When talking about Data Science, it is impossible not to talk about R. In fact, 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, there are many R libraries that 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!
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, select() function that selects the variables based on their names, 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. If you want to install dplyr, the best method is to install the tidyverse which is a collection of R packages created specifically for Data Science. Or you can just install dplyr using install.packages(“dplyr”).
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 in creating 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 which can provide help in ggplot2 when needed. Just like dplyr, if you want to install ggplot2, you can install the tidyverse or you can just install ggplot2 using install.packages(“ggplot2”)
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 that 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”).
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 own 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. You can install the last release of mlr3 from CRAN using install.packages(“mlr3”) or install the development version from GitHub using remotes::install_github(“mlr-org/mlr3”).
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 that provide a protean summary of the time information between two points, Durations that provide the amount of time between two points, Periods that accurately track clock times, etc. If you want to install dplyr, the best method is to install the tidyverse using install.packages(“tidyverse”) as Lubridate is a part of it. Or you can just install dplyr using install.packages(“lubridate”).
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 its best to have the 64bit version of R for Rcrawler. You can install the release version of Rcrawler from CRAN using the command install.packages(“Rcrawler”, dependencies = TRUE) or install the development version from GitHub which may have errors using the command devtools::install_github(“salimk/Rcrawler”)
knitr is an R package for dynamic report generation that can be used to integrate various types of code into the R code such as Markdown, LyX, LaTeX, AsciiDoc, HTML, etc. knitr is a very important package to have if you are working in research for report creation and it is also very supportive in automating the data process from data analysis to creating a report about it. knitr also combines many features into a single package and also solves some problems with Sweave, which is a function in R that integrates R code into LyX or LaTeX documents. You can install the stable version of knitr on CRAN using install.packages(‘knitr’) or install the development version from XRAN using install.packages(‘knitr’, repos = c(‘https://xran.yihui.org’, ‘https://cran.r-project.org’)).
- Top 10 Python Libraries for Data Science in 2020
- Top R Libraries for Data Visualization in 2020
- Top 5 Programming Languages and their Libraries for Machine Learning in 2020
- Top 8 Python Libraries for Data Visualization
- Top 10 Data Science Skills to Learn in 2020
- Top Data Science Trends You Must Know in 2020
- Top Programming Languages for Data Science in 2020
- Difference Between Computer Science and Data Science
- Top 5 Image Loading Libraries in Android
- Top 8 Free Dataset Sources to Use for Data Science Projects
- Top Data Science Use Cases in Finance Sector
- How to Get Masters in Data Science in 2020?
- 5 Best Books to Learn Data Science in 2020
- Difference Between Data Science and Data Engineering
- Difference Between Data Science and Data Mining
- Difference Between Big Data and Data Science
- Difference Between Data Science and Data Analytics
- Difference Between Data Science and Data Visualization
- Top 5 PSUs for Computer Science Engineers in India
- 8 Useful Testing Tools, Libraries and Frameworks For React Developers
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.