Building Essential Skills for Exploratory Data Analysis in R

Key takeaways

Data scientists use exploratory data analysis (EDA) in R to analyze data to find patterns and inconsistencies and prove or disprove hypotheses. 

• R is a programming language that uses specialized analytics and modeling for data analysis and visualization. 

• Core skills for exploratory analysis in R include data cleaning and data transformation skills.

• You can communicate your data findings by creating engaging and informative data visualizations to enhance your R skills using packages, such as ggplot2.

Learn more about using exploratory data analysis in R and the skills you need to hone. Discover projects and practical applications to help you start utilizing EDA in R today. Or, start learning with the Google Data Analytics Professional Certificate. In as little as six months, you can gain an immersive understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job. By the end, earn a shareable certificate to add to your professional profile.

Core skills for exploratory data analysis in R programming

To work on exploratory data analysis in R, you’ll need a basic statistical understanding and a wide range of specialist skills. R is a programming language that you can use to create specialized analytics and modeling to explore your data. It’s important that you understand data analysis and data manipulation and have proficiency in data visualization. It is also helpful to have some understanding and skills in programming.

Statistical foundations

Exploratory data analysis uses statistical tools to discover patterns and uncover inconsistencies in data. Using EDA, you can perform visualization functions like univariant (looking at each raw dataset), bivariant (looking at the relationship between variables), and multivariate (mapping data between different data fields). You’ll also use statistical techniques to work with predictive models like linear regression and probability theory. This means it’s vital that you have a solid grounding in statistical foundations. 

How to learn: Enroll in online courses or workshops focusing on statistical concepts, such as Statistics Foundations by Meta.

Read more: Essential R Programming Skills 

Proficiency in R programming

To be able to use exploratory data analysis in R, you’ll need to have programming proficiency in R. In R, you'll need to write code to clean and analyze your data and create visualizations. You don't need much coding experience to start, but it will require time and practice.

How to learn: Practice coding in R as personal projects and contribute to open-source R projects. You might enroll in an online course such as R Programming, by Johns Hopkins University.

Mastering data manipulation and cleaning in R

Before you begin with data analysis, it’s important to prepare your data set by cleaning and manipulating data in R. Mastering these skills is essential to ensure your data is ready for exploration.

Data cleaning techniques

Cleaning your data is an important pre-analysis step. By cleaning your data, you check for inconsistencies and errors. This is a crucial step because using inaccurate data will skew your results. To do this effectively in R, you can use a package such as tidyr to clean and re-code your data so that it’s usable.

How to learn: Work on real-life data set projects, practicing R packages like tidyr for cleaning data. Learn from a Guided Project, such as Tidy Messy Data using tidyr in R.

Data transformation skills

R has built-in functions to help you organize and manipulate your data, making it easier to work with and analyze. While you can do this in R, you can also access packages to perform data management tasks, such as dplyr, which simplifies manipulation, sorting, and summarizing data in preparation for analysis. 

How to learn: Access various R functions and packages, such as dplyr. To learn more, you can take an online course or tutorial, such as a Guided Project like Data Manipulation with dplyr in R.

Enhancing visualization skills for better insights

Visualization is an important part of explanatory data analysis as a way to understand complex data sets. Visualization helps bring data to life by understanding differences and similarities between variables, seeing interactions between them, and making data clearer to summarize.

Creating impactful visualizations

You can design engaging and informative data visualizations in R using packages such as ggplot2. ggplot2 is an open-source data visualization package that creates graphs and charts by inputting data and mapping the variables. It’s especially helpful when creating complex graphics with multiple layers.

How to learn: Practice using ggplot2, starting with simply supplying a data set and moving on to more complex elements like adding layers and scales. Consider taking an online course such as Data Visualization in R with ggplot2.

Interactive and advanced visualizations

Once you have some experience with visualizations, you may move on to advanced visualizations, such as interactive visualizations. For this, you’ll need another package to use in R, such as Plotly, which is a graphing library. With Plotly, you can produce advanced visualizations of high quality. These include scatter plots, heatmaps, and 3D charts, as well as interactive elements like animations. 

How to learn: Practice using R packages like Plotly, and take an online course on data visualization, such as Data Visualization and Dashboarding with R Specialization.

Exploratory data analysis in R example packages 

When you perform exploratory data analysis in R, you can use a range of packages to make sense of your data and generate useful insights to guide your next steps. A few built-in packages in R that can help your EDA include:

• describe(): generates descriptive statistics such as mean, missingness, and skewness

• normality(): normalizes your data

• plot_normality(): provides a visualization of your data, including histograms or Q-Q plots

• correlate(): determines the correlation coefficient between two of your variables

• plot_correlate(): creates a visualization of the correlation matrix

Practical application: EDA projects in R

To learn and develop your skills in exploratory data analysis, consider gaining some practical experience through EDA projects in R. You’ll find many personal projects you can undertake using public datasets. Check out repositories like Kaggle for datasets that you can use, and see what others have utilized. Alternatively, you can also find datasets online. The data sets you use can be anything from government air traffic to house prices in Boston. You can document your findings in programs like R Markdown to prepare for analysis.

Continuous learning and community engagement

In a technical world, keeping updated with the latest trends and developments in EDA and programming is essential. Doing so helps to ensure your practices are up to date and your portfolio is impactful. You’ll find that the R community is particularly active, and you can attend conferences, meetups, and events, and use forums to practice your skills and participate in projects with others. 

Explore resources to take your skills further

Discover fresh insights into the skills you could build upon and gain career guidance with our Career Resources Hub. Or if you want to keep learning more about programming tools like R and how to use them, check out these free resources:

• Watch on YouTube: R Programming Basics | Microsoft Tutorial 

• Take your first step: Data Analysis: Where to Start and How to Build This High-Income Skill

• Learn from peers: Meet the Data Analyst Using His Creativity to Tell Visual Stories

With Coursera Plus, you can learn and earn credentials at your own pace from over 350 leading companies and universities. With a monthly or annual subscription, you’ll gain access to over 10,000 programs. Just check the course page to confirm your selection is included.

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial