Arranging and Visualizing Data in R from Coursera

What's inside

Syllabus

Become knowledgeable about and conversant in the R environment

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.

Format and manipulate data within R into suitable formats

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.

Develop intuition for doing exploratory data analysis

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.

Develop a workflow in R

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces beginners to the R statistical environment

Covers data manipulation and preparation tasks in R using the tidyverse packages

Guides learners on data visualization methods in R

Suitable for individuals interested in extracting insights from datasets using the R environment

Involves hands-on practice with the instructor and self-paced exercises

Emphasizes good coding practices and project workflow management in R

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Arranging and Visualizing Data in R with these activities:

Review statistical inference and probability

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Refreshes understanding of statistical inference and probability, which are foundational to R programming and statistical modeling.

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Review notes or textbooks on statistical inference and probability
Solve practice problems on hypothesis testing, confidence intervals, and probability distributions

Follow tutorials on R basics

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Provides hands-on practice with R syntax and basic coding concepts, building a foundation for the course.

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Find tutorials on R installation, data types, and basic operations
Follow the tutorials step-by-step and experiment with the code

Read 'R for Data Science'

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Provides a comprehensive review of R programming, data wrangling, and data analysis, complementing the course content.

View R for Data Science: Import, Tidy, Transform,... on Amazon

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Read selected chapters of 'R for Data Science'
Complete exercises and work through examples in the book

Four other activities

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Complete R coding exercises

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Reinforces understanding of R commands and functions through repetitive practice, improving coding proficiency.

Browse courses on R Programming

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Find online coding exercises or problem sets on R
Solve coding problems independently, referring to documentation or tutorials when needed

Create an R cheat sheet

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Enhances recall of R commands and concepts by creating a concise summary of key information.

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Review R documentation and tutorials
Identify and organize essential commands, functions, and concepts
Create a cheat sheet using a text editor or online tool

Answer questions and support peers in online forums

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Deepens understanding and strengthens problem-solving skills by helping others, reinforcing concepts through articulation.

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Participate in R-related online forums
Read and respond to questions posed by other learners

Develop an R project for data visualization

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Applies and extends course concepts through a hands-on project, fostering practical skills and creativity.

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Identify a dataset for visualization
Create visualizations using R's graphical capabilities
Write a brief report summarizing the insights gained from the visualization

Career center

Learners who complete Arranging and Visualizing Data in R will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course provides a strong foundation in the R programming language, which is widely used by data analysts. Learners of this course will develop the skills needed to wrangle, visualize, and analyze data.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use statistical and machine learning techniques to extract insights from data. This course provides a foundation in the R programming language and tidyverse packages, which are essential tools for data scientists. Learners will develop the skills needed to clean, manipulate, and visualize data.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians collect, analyze, interpret, and present data. This course provides a foundation in the R programming language, which is widely used by statisticians for data analysis and visualization. Learners will develop the skills needed to clean, manipulate, and analyze data.

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Data Engineer

Data Engineers design and build systems to manage and process data. This course provides a foundation in the R programming language, which is commonly used for data engineering tasks such as data cleaning, transformation, and integration.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course provides a foundation in the R programming language, which is widely used by quantitative analysts for data analysis and modeling. Learners will develop the skills needed to clean, manipulate, and analyze data.

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Market Researcher

Market Researchers conduct surveys and analyze data to understand consumer behavior. This course provides a foundation in the R programming language, which is widely used by market researchers for data analysis and visualization. Learners will develop the skills needed to clean, manipulate, and analyze data.

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Business Analyst

Business Analysts use data to identify problems and opportunities for businesses. This course provides a foundation in the R programming language, which is increasingly used by business analysts for data analysis and visualization. Learners will develop the skills needed to clean, manipulate, and analyze data.

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Information Security Analyst

Information Security Analysts plan and implement security measures to protect an organization's information systems. This course provides a foundation in the R programming language, which is increasingly used for data analysis and visualization in the field of information security.

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Financial Analyst

Financial Analysts analyze financial data to make investment recommendations. This course provides a foundation in the R programming language, which is widely used by financial analysts for data analysis and modeling. Learners will develop the skills needed to clean, manipulate, and analyze financial data.

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Biostatistician

Biostatisticians apply statistical methods to solve problems in biology and medicine. This course provides a foundation in the R programming language, which is widely used by biostatisticians for data analysis and visualization. Learners will develop the skills needed to clean, manipulate, and analyze data.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course provides a foundation in the R programming language, which is increasingly used for data analysis and visualization in the software development process. Learners will develop the skills needed to clean, manipulate, and analyze data to inform software design and development.

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Actuary

Actuaries assess risk and uncertainty. This course provides a foundation in the R programming language, which is increasingly used by actuaries for data analysis and modeling. Learners will develop the skills needed to clean, manipulate, and analyze data to inform risk assessment and mitigation.

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Economist

Economists study the production, distribution, and consumption of goods and services. This course provides a foundation in the R programming language, which is increasingly used by economists for data analysis and modeling. Learners will develop the skills needed to clean, manipulate, and analyze data to inform economic decision-making.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to improve the efficiency of operations. This course provides a foundation in the R programming language, which is increasingly used by operations research analysts for data analysis and modeling. Learners will develop the skills needed to clean, manipulate, and analyze data to inform operational decision-making.

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Product Manager

Product Managers oversee the development and launch of new products. This course provides a foundation in the R programming language, which is increasingly used by product managers for data analysis and visualization. Learners will develop the skills needed to clean, manipulate, and analyze data to inform product design and development.

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