in R denote with L ex. 1L,2L)
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- Whole numbers (in R denoted with L ex. 1L,2L)
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- Numeric
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- Decimal numbers
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- Logical
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@ -191,7 +243,7 @@ DogBreeds <- c("Labrador Retriever", "Akita", "Bulldog")
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**Which of these is not the correct data type for the value?**
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1. 1.5 - Numeric
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2. "Labrador Retriever" - Character
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2. "1" - Character
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3. NA - Logical
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4. 1 - Integer
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@ -227,6 +279,8 @@ DogBreeds <- c("Labrador Retriever", "Akita", "Bulldog")
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countdown::countdown(minutes = 10,
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seconds = 0,
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color_border = "black",
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color_running_background = "#47d193",
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color_finished_background = "#a3184e",
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padding = "50px",
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margin = "5%",
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font_size = "5em",
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@ -297,6 +351,7 @@ x & !y
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execution of code
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```{r}
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dog_breeds <- c("Labrador Retriever", "Akita", "Bulldog")
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if ("Akita" %in% dog_breeds) {
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print("dog_breeds already contains Akita")
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} else {
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@ -379,11 +434,17 @@ library(ggplot2) # Makes all of the ggplot2 functions available
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- The tidyverse is a collection of commonly used data analysis
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packages
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- Learning curve is less steep
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- Lots of useful packages for data analysis
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- Lots of useful packages for cleaning and "wrangling" data into the correct format
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##
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## Why use Tidyverse Packages?
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- Most of the work in data analysis is getting data into the correct format to create outputs
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- The tidyverse collection of packages simplifies this process
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- Intuitive syntax
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- Comprehensive (data manipulation, cleaning, modeling and graphics)
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- Consistent data structure
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- Strong community support
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# End of Part 1
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@ -392,11 +453,8 @@ packages
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## Upcoming Workshops
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1. [Introduction to Statistics, Experimental Design, and Hypothesis Testing](https://gladstone.org/index.php/events/introduction-statistics-experimental-design-and-hypothesis-testing-0)
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- Jan 25, 2024 (Session 1 - 10am–12pm) (Session 2 - 1pm–3pm)
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- Jan 26, 2024 (Session 3 - 10am–12pm)
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[Single Cell ATAC-Seq Data Analysis Part 2](https://gladstone.org/events/single-cell-atac-seq-data-analysis-part-2-1)
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2. [Intermediate RNA-Seq Analysis Using R](https://gladstone.org/index.php/events/intermediate-rna-seq-analysis-using-r-4)
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- Feb 1, 2024 (9:30am-12:00pm)
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- Check [this link](https://gladstone.org/events?series=data-science-training-program) at the end of the summer for out fall workshop schedule
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@ -1,7 +1,8 @@
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---
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title: "Introduction to R Data Analysis - Part 2"
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title: "Introduction to R Data Analysis"
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subtitle: "Part 2"
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author: "Natalie Elphick"
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date: "January 23rd, 2024"
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date: "May 21st, 2024"
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knit: (function(input, ...) {
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rmarkdown::render(
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input,
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@ -19,6 +20,7 @@ library(kableExtra)
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library(tidyverse)
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library(readxl)
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theme_set(theme_grey(base_size = 16))
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knitr::opts_chunk$set(comment = "")
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```
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##
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@ -29,7 +31,10 @@ theme_set(theme_grey(base_size = 16))
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**Natalie Elphick**
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Bioinformatician I
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**Yihang Xin (TA)**
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**Michela Traglia (In Person TA)**
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Senior Statistician
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**Yihang Xin (Online TA)**
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Software Engineer III
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# Schedule
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@ -46,11 +51,11 @@ Software Engineer III
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- The tidyverse packages work well together because they share
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common data representations and design principles
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- Rows = observations, columns = variables
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- [ggplot2](), for data visualization.
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- [dplyr](), for data manipulation.
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- [tidyr](), for data tidying.
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- [readr](), for data import.
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- [purrr](), for iteration.
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- [ggplot2](https://ggplot2.tidyverse.org/), for data visualization.
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- [dplyr](https://dplyr.tidyverse.org/), for data manipulation.
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- [tidyr](https://tidyr.tidyverse.org/), for data tidying.
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- [readr](https://readr.tidyverse.org/), for data import.
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- [purrr](https://purrr.tidyverse.org/), for iteration.
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- and more..
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## dplyr
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## Example Dataframe
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- mpg is a dataframe built into the ggplot2 package
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```{r, eval = FALSE}
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```{r}
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head(mpg)
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```
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```{r, echo = FALSE}
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head(mpg) |>
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kable() |>
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kable_styling("striped") |>
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scroll_box(width = "100%")
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```
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## Select Columns
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```{r, eval = FALSE}
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```{r}
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select(.data = mpg,
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year, cty, hwy, manufacturer)
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```
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```{r, echo = FALSE}
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select(.data = mpg,
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year, cty, hwy, manufacturer) |>
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head() |>
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kable() |>
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kable_styling("striped") |>
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scroll_box(width = "100%")
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```
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## Filter Rows
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```{r, eval = FALSE}
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```{r}
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filter(.data = mpg,
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year == 2008)
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```
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```{r, echo = FALSE}
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filter(.data = mpg,
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year == 2008) |>
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head() |>
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kable() |>
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kable_styling("striped") |>
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scroll_box(width = "100%")
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```
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## Arrange Rows
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- desc() is used to arrange rows in descending order, the default is ascending
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```{r, eval = FALSE}
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```{r}
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arrange(.data = mpg,
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desc(cyl))
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desc(cty))
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```
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```{r, echo = FALSE}
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arrange(.data = mpg,
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desc(cyl)) |>
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head(n = 3) |>
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kable() |>
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kable_styling("striped") |>
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scroll_box(width = "100%")
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```
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## Summarising data
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- The dplyr **summarise()** function computes a table of
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summaries for a data frame
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different categorical groupings
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## Group and Summarise
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- Get the mean and median city mileage within manufacturer
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```{r, eval = FALSE}
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summarise(group_by(.data = mpg,
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manufacturer),
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@ -144,37 +124,27 @@ summarise(group_by(.data = mpg,
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```
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```{r, echo = FALSE}
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summarise(group_by(.data = mpg,
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summarise(.data = group_by(.data = mpg,
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manufacturer),
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mean_cty = mean(cty),
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median_cty = median(cty)) |>
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head() |>
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kable() |>
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kable_styling("striped") |>
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scroll_box(width = "100%")
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head(10)
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```
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## The pipe operator |>
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- Allows "chaining" of function calls to make code more readable
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```{r, eval = FALSE}
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mpg |>
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group_by(manufacturer) |>
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summarise(mean_cty = mean(cty),
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median_cty = median(cty))
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```
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```{r, echo = FALSE}
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```{r}
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mpg |>
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group_by(manufacturer) |>
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summarise(mean_cty = mean(cty),
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median_cty = median(cty)) |>
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head(n = 4) |>
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kable() |>
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kable_styling("striped") |>
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scroll_box(width = "100%")
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head(5)
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```
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# Plotting
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## ggplot2
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## Adding and Modifying Layers
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```{r, fig.dim=c(10,4)}
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ggplot(data = mpg,
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mapping = aes(x = class, y = cty, fill = class)) +
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geom_violin() +
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geom_boxplot(width = 0.1,
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fill = "white")
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ggplot(data = mpg,
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mapping = aes(x = cty, y = hwy)) +
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geom_point() +
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geom_smooth(formula = y ~ x, method = "lm")
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```
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countdown::countdown(minutes = 10,
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seconds = 0,
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color_border = "black",
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color_running_background = "#47d193",
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color_finished_background = "#a3184e",
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padding = "50px",
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margin = "5%",
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font_size = "5em",
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## Dataset Description
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- PanTHERIA
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- 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
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- Macroecological and macroevolutionary research projects
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- A global species-level data set of key traits of all known extant and recently extinct mammals compiled from literature
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- Used in macroecological and macroevolutionary research projects
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- Data is organized by taxonomic rank
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## Taxonomic Rank
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@ -252,10 +223,20 @@ read_xlsx("Intro_to_R_workshop_materials/PanTHERIA.xlsx") |>
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scroll_box(width = "100%")
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```
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## Hands-on Analysis
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- We will read in the data and explore if the trophic level has a significant impact on the adult body mass of mammals
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Steps:
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1. Combine and clean the data
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2. Visualize adult body mass by trophic level
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3. Check for overrepresented groups
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4. Fit a simple linear model
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## Hands-on Analysis
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- Open part_2.Rmd
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- If you just want to follow along and not run code, open part2_filled_out.html
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@ -263,63 +244,34 @@ read_xlsx("Intro_to_R_workshop_materials/PanTHERIA.xlsx") |>
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## General Tips
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- Follow any relevant institutional guidelines on using LLMs
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- Always confirm ChatGPT's outputs are correct
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- Provide as much detail as possible about the problem in the 1st prompt
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- Use separate chats for separate tasks/projects
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- Try the 'Custom Instructions' function that adds additional information to every prompt
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- Can visit webpages (GPT 4 only), which can help get more specific answers
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- Try the 'Custom Instructions' function
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## Code Tips
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- Commented R code yields better responses in my experience
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- Commented R code yields better responses
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- Provide the code and error message in the same prompt
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- ChatGPT can work well to convert syntax and improve your code:
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- "Turn this loop into a function : [your code]"
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- "Is there a better way to do this : [your code]"
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- Check out the file: `example_code/1_convert_syntax_example.R` for an example use case
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# Finding R Packages
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## Key Questions
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- What assay was the package designed for?
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- When was the last release?
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- Is it maintained (frequent updates)?
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- Does it work on all operating systems?
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- Are other people using it? (citations)
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- Do they respond to github issues?
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- Is there a benchmarking paper?
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## BioConductor and CRAN
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- Both of these have stringent requirements for packages they host (eg. for BioConductor they have to run on all major operating systems)
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- Prefer BioConductor packages if available over CRAN
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- Prefer CRAN packages over ones only hosted on GitHub
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## Start with the Assay
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- Click [here](https://www.bioconductor.org/packages/release/BiocViews.html#___Sequencing) to go to BioC views
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- Pick the assay you want to analyse
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- Pick the type of analysis you want to do
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- Find a package that does it
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- Find benchmarking papers to narrow the list of packages down
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- Find the vignette on the package page and refer to the manual for any questions not covered by it
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# Additional Resources
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## R
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|
||||
- [R Markdown: The Definitive Guide](https://bookdown.org/yihui/rmarkdown/how-to-read-this-book.html) : Excellent R markdown reference
|
||||
|
||||
- [R for Data Science](https://r4ds.hadley.nz/)
|
||||
|
||||
- [Top 10 R Errors and How to Fix them](https://statsandr.com/blog/top-10-errors-in-r/)
|
||||
- [R Markdown: The Definitive Guide](https://bookdown.org/yihui/rmarkdown/how-to-read-this-book.html) : Excellent R markdown reference
|
||||
- [ggplot2: elegant graphics for data analysis](https://ggplot2-book.org/)
|
||||
|
||||
- [Advanced R](https://adv-r.hadley.nz/)
|
||||
|
||||
|
||||
|
||||
## Statistics
|
||||
|
||||
- [Data Analysis in R](https://bookdown.org/steve_midway/DAR) : This book has more statistics details than *R for Data Science*
|
||||
|
|
@ -346,10 +298,9 @@ read_xlsx("Intro_to_R_workshop_materials/PanTHERIA.xlsx") |>
|
|||
|
||||
## Upcoming Workshops
|
||||
|
||||
1. [Introduction to Statistics, Experimental Design, and Hypothesis Testing](https://gladstone.org/index.php/events/introduction-statistics-experimental-design-and-hypothesis-testing-0)
|
||||
- Jan 25, 2024 (Session 1 - 10am–12pm) (Session 2 - 1pm–3pm)
|
||||
- Jan 26, 2024 (Session 3 - 10am–12pm)
|
||||
[Single Cell ATAC-Seq Data Analysis Part 2](https://gladstone.org/events/single-cell-atac-seq-data-analysis-part-2-1)
|
||||
|
||||
- Check [this link](https://gladstone.org/events?series=data-science-training-program) at the end of the summer for out fall workshop schedule
|
||||
|
||||
|
||||
2. [Intermediate RNA-Seq Analysis Using R](https://gladstone.org/index.php/events/intermediate-rna-seq-analysis-using-r-4)
|
||||
- Feb 1, 2024 (9:30am-12:00pm)
|
||||
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@
|
|||
},
|
||||
"MASS": {
|
||||
"Package": "MASS",
|
||||
"Version": "7.3-60",
|
||||
"Version": "7.3-60.0.1",
|
||||
"Source": "Repository",
|
||||
"Repository": "CRAN",
|
||||
"Requirements": [
|
||||
|
|
@ -33,11 +33,11 @@
|
|||
"stats",
|
||||
"utils"
|
||||
],
|
||||
"Hash": "a56a6365b3fa73293ea8d084be0d9bb0"
|
||||
"Hash": "b765b28387acc8ec9e9c1530713cb19c"
|
||||
},
|
||||
"Matrix": {
|
||||
"Package": "Matrix",
|
||||
"Version": "1.6-1.1",
|
||||
"Version": "1.6-5",
|
||||
"Source": "Repository",
|
||||
"Repository": "CRAN",
|
||||
"Requirements": [
|
||||
|
|
@ -50,7 +50,7 @@
|
|||
"stats",
|
||||
"utils"
|
||||
],
|
||||
"Hash": "1a00d4828f33a9d690806e98bd17150c"
|
||||
"Hash": "8c7115cd3a0e048bda2a7cd110549f7a"
|
||||
},
|
||||
"R6": {
|
||||
"Package": "R6",
|
||||
|
|
@ -872,7 +872,7 @@
|
|||
},
|
||||
"lattice": {
|
||||
"Package": "lattice",
|
||||
"Version": "0.21-9",
|
||||
"Version": "0.22-6",
|
||||
"Source": "Repository",
|
||||
"Repository": "CRAN",
|
||||
"Requirements": [
|
||||
|
|
@ -883,7 +883,7 @@
|
|||
"stats",
|
||||
"utils"
|
||||
],
|
||||
"Hash": "5558c61e0136e247252f5f952cdaad6a"
|
||||
"Hash": "cc5ac1ba4c238c7ca9fa6a87ca11a7e2"
|
||||
},
|
||||
"learnr": {
|
||||
"Package": "learnr",
|
||||
|
|
@ -977,7 +977,7 @@
|
|||
},
|
||||
"mgcv": {
|
||||
"Package": "mgcv",
|
||||
"Version": "1.9-0",
|
||||
"Version": "1.9-1",
|
||||
"Source": "Repository",
|
||||
"Repository": "CRAN",
|
||||
"Requirements": [
|
||||
|
|
@ -990,7 +990,7 @@
|
|||
"stats",
|
||||
"utils"
|
||||
],
|
||||
"Hash": "086028ca0460d0c368028d3bda58f31b"
|
||||
"Hash": "110ee9d83b496279960e162ac97764ce"
|
||||
},
|
||||
"mime": {
|
||||
"Package": "mime",
|
||||
|
|
@ -1033,7 +1033,7 @@
|
|||
},
|
||||
"nlme": {
|
||||
"Package": "nlme",
|
||||
"Version": "3.1-163",
|
||||
"Version": "3.1-164",
|
||||
"Source": "Repository",
|
||||
"Repository": "CRAN",
|
||||
"Requirements": [
|
||||
|
|
@ -1043,7 +1043,7 @@
|
|||
"stats",
|
||||
"utils"
|
||||
],
|
||||
"Hash": "8d1938040a05566f4f7a14af4feadd6b"
|
||||
"Hash": "a623a2239e642806158bc4dc3f51565d"
|
||||
},
|
||||
"openssl": {
|
||||
"Package": "openssl",
|
||||
|
|
|
|||
|
|
@ -129,4 +129,14 @@ small {
|
|||
.big-picture img{
|
||||
max-width: 70%;
|
||||
border: 1px solid black !important;
|
||||
}
|
||||
}
|
||||
|
||||
/* Chage link color to sky blue */
|
||||
.reveal a {
|
||||
color: #0c74dc;
|
||||
}
|
||||
|
||||
/* Change link color to magenta on hover */
|
||||
.reveal a:hover {
|
||||
color: #9c0366 !important;
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue