University of Michigan
Arranging and Visualizing Data in R
University of Michigan

Arranging and Visualizing Data in R

This course is part of Data Science for Health Research Specialization

Taught in English

Philip S. Boonstra

Instructor: Philip S. Boonstra

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

18 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Become knowledgeable about and conversant in the R environment

  • Format and manipulate data within R into suitable formats

  • Develop an intuition for doing exploratory data analysis

  • Develop a workflow in R

Details to know

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Assessments

13 quizzes

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

18 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Data Science for Health Research Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
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There are 4 modules in this course

Module 1 will cover all of the tasks to get you up and running in R. You’ll learn how to access R, how to navigate it, how to install R packages, and how to create scripts that keep a record of your work. We will also learn about The Global Findex Database 2017, a population-based survey and report that provides a wealth of information on financial access for persons all over the world. Your assessments will use data from The Global Findex Database 2017 to create a table and figure from the report.

What's included

9 videos8 readings4 quizzes1 discussion prompt

In Module 2, you will develop insight into how functions work as you are introduced to various functions from the tidyverse, which is a collection of eight R packages useful in data science. The lessons will guide you through performing common data wrangling tasks, such as filtering observations of a dataset and joining data from different sources. By the end of the module, you will have used these tools to reproduce the Indicator Table from The Global Findex Database 2017, which estimates account ownership statistics, including gender and income gaps, for all of the surveyed countries.

What's included

15 videos13 readings5 quizzes1 discussion prompt

Module 3 introduces you to R graphical capabilities. You will both learn about different types of plots – including scatterplots, lineplots, barplots, boxplots, and histograms – and how to make them in R. You’ll learn how to create multipanel plots. And you’ll continue to learn good overall R “hygiene” by keeping your code tidy. You’ll put these newly learned skills to work re-creating Figure 1.1 from The Global Findex Database 2017, which shows how account ownership varies by the income level of a country.

What's included

19 videos7 readings4 quizzes

Having worked through the first three modules, you’ve (re)produced a table and figure from The Global Findex Database 2017. Now what? In Module 4, you will learn about sharing your work with others: exporting tables and figures from R onto your computer. You’ll be introduced to a means of writing reports in R using RMarkdown. And finally we’ll talk about what happens when you get stuck: how to ask questions and where to get help.

What's included

5 videos4 readings1 peer review

Instructor

Philip S. Boonstra
University of Michigan
4 Courses1,248 learners

Offered by

Recommended if you're interested in Data Analysis

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