baggiponte / ta-business-statistics-2024

🎓 Business Statistics A.Y. 2023-2024

Welcome to the tutorials of the Business Statistics course (AY 2023-2024). This repository contains the code that is presented at each lecture.

The code is stored in Rmarkdown files. These are notebooks that alternate code and descriptions. It's slightly different from a regular script (ending in .R), but you can treat it in the same way. For all that matters, treat it as a script with a nicer way to combine code and text.

To run the notebooks, you should make sure the {rmarkdown} package is installed.

install.packages("rmarkdown")

There will be 8 classes, divided as follows:

• Part 1 (two classes): Introduction to R and programming.
• Part 2 (three classes): Clustering methods.
• Part 3 (three classes): Time series analysis.

Part 1: Introduction to R Programming

This part provides an overview of the R programming language and introduce the basic components of R.

1. Installing R and RStudio + Elements of R: working with strings, numbers, and operators

• Introduction to R and RStudio
• How to run R code in the Console
• Persist code in R scripts and run them
• Literate programming in RMarkdown files
• Setting up your R environment (installing packages, setting working directory, etc.)
• R syntax basics (comments, variables, data types, operators)
• Using R as a calculator (basic arithmetic, logical operations, comparison operators)
• Basic data types: strings
• Define your own functions
• R built-in functions for data manipulation (sum, mean, max, min, etc.)

2. Vectors, Matrices, and DataFrames + Introduction to statistical computations

• Creating and working with vectors
• Creating and accessing subsets of data in R (indexing, slicing)
• Apply functions to vectors
• Sample from random variables
• data.frames introduction
• factor datatypes.
• Introduction to the {tidyverse} and tibbles.
• Introduction to exploratory data analysis and data visualisation with {ggplot2}

Appendices:

• Matrices and Lists, as well as how to index and perform operations on them.
• Control structures in R (if-else statements, loops)
• Elements of the {tidyverse}:
  • Plotting with {ggplot2}
  • Data manipulation with {dplyr}
  • The pipe operator