Intro to R & Data Science

Learning Outcomes

• Understand Data Science as the context for this course
• Understand R in the context of data science.

What is Data Science?

• Coined in 2001, “Data science is a discipline that incorporates varying degrees of Data Engineering, Scientific Method, Math, Statistics, Advanced Computing, Visualization, Hacker mindset, and Domain Expertise.” William S Cleveland¹

• “Data science, or data-driven science, combines different fields of work in statistics and computation to interpret data for decision-making purposes.” Caroline Banton²

• “Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.”

• “Data Scientist: The Sexiest Job of the 21st Century” THDJ Patil and T Davenport³

Components of Data Science

• Statistics
• Domain Knowledge
• Computation

Statistics

• Inferring general properties given data.
• Causal inference.
• Modeling (descriptive and predictive).
• Quantifying uncertainty.
• STAT 514, Statistical Methods, STAT 615 (Regression), STAT 627 (Machine Learning),
• Most of the STAT curriculum is applicable.

Domain Knowledge

• Expertise in an area of application
  • e.g., biology, psychology, economics, chemistry, etc..
• Allows you to understand data in context of the area and/or decision to be made.
• Lets you ask interesting questions.
• Lets you spot problems with existing analysis pipelines.
• Various “Tracks” in the data science program.

Computation – This class

• Data import
• Data preparation
• Data exploration
• Data transformation
• Data visualization
• STAT 612 (R programming), STAT 613 (Data Science), most of the CS curriculum.

Various Professions

What makes a data scientist?

• People in diverse professions use these three skills to analyze data.
• Professions often differ by their level of expertise or interest in each skill.
• Data Science projects usually a “team activity”

Introductions

• We’ll be doing group projects and you should form your groups of 2-4 people early in the semester.

• Let’s take a break to introduce ourselves. Turn on your cameras and say Hi and one or two sentences about yourself and your goals for the course.

The Steps of an Analysis and R

Steps of a data analysis

• Before you start your data analysis and R
  • Something is happening in the world
  • Someone collects data
  • Someone asks a question
• R Time

Tools

• Many tools exist for these steps:
• General data tools: R, Python, Julia, Matlab, STATA, SAS
• Other tools: SQL (data import), git (version control), map/reduce software (for big data).
• Advantages and disadvantages to each.

R

• R is a statistical programming (or scripting) language.
• You write code (a series of functions) to perform some task.
• R can be used to perform all of the tasks of a data analysis.
• R is built around the idea of packages: like apps
  • Packages are sets of functions designed to work together to accomplish a specific set of tasks
  • There are thousands of packages and you can install any one with a simple function install_packages()

Motivation for R

1. It’s free and open source.
   • You will always have access to R.
   • Not true for other software (Matlab, STATA, SAS).
2. It’s widely used with a lot of community support.
   • If you need some special analysis, someone has often made an R package.

1. It’s relatively easy (especially graphics and data wrangling).
   • “Evolution” driven by statisticians for local utility more than enterprise software

1. It enables reproducible research and analysis
   • Copying and pasting across spreadsheets can lead to mistakes - see Thomas Herndon, Michael Ash, and Robert Pollin⁴
   • In R, you can essentially automate your analysis, reducing the chance for mistakes and making your analysis transparent to the wider research community as well as reproducible.

What about Python??

• Python is also a very good language for data science.
• As a more general computer language it can be used for developing broader applications.
• Computer scientists tend to prefer it because its design and syntax is more like a standard computer language.
  • Can make it harder to learn for a non-programmer.
• Main reason to use either tool is based on the use case and your collaborators.

Two main flavors of R Users

• There are two main flavors of R programmers: Base R users and tidyverse users.

• Base R is the default system - it’s more general but not as intuitive or consistent as the tidyverse.

• tidyverse packages are much more convenient for the vast majority of tasks, as long as you drink the Koolaid.

  • They are not always the fastest but for many many uses and data sets they provide a convenient framework

This Class - See the Syllabus

Learning Outcomes

STAT 412/612 will Develop your competence, creativity, and confidence as a data scientist working with R so you can …

1. Execute a regular process to execute reproducible research and analysis using R and R Studio and communicate the results and implications to others.
2. Install and use R packages for specific applications
3. Import data from a variety of external sources
4. Use tidyverse capabilities to transform data to support analysis in R
5. Use tidyverse graphical tools to visualize and understand data
6. Write basic R functions using control and data structures
7. Employ R functions to conduct statistical analysis and inference
8. Generate research or analytical reports and presentations using R Markdown and basic LaTeX capabilities.
9. Deliver an oral presentation describing your data science analysis to an audience .

Books and Resources:

• All material used in this course is free and online.
  • R for Data Science: https://r4ds.had.co.nz/ Garrett Grolemund and Hadley Wickham⁵
  • Tidyverse Style Guide: https://style.tidyverse.org/ H Wickham⁶
  • RStudio Cheat Sheets: https://www.rstudio.com/resources/cheatsheets/

References