EDUC 260A

EDUC 260A
Introduction to Programming and Data Management

Promo Video

Fall 2024

This is the first course in a programming/data science sequence designed for students who do not have a programming background and perhaps never thought they would write code. (The second course in the sequence is EDUC 260B: Fundamentals of Programming)

The course has two foundational goals: (1) to develop core skills in "data management," which are important regardless of which programming language you use, and (2) to learn the fundamentals of the R programming language.

Data management consists of acquiring, investigating, cleaning, combining, and manipulating data. Most statistics courses teach you how to analyze data that are ready for analysis. In real research projects, cleaning the data and creating analysis datasets is often more time consuming than conducting analyses. This course teaches the fundamental data management and data manipulation skills necessary for creating analysis datasets.

Syllabus & Resources

Class materials including the syllabus, class Zoom link, GitHub issues, GitHub teams, and course textbook are linked below.

Lecture Materials

Lecture materials are organized by topic. All lectures will have associated materials for that lecture topic linked below. This includes lecture slides (html/pdf), the R markdown file used to create the slides (.Rmd), and pre-recorded lecture videos.

Introduction to R

HTML

Introduction to R

.qmd

Introduction to R

Video recordings

Fall 2021

Fall 2022

Investigating data patterns in Base R

HTML

Investigating data patterns in Base R

.qmd

Investigating data patterns in Base R

Video recordings

Fall 2021

Fall 2022

Into the tidyverse: pipes, dplyr, and variable creation

HTML

Into the tidyverse: pipes, dplyr, and variable creation

.qmd

Into the tidyverse: pipes, dplyr, and variable creation

Video recordings

Fall 2021

Fall 2022

Intro and Select Variable
Pipes

Across the tidyverse: processing across rows

HTML

Across the tidyverse: processing across rows

.qmd

Across the tidyverse: processing across rows

Video recordings

Fall 2021

Attributes and class

HTML

Attributes and class

.qmd

Attributes and class

Video recordings

1.1 & 1.2 attributes and class - Week 7 (11/05/22)
2.0 attributes and class object class - Week 7 (11/05/22)
3.0 attributes and class factor class - Week 7 (11/05/22)
4.0 attributes and class labelled class - Week 7 (11/05/22)
4.1 attributes and class get labels - Week 7 (11/05/22)
4.2 attributes and class set labels - Week 7 (11/05/22)
5.0 attributes and class compare labels - Week 7 (11/05/22)

Strings and dates

HTML

Strings and dates

.qmd

Strings and dates

Video recordings

1 Introduction & 2 Data structures and types - Week 5 (10/28/22)
3 String basics & 4.1 str_length - Week 5 (10/28/22)
4.2 str_c() - Week 5 (10/28/22)
4.3 str_sub() - Week 5 (10/28/22)
5.1.1 Creating date/time objects by parsing input - Week 5 (10/28/22)
5.1.2 Create date/time objects from individual components - Week 5 (10/28/22)
5.2 Accessing components of date/time objects - Week 5 (10/28/22)

Visualizations with gpplot2

HTML

Visualizations with gpplot2

.qmd

Visualizations with gpplot2

Video recordings

Lecture for Week 6, Visualizations using ggplot: Section 1 (01/14/22) - Introduction
Lecture for Week 6, Visualizations using ggplot: Section 2 (01/14/22) - Concepts
Lecture for Week 6, Visualizations using ggplot: Section 3.1 (01/14/22) - ggplot() and aes() functions
Lecture for Week 6, Visualizations using ggplot: Section 3.2 (01/14/22) - Adding geometric layers
Lecture for Week 6, Visualizations using ggplot: Section 3.3 (01/14/22) - Small multiples using faceting
Lecture for Week 6, Visualizations using ggplot: Section 4.1 (01/14/22) - Labels
Lecture for Week 6, Visualizations using ggplot: Section 4.2 (01/14/22) - Scales
Lecture for Week 6, Visualizations using ggplot: Section 4.3 (01/14/22) - Colors
Lecture for Week 6, Visualizations using ggplot: Section 4.4 (01/14/22) - Themes
Lecture for Week 6, Visualizations using ggplot: Section 5 (01/14/22) - Exporting plots

Tidy data

HTML

Tidy data

.qmd

Tidy data

Video recordings

1.0 introduction - Week 9 (11/18/22)
2.0 data structure & data semantics - Week 9 (11/18/22)
3.1 defining tidy data & 3.2 diagnosing untidy data - Week 9 (11/18/22)
3.3 Common types untidy data - Week 9 (11/18/22)
4.1 reshaping wide to long - Week 9 (11/18/22)
4.2 reshaping long to wide - Week 9 (11/18/22)
5.0 missing values - Week 9 (11/18/22)

Joining data

HTML

Joining data

.qmd

Joining data

Video recordings

Lecture for Week 10, Part 1 (12/2/22) - Introduction to joins (Section 1)
Lecture for Week 10, Part 2 (12/2/22) - Keys(Section 2)
Lecture for Week 10, Part 3 (12/2/22) - Join Data by Multiple Variables(Section 3.1.2)
Lecture for Week 10, Part 4 (12/2/22) - Inner Joins(Section 3.1)
Lecture for Week 10, Part 5 (12/2/22) - Outer Joins(Section 3.2)
Lecture for Week 10, Part 6 (12/2/22) - Filtering Joins(Section 4)
Lecture for Week 10, Part 7 (12/2/22) - Appending Data(Section 5)
R Class 2 - Preview

Data quality (Optional)

HTML

Data quality

.qmd

Data quality

Video recordings

Lecture for Week 8, Part 1 (11/11/22) - Introduction and Guidelines for EDA
Lecture for Week 8, Part 2 (11/11/22) - Data Quality Section 3a: One Way Continuos
Lecture for Week 8, Part 3 (11/11/22) - Data Quality Section 3b: One Way Categorical
Lecture for Week 8, Part 4 (11/11/22) - Data Quality Section 3c: EDA Two Way

Readings & Assignments

Weekly pre-recorded lectures, slides (html/pdf), readings, and problem sets will be posted on the class website a week in advance. Students are expected to work through the asynchronous lecture materials (videos, slides, etc.) and submit the problem set prior to the synchronous class meeting on Friday.

Week 1 (Do before 10/04/24 class)

Required readings

From Introduction to R lecture:

Section: Executing R commands
Section: R objects and data structures

"Matrices" and "Practical example" sub-sections are optional

Section: Using functions
Optional: Directories
Appendix Section: Create "R project"

Section 3.1 from R Markdown: The Definitive Guide
Syllabus

Encouraged readings

Chapter 4 from R for Data Science by Grolemund and Wickham
Chapter 20.1 - 20.3 from R for Data Science
Spend 10 minutes on Mastering Issues on GitHub
Watch Videos: on relative and absolute filepaths

Absolute versus relative file paths Youtube link
Relative paths and working directory in R Youtube link

Problem set 1 due 10/04/24 @ 9am

Problem set files

Week 2 (Do before 10/11/24 class)

Required readings

From Investigating data patterns in Base R:

Section 1: Investigate objects, base R
Section 2.1: Subset atomic vectors using []
Section 2.2: Subsetting lists/data frames using []
Section 2.3: Subsetting lists/data frames using [[]] and $

Reading about R Markdown
- Section 3.3 from R Markdown: The Definitive Guide
- Spend 10 minutes on R Markdown Reference Guide

Encouraged readings

Chapter 27 from R for Data Science
Chapter 4.1 - 4.4 from Advanced R

Problem set 2 due 10/11/24 @ 9am

Problem set files

Week 3 (Do before 10/18/24 class)

Required readings

From Into the tidyverse: pipes and dplyr:

Section 1: Introduction (1.1, 1.2)
Section 2: Investigating data patterns (2.1, 2.2, 2.3)
Section 3: Pipes (3.1, 3.2, 3.3, 3.4, 3.5, 3.6)

Encouraged readings

From Investigating data patterns in Base R:

Section 3: Subset using subset() function

Sections 5.1 - 5.4 from R for Data Science
Appendix on sorting data Investigating data patterns in Base R:

Appendix 5.1: Sorting data

Problem set 3 due 10/18/24 @ 9am

Problem set files

Week 4 (Do before 10/25/24 class)

Required readings

From Into the tidyverse: pipes, dplyr, and variable creation:

Section 1.2: Data for lecture sections on pipes and mutate() function
Section 3: Pipes
Section 4: Creating variables using mutate()
Section 4: Creating variables

Encouraged readings

Section 5.5 from R for Data Science
From Investigating data patterns in Base R:

Section 4: Creating variables

Problem set 4 due 10/25/24 @ 9am

Problem set files

Week 5 (Do before 11/01/24 class)

Required readings

From Across the tidyverse: processing across rows:

Section 1: Introduction
Section 2: Introduce group_by() and summarize()
Section 3: Combining group_by() and summarize()
Section 4: Summarize multiple columns

Encouraged readings

From Across the tidyverse: processing across rows:

Section 5: Attach aggregate measures to your data frame

Section 5.6 from R for Data Science:

The textbook uses the flights data frame. You will have to install the nycflights13 package (install.packages('nycflights13')) and load (library(nycflights13)) it in R.

Problem set 5 due 11/01/24 @ 9am

Problem set files

Week 6 (Do before 11/08/24 class)

Required readings

From Attributes and class lecture:

Section 1: Introduction
Section 2.2: Atomic vs augmented vectors
Section 3: Object class
Section 4: Class = factor
Section 5: Class = labelled
Section 6: Comparing class labelled to class factor

Encouraged readings

From Attributes and class lecture:

Section 7.1: Creating factors from string variables
Section 7.2: Creating factors from integer variables

Section 20.6 "Attributes" from R for Data Science
Section 20.7 - 20.7.1 "Augmented vectors and Factors" from R for Data Science

Problem set 6 due 11/08/24 @ 9am

Problem set files

Week 7 (Do before 11/15/24 class)

Required readings

From Strings and dates lecture:

Section 1: Introduction
Section 2: Data structures and types
Section 3: String basics
Section 4: stringr package
Section 5.1-5.2: Dates and times

Encouraged readings

From Strings and dates lecture:

Section 5.3: Time spans

Section 14.1 - 14.2 from R for Data Science
Section 16.1 - 16.4 from R for Data Science

Problem set 7 due 11/15/24 @ 9am

Problem set files

Week 8 (Do before 11/22/24 class)

Required readings

From ggplot lecture:

Section 1: Introduction
Section 2: Concepts
Section 3: Creating graphs using ggplot
Section 4: Customization
Section 5: Exporting plots
ggplot cheatsheet:

Problem set 8 due 11/22/24 @ 9am

Problem set files

Week 9 (Thanksgiving Break, No Class!)

Required readings

From Tidy data:

Section 1: Introduction
Section 2: Data Structure vs. Data semantics
Section 3: Tidy vs. untidy data
Section 4: Tidying untidy data
Section 5: Missing values

Encouraged readings

Chapter 12: Tidy data from R for Data Science

Week 10 (Do before 12/06/24 class)

Required readings

From Joining Datasets:

Section 1: Introduction
Section 2: Keys
Section 3: Mutating joins
Section 4: Filtering joins
Section 5: Appending data

Encouraged readings

Chapter 13: Relational data R for Data Science

Problem set 9 due 12/06/24 @ 9am

Problem set files

Finals Week (Due 12/13/24)

Required readings

Encouraged readings

Problem set 10 due 12/13/24 @ 9am

Problem set files

Course Communication

GitHub is the industry standard platform used by programmers to collaborate on projects. We will use GitHub for course communication and discussion.

Ask course questions using GitHub issues

You will be using GitHub issues to post any questions you have relating to the course material. When you open an issue, please title it appropriately with the question you have. For urgent matters, tag the instructors in your post (@ozanj, @augias/a>, @ezamora0646). We encourage students to answer each other's questions as well and discuss ideas.

Class announcements with GitHub teams

We will be posting all class announcements using GitHub teams. The GitHub team discussions feature allows for quick and seamless communication to all members of an organization or team – in this case, to all students with a GitHub account enrolled in the course. You will also be creating separate teams for your problem set groups.

Email instructors for private questions

If you have a personal question or issue, you can email the instructor or TA directly. Additionally, we are available for office hours or by appointment if there is anything you would like to discuss with us in private.