Dplyr: A Fundamental Tool for Data Manipulation

Dplyr is considered a leading library for data manipulation, offering a consistent set of verbs to address common data handling challenges:

• mutate(): Adds new variables as functions of existing ones.
• select(): Chooses variables based on their names.
• filter(): Selects cases according to their values.
• summarise(): Condenses multiple values into a single summary.
• arrange(): Alters the order of rows.

For more details, refer to the official documentation.

The video titled "9 R packages that EVERY Data Scientist must know (in 9-minutes)" provides a quick overview of essential R packages, including Dplyr and others, which are vital for any data scientist.

Tidyr: The Key to Tidy Data

Tidyr is an invaluable R library designed for reshaping and organizing datasets. It facilitates the conversion of deeply nested lists into rectangular data frames and aids in managing missing values. Tidyr's primary objective is to create tidy data, where:

• Each column represents a variable.
• Each row serves as an observation.
• Every cell contains a single value.

For additional insights, consult the official documentation.

ggplot2: Visualizing Data with Elegance

As the most widely used library for data visualization in R, ggplot2 is part of the tidyverse ecosystem, ensuring seamless integration with other tidyverse libraries. Based on the Grammar of Graphics, ggplot2 allows users to provide data and map variables to aesthetics like color and axes, managing the rest automatically.

As a comprehensive charting library, ggplot2 also offers various enhancements such as legends, themes, and labels.

For more information, check the official documentation.

Shiny: Building Interactive Web Applications

Shiny is an R package that simplifies the creation of interactive web applications directly from R. Users can host standalone apps on a webpage, embed them in R Markdown documents, or develop dashboards. Shiny applications can be enriched with CSS themes, htmlwidgets, and JavaScript functionality.

By using Shiny, developers can create robust interactive web applications entirely in R. The package transforms R code into the necessary HTML, CSS, and JavaScript, enabling various functionalities, including dataset manipulation based on user input or employing machine learning models.

For further details, explore the official documentation.

Caret: Streamlining Model Training

The caret package, short for Classification And REgression Training, offers functions to facilitate the model training process for complex regression and classification tasks. Caret serves as a comprehensive solution for machine learning in R, allowing users to fit over 230 different models with a single command through its robust train function.

For more on the caret package, see the official documentation.

Fable: Time Series Forecasting Made Easy

The fable R package provides a suite of commonly used univariate and multivariate time series forecasting techniques, including automated ARIMA modeling and exponential smoothing through state space models. The fable framework allows for model assessment, visualization, and combination, aligning with tidyverse principles.

For additional information, check the official documentation.