Building Essential Skills for Exploratory Data Analysis in R

Data scientists use exploratory data analysis (EDA) to analyze data by manipulating data sources to aid in finding patterns and inconsistencies, and proving or disproving hypotheses. R is a programming language that uses specialized analytics and modeling for data analysis and visualization. As EDA has a focus on providing visualization methods, R is a helpful tool to use for EDA. 

Learn more about the use of exploratory data analysis in R and the skills you need to hone to do so. Discover projects and practical applications to help you start utilizing EDA in R today. 

Core skills for exploratory data analysis in R

To work on exploratory data analysis in R, you’ll need a basic statistical understanding and a wide range of specialist skills. 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.

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, as well as 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 John Hopkins University.

Mastering data manipulation and cleaning in R

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

Data cleaning techniques

Cleaning your data is an important pre-analysis step. By cleaning your data, you are checking it for any inconsistencies and errors. This is an extremely important 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 dataset 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 to make 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. You can take an online course or tutorial to learn more, 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 ggplot 2, 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.

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 datasets you use can be anything from Government on 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, it’s important to keep updated with the latest trends and developments in EDA and programming. 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. 

Getting started in exploratory data analysis in R with Coursera

Exploratory Data Analysis in R is a process of analyzing and manipulating data to solve problems and gain meaningful insights. R is an excellent tool for creating visualizations as part of data analysis. If you’d like to learn more about data analysis with R, you might consider an online course such as Data Science: Foundations Using R Specialization, delivered by John Hopkins University, or the Google Data Analysis with R Programming course, as part of the Google Data Analytics Professional Certificate.