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🌟 R Skills Overview 🌟
1. Data Manipulation and Analysis
- A grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges.
- Tools to help you tidy your data; makes it easier to work with messy data.
- An extension of data.frame that provides fast aggregation of large datasets, fast ordered joins, and fast add/modify/delete of columns by reference.
2. Data Visualization
- A system for declaratively creating graphics, based on The Grammar of Graphics, which provides a coherent system for describing and building graphs.
- An interactive graphing library that makes it easy to create interactive plots and dashboards.
- A framework for building interactive web applications directly from R.
3. Statistical Modeling and Machine Learning
- A unified interface for creating and comparing the performance of machine learning models.
- An implementation of the Random Forest algorithm, used for classification and regression.
- A package that fits a generalized linear model via penalized maximum likelihood.
4. Time Series Analysis
- Tools for working with regularly and irregularly spaced time series data in R.
- Tools for time series forecasting, including exponential smoothing and ARIMA models.
- A forecasting tool for time series data that is particularly strong in handling seasonal effects.
5. Text Mining and Natural Language Processing (NLP)
- A framework for text mining applications within R, providing tools for text processing and term-document matrices.
- A package for managing, processing, and analyzing text data; includes fast and flexible tools for creating document-feature matrices.
- Integrates textual data into the tidyverse framework for easier manipulation and analysis.
6. Web Scraping and APIs
- Simplifies the process of scraping web pages, providing tools to read HTML and extract data.
- A package to handle HTTP requests, making it easy to communicate with web APIs and parse responses.
- A robust tool for parsing and generating JSON within R.
7. Reporting and Reproducible Research
- Combines R with Markdown to produce documents that include code, output, and text.
- A package that enables dynamic report generation, allowing you to include the results of your R code directly in your documents.
- A tool that allows the integration of R code into LaTeX documents for reproducible research.
8. Database Interaction
- A database interface definition for communication between R and relational database management systems.
- A driver for connecting R to PostgreSQL databases.
- A driver for connecting R to MySQL databases.
- A driver for connecting R to SQLite databases.
- Extending dplyr capabilities to work directly with databases, allowing you to manipulate remote tables as if they were local data frames.
9. Parallel Computing
- Base R support for parallel computation on multiple cores or nodes.
- An extension that allows for easy execution of code in parallel, loop by loop.
- A simple network of workstations package that facilitates parallel computing by running R code on multiple nodes.
10. Development and Collaboration Tools
- An integrated development environment (IDE) for R that makes coding, debugging, and visualization easy and intuitive.- [
