Nonprofit Data Analysis Using R

Course Curriculum

Chapter 1: Introduction

Lecture 1: Dataset Introduction

Chapter 2: Project Set-Up

Lecture 1: Download R

Lecture 2: Download R Studio

Lecture 3: Download Course Files

Lecture 4: Set Working Directory

Lecture 5: Install Packages

Chapter 3: Section 1: Jumpstart

Lecture 1: 1.0. Load Libraries and Import Data

Lecture 2: 1.1. Data Wrangling Part 1 (mutate, change data types)

Lecture 3: 1.2. Data Wrangling Part 2 (select, set_names, rename, and separate)

Lecture 4: 1.3. Data Wrangling Part 3 (filter, group_by, and count)

Lecture 5: 1.4. Data Wrangling Part 4 (distinct, slice, and filter by another variable)

Lecture 6: 1.5. Data Visualization Part 1 (core syntax, facet_wrap, geom_text, & scales)

Lecture 7: 1.6. Data Visualization Part 2 (Add theme and labels)

Lecture 8: 1.7. Data Visualization Part 3 (geom_point, geom_smooth, geom_jitter)

Lecture 9: Challenge 1 Introduction

Lecture 10: Challenge 1 Explanation

Chapter 4: Section 2: Loading, Joining, and Exploring Data

Lecture 1: Section 2 Introduction

Lecture 2: Data Type Intro.

Lecture 3: Data Structure Intro.

Lecture 4: Load Data from Snowflake Database

Lecture 5: Mutate (with case_when, if_else)

Lecture 6: Exploratory Data Analysis Part 1 (Introduction)

Lecture 7: Exploratory Data Analysis Part 2 (DataExplorer package)

Lecture 8: Exploratory Data Analysis Part 3 (skimr and GGally packages)

Chapter 5: Section 3: Data Transformation

Lecture 1: Filter Part 1

Lecture 2: Filter Part 2

Lecture 3: Pivot_wider and pivot_longer Part 1

Lecture 4: Pivot_longer Part 2

Lecture 5: Bind_rows

Lecture 6: Group_by & Summarize

Lecture 7: Dates and Times Part 1: Date components

Lecture 8: Dates and Times Part 2: floor & ceiling_date

Lecture 9: Dates and Times Part 3: lag & change over time

Lecture 10: Dates and Times Part 4: rollmean & cumsum

Lecture 11: Modify Strings Part 1: str_to_lower, str_detect, and str_replace_all

Lecture 12: Modify Strings Part 2: str_glue

Lecture 13: Modify Strings Part 3: separate & unite

Lecture 14: Challenge 3 Introduction

Lecture 15: Challenge 3 Solutions

Chapter 6: Section 4: Feature Engineering

Lecture 1: Feature Engineering Introduction

Lecture 2: Cumulative (year-to-date) and Rolling Averages

Lecture 3: Extracting Time-Based Features

Lecture 4: Course Option: Functions or Visualizations

Lecture 5: Functional Programming Part 1: Anonymous functions within a list

Lecture 6: Functional Programming Part 2: Creating your first function

Lecture 7: Interpreting a Boxplot and Defining Outliers

Lecture 8: Functional Programming Part 3: Run a Function on a Single Column

Lecture 9: Functional Programming Part 4: Run a Function On Multiple Columns

Lecture 10: Functional Programming Part 4: Adding Function Results to Visualization

Lecture 11: Functional Programming Part 5: Run Multiple T-Tests on a Dataframe

Lecture 12: Functional Programming Part 6: Save and Load Functions

Chapter 7: Section 5: Data Visualizations and Reports

Lecture 1: Introduction: Choosing the Right Plot

Lecture 2: Part 1: Barplot

Lecture 3: Part 2: Barplot Function

Lecture 4: Part 3: Scatterplots (& geom_jitter)

Lecture 5: Part 4: Scatterplot Function

Lecture 6: Part 5: Density Plot

Lecture 7: Part 6: Boxplot & Violin Plot

Lecture 8: Part 7: Line Graph and Sourcing Plot Functions

Lecture 9: Part 8: Load New Libraries Before Next Section

Chapter 8: Section 6: Building Reports

Lecture 1: R Markdown Introduction

Lecture 2: Part 1: Creating a Report

Lecture 3: Part 2: Adding Graphs and Tables to Reports

Lecture 4: Part 3: Using CSS to Customize Report Layout

Lecture 5: Part 4: PDF Reports

Lecture 6: Part 5: Intro to Graph Layout with Patchwork

Lecture 7: Part 6: Additional Ways to Customize Graph Layout

Lecture 8: Part 7: Visual Editor Window

Lecture 9: Part 8: Parameterized Reports for Automation