Using R for Data Analytics
R was created in 1995 by two New Zealand statisticians, Robert Gentleman and Ross Ihaka. This free software environment is an implementation of a prior statistical coding language, S, which originated in the 1970s. The beta version of R was released in 2000, and in the time since, this powerful suite of tools continues to be used for graphing and statistical modeling.
R is an interpreted language, which means it doesn’t have to be run through a compiler prior to running the code. This extensible language enables users to easily call R objects from a variety of other programming languages. R code is typically written and edited using RStudio, a collection of open-source, free tools that allow data science teams to share work.
R is becoming an increasingly popular programming language for those working with big data. This powerful scripting language is often used for statistical computing and graphics. It’s also able to handle complex datasets, which makes it a go-to language for anyone working in data analytics or data science. R is generally seen as more approachable than Python for non-developers because users can create a statistical model and compelling visualization with only a few lines of code. The growing popularity of R has led to it overshadowing older and more traditional statistical packages, such as SPSS and SAS.
Benefits of Using R for Data Analytics
- Ease of producing publication-quality plots
- Highly extensible language
- Capacity to implement new theoretical approaches
- Simplicity of data wrangling
- Powerful graphics
- Supportive community
- Reproducible research
- Ability to generate quality reports
Drawbacks of Using R for Data Analytics
- Because R doesn’t have basic security, it can’t be embedded into a web app.
- When dealing with large datasets, R performs slower than other statistical packages
- R does not come with a point-and-click interface, which means that those who use this language for data analytics must spend time learning the programming language.
R Data Visualization Libraries
For those who are interested in creating data visualizations based on their data findings, R has several helpful visualization libraries that can aid in this process:
- Plotly is a free, open-source data visualization library that provides users with ample options for creating their own interactive visualizations. This library has an extensive collection of graphs: heatmaps, network graphs, 3D charts, histograms, and contour plots, among others.
- Ggplot2 is one of R’s most popular libraries. This library takes a minimalist approach and requires little user input. Those working with this library simply provide data, select a mapping option, and ggplot2 will do the rest.
- Lattice is great with multivariate data. It allows users to create Trellis graphs, which demonstrate the relationship between one or more variables in a given dataset.
- Leaflet allows users to create high-quality maps that can be customized based on individual needs. It is used by The Washington Post and The New York Times for their map-creation projects.
- Interactive capabilities
- Rich interfaces that feature drag-and-drop components
- Because it is an interpreted language, it is quicker to use than other programming languages.
- Regular updates
- Prompt feedback to visitors
- The versatility of use through Node.js servers
- Highcharts: Because it is completely based on native browser technologies, Highcharts doesn’t need client-side plugins such as Flash. It performs well in all modern browsers, even mobile devices.
- Toast UI Chart: This statistical data visualization library offers users an identical look in all browsers. It performs quickly and is easy to use, and includes options to customize themes.
- D3.js: This flexible library uses CSS, SVG, and HTML to bring data to life. Its fast performance allows it to support large datasets as well as interactive or animated elements. D3.js has a clear API reference and comes bundled with several community-supported plugins.
- Recharts: This compostable charting library allows users to customize charts and add effective interactions to various chart components. Its API is easy to use and can support multiple types of shapes, charts, and components.
- Chart.js: This open-source library is user-friendly and can be easily customized based on specific visualization needs. It has over eight chart types, including built-in charts.
Hands-On Coding Classes
Learning to code is an in-demand skill for those working with data. It can open professional doors and also lead to upward career mobility within a Data Analyst role. Noble Desktop has a variety of coding classes available for interested learners. They are taught in-person in NYC and are also available in the live online format. These classes and bootcamps cover topics like SQL, machine learning, HTML, CSS, and Python.
Noble Desktop’s Python bootcamps provide a great learning option for those who are interested in an intensive learning experience. Courses are available with a focus on topics like Python machine learning, Python for data science, and data science, among others.
In addition, over 100 in-person and live online coding classes are available from a variety of top providers. These small classes are designed for novice coders, as well as intermediate and advanced learners.