lessons.qmd

---
title: "Session Materials"
---
# Lesson 1

## Agenda

1. Speakers Introduction. 
2. Website and Code of Conduct Overview
3. R Knowledge and Experience Assessment ~ *"We will go as fast as the slowest person in the room"*
4. R Exploration 
5. How to work within a project
6. Open a quarto file
7. Data Import
8. Types of data 
9. Respond our survey 

## How to download R and R studio (We need both)

- [For Windows](https://derailment.netlify.app/2019-12-10-installing-r-rstudio-on-windows/)
- [For Mac](https://derailment.netlify.app/2019-11-16-installing-r-rstudio-on-a-mac/)


## Data Import

1. Click on this link and download the data:
[Taylor Data](https://docs.google.com/spreadsheets/d/11jbQwfSBr-pHEap2fs8_JvYgipbtDsps/edit?usp=sharing&ouid=101037354673858862140&rtpof=true&sd=true)

2. Store it in your `raw_data` folder

3. [Data dictionary](https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-10-17/readme.md)

## Types of Data 

**Integer:** Integers are whole numbers without any decimal or fractional parts. They are used to represent counts or discrete quantities. In R, integer values are represented by the integer data type.

**Numeric (or Double):** Numeric data, also known as double precision floating-point numbers, can store both integer and decimal values. Numeric data is commonly used for continuous measurements and calculations. In R, numeric values are represented by the numeric data type.
Character: Character data, also known as string data, represents text values. Character data is used to store text or categorical information. For example, names, addresses, categories, etc.

**Logical (LGL):** Logical data represents binary values, typically TRUE or FALSE. Logical data is often used for conditions or Boolean operations. For example, whether an event occurred (TRUE) or not (FALSE), whether a condition is met (TRUE) or not (FALSE), etc.

**Factor:** Factor data is used to represent categorical variables with a fixed number of levels or categories. Factors are stored as integers, where each integer corresponds to a specific level or category. Factor data is useful for statistical analysis and plotting categorical data. For example, gender (Male, Female), education level (High School, College, University), etc.

## Resources we talked about today

- [R for Data Science](https://r4ds.hadley.nz/)
- [Cheatsheets](https://posit.co/resources/cheatsheets/)
- [R Medicine 2024](https://rconsortium.github.io/RMedicine_website/)