R is a powerful programming language for data science, and there are a number of great libraries that can be used to extend its functionality. In this video, we will discuss the top 10 R libraries for data science in 2023.
dplyr R libraryOpens in a new window
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dplyr R library
1. dplyr is a library for data manipulation. It provides a number of functions for selecting, filtering, and transforming data frames. 2. ggplot2 is a library for data visualization. It provides a powerful and flexible framework for creating charts, graphs, and maps. 3. tidyverse is a collection of R packages that work together to make data science easier. It includes dplyr, ggplot2, and a number of other popular libraries. 4. caret is a library for machine learning. It provides a number of functions for training and evaluating machine learning models. 5. randomForest is a library for random forest machine learning. It provides a number of functions for training and evaluating random forest models. 6. xgboost is a library for extreme gradient boosting machine learning. It provides a number of functions for training and evaluating extreme gradient boosting models. 7. NLP is a library for natural language processing. It provides a number of functions for processing and analyzing text data. 8. Rcpp is a library for integrating R with C++. It allows R users to write code in C++ that can be called from R. 9. shiny is a library for creating interactive web applications. It allows R users to create web applications that can be used to explore and visualize data. 10. plotly is a library for creating interactive visualizations. It allows R users to create visualizations that can be embedded in web pages or shared on social media.
These are just a few of the many great R libraries that are available for data science. The best library for you will depend on your specific needs.
