Data Science: R Basics

Chapters

1Getting Started with R and RStudio

2R Language Fundamentals

3Data Types and Structures

4Vectors and Factors

5Matrices, Arrays, and Lists

6Data Frames and Tibbles

7Importing, Exporting, and Reading Data

8Data Cleaning and Transformation with dplyr

9Reshaping Data with tidyr

10Working with Dates, Strings, and Categories

11Exploratory Data Analysis and Visualization with ggplot2

12Control Flow, Functions, and Apply Family

13Reproducibility, Projects, and Packages

RStudio projects workflow Working directories best practices Script organization and sourcing Using renv for dependencies Version control with Git GitHub integration in RStudio Quarto and R Markdown basics Parameterized reports Caching and targets overview Data dictionaries and metadata Packrat vs renv migration Writing your first package Testing with testthat Documentation with pkgdown Sharing and publishing results

14Basics of Statistical Modeling in R

15Working with Larger Data and Performance

Courses/Data Science: R Basics/Reproducibility, Projects, and Packages

Reproducibility, Projects, and Packages

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Structure analyses for reliability using projects, dependency management, and reporting.

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Caching and targets overview

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