1.1 Why R?
R is not a programming language like C or Java. It was not created by software engineers for software development. Statisticians developed it as an interactive environment for data analysis. Interactivity is an indispensable feature in data science because, as you will soon learn, the ability to quickly explore data is necessary for success in this field. However, like in other programming languages, you can save your work as scripts that can be easily executed at any moment. These scripts record the analysis you performed, a key feature that facilitates reproducible work. If you are an expert programmer, you should not expect R to follow the conventions you are used to since you will be disappointed. If you are patient, you will come to appreciate R's unequal power in data analysis and, specifically, data visualization.
R can be used to compute a large variety of classical statistic tests, including:
Student’s t-test comparing the means of two groups of samples
Wilcoxon test, a non-parametric alternative of the t-test
Analysis of variance (ANOVA) comparing the means of more than two groups
Chi-square test comparing proportions/distributions
Correlation analysis for evaluating the relationship between two or more variables
It’s also possible to use R for performing classification analysis such as:
Principal component analysis
clustering
Many types of graphs can be drawn using R, including box plots, histograms, density curves, scatter plots, line plots, bar plots,
Why learn R?
R is open source, so it’s free.
R is cross-platform compatible and can be installed on Windows, MAC OSX and Linux.
R provides a wide variety of statistical techniques and graphical capabilities.
R provides the possibility to make reproducible research by embedding scripts and results in a single file.
R is highly extensible, and it has thousands of well-documented extensions (named R packages) for a wide range of applications in statistical analysis, health care,…
1.2 R Installation
R language installation can be found at this link https://cran.mirror.ac.za/. It has different installation instructions for different operating systems:
Windows: https://cran.mirror.ac.za/bin/windows/
Linux: https://cran.mirror.ac.za/bin/linux/
McOS : https://cran.mirror.ac.za/bin/macosx/
1.3 R-Studio (Integrated development environment IDE )
R Studio is an integrated development environment (IDE) that supports the R programming language. Its user-friendly interface allows for writing, running, debugging, and visualizing R code. Here's a breakdown of what R Studio provides with the R language.
Script Editor: R Studio provides a text editor for writing and editing R scripts. This editor typically includes syntax highlighting, code completion, and automatic indentation to assist with writing code efficiently.
Console: The console in R Studio allows you to interactively execute R code. You can type commands directly into the console and see the results immediately. It’s useful for quick experimentation and testing code snippets.
Workspace: R Studio provides a pane that displays information about the objects (variables, functions, etc.) currently stored in the R environment. You can view and manage these objects from the workspace pane.
File Browser: R Studio includes a file browser pane that allows you to navigate your file system and manage files and directories. You can also open, save, and organize R scripts, data files, and other resources from this pane.
Plots and Graphics: R Studio provides integrated support for creating and visualizing plots and graphics. You can generate various plots using R's powerful visualization packages and view them directly within R Studio.
Packages and Help: R Studio offers tools for managing R packages, including installing, updating, and loading packages. It also provides access to R’s extensive documentation and help resources, allowing you to look up functions, packages, and syntax information.
Version Control: R Studio includes built-in support for version control systems like Git and SVN. You can connect your projects to version control repositories, track changes, and collaborate with others on your R projects.
Markdown and Notebooks: R Studio supports authoring documents using Markdown, a lightweight markup language. You can create reproducible reports, documents, and presentations combining R code, output, and narrative text. R Markdown Notebooks provide an interactive environment for mixing code, output, and explanatory text in a single document.
Installation Of R studio https://posit.co/download/rstudio-desktop/
1. 4 R Packages
R packages consist of R functions, compiled code, and sample data. In the R environment, they are placed in a library directory. R automatically installs a set of packages during installation. Later, more packages are added, as they are required for a specific purpose. When we first launch the R console, only the default packages are available. Other packages that are already installed must be manually loaded before the R application may use them.