While there are similarities among programming languages, there are known differences as well. Programming languages each treat information and data in ways that correspond to specific fields and industries. For example, SQL is the language used to communicate with databases, so it is essential knowledge for data scientists working in database management and design.
Data scientists must learn the language most relevant to their position or projects. For example, data scientists interested in statistical analysis and modeling should learn the R programming language which offers multiple data science libraries specializing in data analysis and visualization. Data scientists who learn how to program with R should also learn about R's top data science libraries.
What is R?
R is a programming language used for working with large stores of numerical data and is associated with big data. Like Python, R is one of the most globally popular open-source programming languages because the R programming language is used for statistical analysis, modeling, and mathematical computation. Products such as RStudio and other environments, enable students and professionals to perform data mining, debugging, and editing code. Developed from the S programming language, R is the go-to language for data manipulation and visualization. It is also an essential skill for engineers, developers, statisticians, information technologists, and data scientists.
Why Data Scientists Learn R
Because of its capabilities for analyzing and modeling quantitative data, R is used for data science applied to scientific research and financial analysis. Data scientists learn R when developing software or working on a project that requires collaboration and statistical analysis. R provides all the tools and features typical in finance and technology, from risk assessments to portfolio management. Statisticians can also use R when making estimates or predictions about the study population.
R is an open-source programming language that simplifies time-consuming tasks like data cleaning in a data science team or online community. Scripting the steps for data cleaning makes dataset organization easily reproducible by other team members. However, working with libraries and packages is the main reason students and professionals learn R for data science. Multiple modeling packages based on R organize, analyze, and visualize datasets, so many data scientists learn R when they need to visualize a model or plot a graph.
Top Data Science Libraries and Packages in R
1. Shiny
Shiny is an R package used for developing websites and applications and creating digital dashboards for data visualization. Data scientists use the Shiny app to share their findings with a larger audience because Shiny makes it easier to animate visualizations. Data scientists use Shiny apps to transform their data analysis into data storytelling. Shiny is an extension of RStudio and can be paired with widgets and other interactive functions to create more engaging websites or applications. The Shiny library is a mainstay for web and mobile application developers and designers.
2. Dplyr
Dplyr is a grammar engineered for data manipulation included within the tidyverse. The tidyverse is a collection of online packages and libraries focused on educating data science students and professionals in R. As a grammar, dplyr provides a syntax that includes multiple functions useful for data scientists cleaning a dataset. This package also comes with datasets for practicing how to use these functions.
3. tidyr
Tidyr is a library in the tidyverse of R packages and libraries. As the name suggests, tidyr is a library for tidying or cleaning your data. Data cleaning is a common use of R libraries and tidyr is one of many tidyverse libraries that make it easier for data scientists to edit and organize their data. R practitioners using the tidyverse have also developed online communities around the programming language and its packages, such as #TidyTuesday, where data scientists and students can share their R projects and cleaning techniques.
4. ggplot2
Ggplot2 is one of the most popular R packages for creating data visualizations. Data scientists use ggplot2 for various visualization techniques, including plotting points and drawing histograms. This library includes multiple functions for plotting different types of graphs and images. Ggplot2 is another grammar of the visualization library and is known for its unique use of syntax that enables data scientists to declare statements that can create graphics. While this syntax may be more difficult for beginners, the tidyverse is full of resources that offer step-by-step instructions on calling functions and creating programs using ggplot2.
5. Plotly
Similar to ggplot2, plotly is another R library used to plot graphs and create data visualizations. As a graphing library, plotly offers multiple options for data visualizations, including traditional plots like line graphs and bar charts and more advanced visualizations like heat maps and 3D charts. Plotly can also be paired with other libraries in the tidyverse to create animated interactive graphs.
Need to Learn How To Program with R?
Noble Desktop's data science classes offer training in several programming languages and tools, including data analytics, programming with R, statistical analysis, and industry trends. Students and professionals with busy schedules can take live online data science classes in a virtual learning environment. Those who prefer a traditional classroom experience can also choose in-person data science classes.