Nonprofit Data Analysis Using R

An 80-20 Approach to Proficiency for Beginners

[b]What you'll learn[/b]

Load data from different sources into R (files, databases)

Clean and transform data using the tidyverse packages

Quickly explore and visualize data trends

Create professional visualizations and reports

Perform time-series analysis

Conduct feature engineering for deeper analysis

Automate reports with Rmarkdown

[b]Requirements[/b]

No programming or statistical experience necessary.

[b]Description[/b]

This is the R course for beginners with no coding experience. It is based on the latest research in online learning theory and my personal experience with dozens of online courses. I created this course as the course I wish I would have had when I first started learning R. We will code together and focus on the 20% of code responsible for 80% of the work. At the end of sections, you will have a 'Make It Stick' challenge to apply what you have just learned with a different dataset (based on principles in the book 'Make It Stick'). This course is different from other beginner courses in R in a couple significant ways Tongue

roject-based learning with real-world scenarios: All lessons are based on common questions facing data practioners. Content focus: The course outline and lectures are based on everyday workflows of data practioners rather than a bottom-up approach to R programming. Practically, this means we won't spend much time learning about R and core principles of programming; we will immediately start with how you will use it. Current (& continually updated) code: I work in R everyday and make sure you are learning the best and most efficient ways to accomplish the most common and important tasks. For example, the rowwise function in the dplyr package enables you to perform calculations across columns by rows. A single line of code can now accomplish what was previously far more challenging. Keeping it real: I keep the video rolling when I make an error. You can learn a lot from mistakes. R was my first programming language and I quit twice because of too many errors, too much time to learn it, and frustration with online courses that left out important steps or assumed knowledge that simply wasn't there. I try really hard to explain what we're doing while we're doing it and then giving you an opportunity to do it on your own with a different (but related) dataset. In this course, you will learn to:Load data from different sources (files, databases)Structure data for analysis using the tidyverse packagesQuickly explore and visualize data trendsConduct feature engineering for deeper analysisAnalyze survey dataSelect the right visualization for your dataCreate professional visualizationsCreate and automate reports using RMarkdown

Overview

Section 1: Introduction

Lecture 1 Dataset Introduction

Section 2: Project Set-Up

Lecture 2 Download R

Lecture 3 Download R Studio

Lecture 4 Download Course Files

Lecture 5 Set Working Directory

Lecture 6 Install Packages

Section 3: Section 1: Jumpstart

Lecture 7 1.0. Load Libraries and Import Data

Lecture 8 1.1. Data Wrangling Part 1 (mutate, change data types)

Lecture 9 1.2. Data Wrangling Part 2 (select, set_names, rename, and separate)

Lecture 10 1.3. Data Wrangling Part 3 (filter, group_by, and count)

Lecture 11 1.4. Data Wrangling Part 4 (distinct, slice, and filter by another variable)

Lecture 12 1.5. Data Visualization Part 1 (core syntax, facet_wrap, geom_text, & scales)

Lecture 13 1.6. Data Visualization Part 2 (Add theme and labels)

Lecture 14 1.7. Data Visualization Part 3 (geom_point, geom_smooth, geom_jitter)

Lecture 15 Challenge 1 Introduction

Lecture 16 Challenge 1 Explanation

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

Lecture 17 Section 2 Introduction

Lecture 18 Data Type Intro.

Lecture 19 Data Structure Intro.

Lecture 20 Load Data from Snowflake Database

Lecture 21 Mutate (with case_when, if_else)

Lecture 22 Exploratory Data Analysis Part 1 (Introduction)

Lecture 23 Exploratory Data Analysis Part 2 (DataExplorer package)

Lecture 24 Exploratory Data Analysis Part 3 (skimr and GGally packages)

Section 5: Section 3: Data Transformation

Lecture 25 Filter Part 1

Lecture 26 Filter Part 2

Lecture 27 Pivot_wider and pivot_longer Part 1

Lecture 28 Pivot_longer Part 2

Lecture 29 Bind_rows

Lecture 30 Group_by & Summarize

Lecture 31 Dates and Times Part 1: Date components

Lecture 32 Dates and Times Part 2: floor & ceiling_date

Lecture 33 Dates and Times Part 3: lag & change over time

Lecture 34 Dates and Times Part 4: rollmean & cumsum

Lecture 35 Modify Strings Part 1: str_to_lower, str_detect, and str_replace_all

Lecture 36 Modify Strings Part 2: str_glue

Lecture 37 Modify Strings Part 3: separate & unite

Lecture 38 Challenge 3 Introduction

Lecture 39 Challenge 3 Solutions

Section 6: Section 4: Feature Engineering

Lecture 40 Feature Engineering Introduction

Lecture 41 Cumulative (year-to-date) and Rolling Averages

Lecture 42 Extracting Time-Based Features

Lecture 43 Course Option: Functions or Visualizations

Lecture 44 Functional Programming Part 1: Anonymous functions within a list

Lecture 45 Functional Programming Part 2: Creating your first function

Lecture 46 Interpreting a Boxplot and Defining Outliers

Lecture 47 Functional Programming Part 3: Run a Function on a Single Column

Lecture 48 Functional Programming Part 4: Run a Function On Multiple Columns

Lecture 49 Functional Programming Part 4: Adding Function Results to Visualization

Lecture 50 Functional Programming Part 5: Run Multiple T-Tests on a Dataframe

Lecture 51 Functional Programming Part 6: Save and Load Functions

Section 7: Section 5: Data Visualizations and Reports

Lecture 52 Introduction: Choosing the Right Plot

Lecture 53 Part 1: Barplot

Lecture 54 Part 2: Barplot Function

Lecture 55 Part 3: Scatterplots (& geom_jitter)

Lecture 56 Part 4: Scatterplot Function

Lecture 57 Part 5: Density Plot

Lecture 58 Part 6: Boxplot & Violin Plot

Lecture 59 Part 7: Line Graph and Sourcing Plot Functions

Lecture 60 Part 8: Load New Libraries Before Next Section

Section 8: Section 6: Building Reports

Lecture 61 R Markdown Introduction

Lecture 62 Part 1: Creating a Report

Lecture 63 Part 2: Adding Graphs and Tables to Reports

Lecture 64 Part 3: Using CSS to Customize Report Layout

Lecture 65 Part 4: PDF Reports

Lecture 66 Part 5: Intro to Graph Layout with Patchwork

Lecture 67 Part 6: Additional Ways to Customize Graph Layout

Lecture 68 Part 7: Visual Editor Window

Lecture 69 Part 8: Parameterized Reports for Automation

Non-profit employees responsible for measuring and understanding program performance.,Employees who work on spreadsheets and are looking for more capacity and efficiency.

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