1.1 Learning objectives

• Review the course topics and goals
• Start using R and Rstudio
• Start using Quarto documents in R as a reproducible method to present analyses along with narrative text

2.1 What is statistics? Interdisciplinary

• Mathematics
• Probability
• Uncertainty
• Decision making
• Programming / computer science
• Study design and methodology
• Integrated into science

2.2 What is statistics? People

• William Sealy Gosset
  • Guinness Brewing Company: Making better beer
  • \(t\)-distribution and \(t\)-test
• Ronald Fisher
  • Rothamsted Experimental Station: Agricultural questions
  • Analysis of variance (ANOVA) and \(F\)-distribution
  • Popularized the \(p\)-value and \(p < .05\) as a cut-off

2.3 What is statistics? Context

https://www.instagram.com/p/Bx8p0Pih6qs/

2.4 What is statistics? Context

• This thing is 8 feet tall. Is that tall?
  • For a person: Yes
  • For a building: No
• These two things are 12 inches different in height. Is that a lot?
  • For people: Yes
  • For buildings: No

2.5 When to think about statistics?

• Study design

• Data collection

• Data analysis
  
• Interpretation

2.6 Topics

• Reading in and manipulating data in R
• Making plots in R
• Descriptive statistics
• Probability, sampling distributions, uncertainty
• Comparing 2 means or proportions
• Controlling for multiple comparisons
• Comparing 3 or more means

2.7 What you’ll learn about

• Programming (in R)
• Probability and mathematics underlying statistical methods
• Conducting (some) statistical analyses
• Presenting results of statistical analyses
• Thinking about issues in science related to statistics

3.1 What is R?

• Free, open source software
  • Windows, Mac, Unix, cloud
• Object-oriented programming language
• Built-in capabilities (“packages”)
• Users can write their own packages and functions

3.2 What is R?

3.3 What does “object-oriented” mean?

• Programming built on the idea of “objects”
• Everything is an object
  • If you have an object’s name, you can do something with it
• Objects: Datasets, variables, analyses, plots

3.4 What does “object-oriented” mean?

x <- c(1, 1, 2, 3, 5, 8)
x

[1] 1 1 2 3 5 8

• Line 1
  • Define an object called x that is this set of numbers
  • <- is the assignment operator
• Line 2
  • Print the object x

3.5 What is a function?

• Pre-written piece of code that
  • Takes one or more inputs (or arguments)
  • Produces one or more outputs

3.6 What is a function?

mean(x)

[1] 3.333333

• Function: mean()
• Input: x
• Output: mean of x

Documentation for mean() function

3.7 What is a package?

• Collection of functions (that work together in some way)
  • dplyr package is a set of functions to manipulate data
  • ggplot package is a set of functions to create plots
  • stats package (built-in) is a set of functions to perform basic statistical operations

3.8 What is a package?

• To use functions in a package, you need to
  • Install the package
    • Just once per machine / R install
  • Load or library() the package
    • Every time you use it

3.9 What is Rstudio?

• Integrated development environment (IDE) for R
• Graphical user interface to use R
  • Manage installed packages
  • Connect to git / github
  • Easily see loaded datasets

3.10 What is Rstudio?

4.1 What is “markdown”?

• “Markup” language to format plain text
  • HTML: Hyper-Text Markup Language
• Headings, links, italics / bold, bullet points, equations
• Output final, formatted document into multiple formats
  • HTML, Word, PDF documents or slides
• Used across a wide variety of platforms, not just in R
  • Github, Jekyll, note-taking apps

4.2 markdown example

https://www.markdownguide.org/getting-started/#kicking-the-tires

4.3 markdown example: Organization

• Organize document with headings
  • # Level 1 heading
  • ## Level 2 heading
  • ### Level 3 heading
• Most documents can have a table of contents, which will use these headings
  • Slides have a table of contents accessed from bottom left

4.4 markdown example: Format text

• *italics* turns into italics
• **bold** turns into bold
• Mathematical text in between pairs of $
  • $R^2_{multiple}$ turns into \(R^2_{multiple}\)
  • $\hat{Y} = b_0 + b_1 X_1$ turns into \(\hat{Y} = b_0 + b_1 X_1\)

4.5 markdown example: Code chunks

```{r, echo = TRUE}
x <- c(1, 1, 2, 3, 5, 8)
mean(x)
```

• Intersperse text and code in the same document
  • Description of data and analysis
  • Analysis
  • Summarize the results

4.6 rmarkdown package

• Implements markdown in R
• Lets you create several document types
  • HTML or PDF or Word documents
  • Slides in HTML or PDF or Powerpoint
• Does all the heavy lifting to convert your markdown document to the final, formatted version

4.7 Quarto

• “Next gen” of markdown
• Works with multiple languages (even in the same document): R, Python, Julia
• Additional features
• Install Quarto from here
  • More general info here

5.1 In-class activities

• Any questions on the course topics and goals
• Start using R and Rstudio
• Start using Quarto in R as a reproducible method to present analyses along with narrative text