When talking about Data Science, it is impossible not to talk about R. It can be said that R is the best language for Data Science as it was developed by statisticians for statisticians! It is also very popular (despite getting stiff competition from Python!) with an active community and many cutting-edge libraries currently available.
In fact, many R libraries contain a host of functions, tools, and methods to manage and analyze data. Each of these libraries has a particular focus with some libraries managing image and textual data, data manipulation, data visualization, web crawling, machine learning, and so on. Here we have the top 10 R libraries for Data Science so let’s check them out now!
1. dplyr
dplyr is a very popular data manipulation library in R. It has five important functions that are combined naturally with the group_by() function that can help in performing these functions in groups. These functions include the mutate() function which can add new variables that are functions of existing variables, the select() function which selects the variables based on their names, the filter() function that picks selects the variables based on their values, summarise () function that reduces multiple values into a summary, and the arrange() function that arranges the arranges the row orderings.
2. ggplot2
ggplot2 is an R data visualization library that is based on The Grammar of Graphics. ggplot2 can create data visualizations such as bar charts, pie charts, histograms, scatterplots, error charts, etc. using high-level API. It also allows you to add different types of data visualization components or layers in a single visualization. Once ggplot2 has been told which variables to map to which aesthetics in the plot, it does the rest of the work so that the user can focus on interpreting the visualizations and take less time to create them. But this also means that it is not possible to create highly customized graphics in ggplot2. But there are a lot of resources in the RStudio community and Stack Overflow that can provide help in ggplot2 when needed.
3. Esquisse
Esquisse is a data visualization tool in R that allows you to create detailed data visualizations using the ggplot2 package. You can create all sorts of scatter plots, histograms, line charts, bar charts, pie charts, error bars, box plots, multiple axes, sparklines, dendrograms, 3-D charts, etc. using Esquisse and also export these graphs or access the code for creating these graphs. Esquisse is such a famous and easily used data visualization tool because of its drag-and-drop ability which makes it popular even among beginners. You can install Esquisse from CRAN using install.packages(“esquisse”) or install the development version from GitHub using remotes::install_github(“dreamRs/esquisse”).
4. Shiny
Shiny is an R package that can be used to build interactive web applications in R. Basically, Shiny gives a combination of R and the modern web. And you can easily create web applications using Shiny without needing any special web development skills. Using Shiny, you can embed web applications in R documents, create standalone applications on a webpage, or even create web visualization dashboards. If you want to extend the functionality of your shiny applications, you can do so by adding HTML widgets, CSS themes, JavaScript actions, etc. If you are not that knowledgeable about Shiny, you can access Video tutorials on the Shiny RStudio website. You can also deploy the Shiny app to the cloud or on your servers with an open-source or commercial license.
5. mlr3
mlr3 is an R tool created specifically for Machine Learning. You can implement various Supervised and Unsupervised Machine learning models on Scikit-learn like Classification, Regression, Support Vector Machines, Random Forests, Nearest Neighbors, Naive Bayes, Decision Trees, Clustering, etc. with mlr3. It is also connected to the OpenML R package which is dedicated to supporting machine learning online. You can easily create your Machine Learning algorithms on mlr3 or even work within the already established algorithms. mlr3 is an improvement over its previous version mlr which is now retired and not updated by the mlr-org team.
6. Lubridate
Lubridate is an R library that is particularly focused on making date-time easy to handle. Working with date-time data can be frustrating with R because R commands are unintuitive for this type of data and can change based on the type of date-time object. In this situation, Lubridate is a lifesaver as it allows easy management of date-time data with simple functions that manage components of date-time such as second(), minute(), hour(), day(), month(), and year(). There are many new time span classes in Lubridate as well that help in handling mathematical operations. these classes include Intervals which provide a protean summary of the time information between two points, Durations which provide the amount of time between two points, Periods that accurately track clock times, etc.
7. RCrawler
RCrawler is an R package for domain-based web crawling and web scraping which involves obtaining structured data from websites that can be used in multiple applications. RCrawler is involved in web structure mining, text mining, web content mining, etc. Rcrawler can automatically move across all the pages on a website and extract all data that is required from these web pages using a single command. However, this process of web crawling is performed by concurrent nodes that work in parallel so it’s best to have the 64bit version of R for Rcrawler.
8. knitr
Knitr is like a helpful tool for R users who want to create dynamic reports. It lets you mix different types of code (like Markdown, LyX, LaTeX, AsciiDoc, and HTML) right into your R code. This is super handy for researchers who need to turn their data analysis into a report. Knitr makes the whole process smoother and more automated. It’s an upgrade from Sweave, another R function, fixing some issues it had.
9. DT
DT is an R package that provides an interface to the JavaScript library DataTables that can be used to display R matrices and data frames in the form of tables. These tables are interactive HTML tables and you can perform many different functions such as sorting, searching, filtering, etc. The most important function in DT is datatable() as it can create a data table to display the R objects. You can also style your tables in DT using the CSS classes.
10. Plotly
Plotly is a cool tool for making graphs without spending any money. It’s open-source and works with R. Think of it as an R package that sits on top of the Plotly JavaScript library. This combo lets you whip up data visualizations that you can show off in Jupyter notebooks, web apps (thanks to Dash), or keep as standalone HTML files. Plotly gives you over 40 types of charts, from basic stuff like scatter plots to more fancy ones like 3-D charts and even contour plots, which not every graphing tool has.
11. caret
caret is a tool designed for regression analysis and classification. It revolves around a key function called ‘train,’ which explores the impact of resampling on tuning parameters for model performance. Caret is adept at working with a wide range of algorithms in both regression and classification scenarios. Additionally, it generates tables and plots, providing valuable insights and support during the model training process. Caret simplifies the process of model development by offering comprehensive support for exploring various algorithms and tuning parameters. Its ability to generate tuneGrid tables and plots enhances the efficiency and effectiveness of model training.
12. ROCR
ROCR in R is a valuable package designed for evaluating and visualizing the performance of classification models. It specializes in creating essential metrics like ROC curves (Receiver Operating Characteristic) and precision-recall curves to provide a clear assessment of model accuracy and effectiveness. Utilize ROCR to enhance the visual representation and understanding of classification model quality. ROCR is a helpful tool in evaluating how well classification models work. It creates graphs like ROC curves and provides detailed information to analyze how accurate and useful models are. It’s user-friendly, making it useful for researchers and data analysts who want to understand how well their models perform.
13. Glmnet
glmnet is a widely-used R package for building regression models with regularization techniques like LASSO and elastic-net. It helps in selecting important variables, preventing overfitting, and making linear and logistic regression models more understandable and effective. glmnet’s flexibility extends to various types of regression tasks, making it a versatile tool for data analysts. It strikes a balance between model simplicity and accuracy, making it useful in scenarios where interpretability is crucial. Whether preventing overfitting or enhancing model performance, glmnet is a valuable asset in the toolkit of researchers and analysts.
14. Markdown
Markdown simplifies the process of creating dynamic documents by seamlessly blending code, text, and visual elements within a single document. With support for multiple output formats such as HTML, PDF, and Word, it empowers users to generate reproducible research and reports effortlessly. Its versatility and user-friendly features make rmarkdown an essential tool for researchers and analysts seeking efficient document creation and seamless integration of data-driven insights.
15. RSQLite
RSQLite is like a helpful tool for R users who want to work with SQLite databases. It lets you easily manage, ask questions, and change SQLite databases directly from your R program. RSQLite makes dealing with databases in R simpler and smoother for data scientists and analysts. If you want to get RSQLite, just use ‘install.packages(“RSQLite“)’ from CRAN. It enhances the capabilities of R users working with SQLite databases and ensures efficient data handling.
Conclusion
R is a great language for Data Science. It has many useful tools, like dplyr, ggplot2, Shiny, mlr3, and more, that help with tasks like working with data, creating visuals, and building machine learning models. Even though Python is a strong competitor, R’s active community and powerful libraries make it a top choice. Whether you’re just starting or an expert, these R packages make data science tasks easier, from analyzing and visualizing data to developing models. Tools like RSQLite also make managing databases in R simpler, making the overall data science experience smooth and comprehensive.
