Essential R Packages for Every Stage of a Data Science Project

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A comprehensive data science project in R may involve various stages, including data cleaning, exploration, analysis, modeling, and visualization. Below is a list of R packages that cover different aspects of a data science project from start to end:

1. Data Import and Cleaning:

• readr: For reading rectangular data (like CSVs) quickly.
• dplyr and tidyr: For data manipulation and cleaning.
• stringr: For working with strings.

2. Exploratory Data Analysis (EDA):

• ggplot2: For creating sophisticated and customizable visualizations.
• tidyr and dplyr: For data manipulation and summarization.
• summarytools: For creating exploratory data analysis summaries.

3. Statistical Analysis:

• stats: Base R package for fundamental statistical functions.
• car: For companion functions for regression modeling.
• psych: For psychological and psychometric research functions.
• broom: For converting statistical analysis objects into tidy format.

4. Machine Learning: