Exploratory Data Analysis for the Public Sector with ggplot

Overview

Learn about the core pillars of the public sector and the core functions of public administration through statistical Exploratory Data Analysis (EDA). Learn analytical and technical skills using the R programming language to explore, visualize, and present data, with a focus on equity and the administrative functions of planning and reporting. Technical skills in this course will focus on the ggplot2 library of the tidyverse, and include developing bar, line, and scatter charts, generating trend lines, and understanding histograms, kernel density estimations, violin plots, and ridgeplots. These skills are enhanced with lessons on best practices for good information visualization design. Upon completing this course, you will understand the layered grammar of graphics and its implementation in ggplot2, all while exploring a diverse set of authentic public datasets. All coursework is completed in RStudio in Coursera without the need to install additional software. This is the second of four courses within the Data Analytics in the Public Sector with R Specialization. The series is ideal for current or early-career professionals working in the public sector looking to gain skills in analyzing public data effectively. It is also ideal for current data analytics professionals or students looking to enter the public sector.

Syllabus

Week 1 | Introduction to Visualization with ggplot2

Welcome to the second course in the Data Analytics in the Public Sector with R—Exploratory Data Analysis for Public Administration with ggplot. This week, you will begin to develop the skills of exploratory data analysis through constructing ggplot plots and line plots in RStudio. You will also get to recognize the importance of data visualization for public administration and how powerful data visualization skills are in the profession of a data analyst.

Week 2 | Fundamentals of Exploratory Data Analysis (EDA)

Welcome to Week 2! This week you will learn about the importance of Exploratory Data Analysis (EDA) for the core principle of equity. Building on the previous week, you will also continue to develop skills of exploratory data analysis through understanding frequencies and distributions and how to construct trendlines and histograms using RStudio.

Week 3 | Visualizing Populations and Trends with R

Welcome to Week 3! You will start this week exploring distributions and develop the skills of visualizing different distributions, such as boxplots, violin plots, and ridgeplots. You will also learn how to develop scale, coord, faceats across layers in ggplot, using RStudio.

Week 4 | Best Practices for Data Visualization

Welcome to Week 4, the last week in this course! This week you will learn the best practices for data visualization through communication principles and skills. You will also learn about information visualization theory and how to apply that in developing data visualization in RStudio.