Intelligence analysis of data using the R language

1. Getting started with R and RStudio. Organizing the workspace: creating projects, scripts, reports in RMarkdown, working with variables. The main data type — the vector; indexing. Ways to create vectors, slices. Data types. Matrices. Basic descriptive statistics (min, max, mean, median). Indices and values. Accessing help documentation.

2. Rectangular data — tables. Creating tables from the command line. Structure and features of tabular data. Reading tables from files in various formats. Writing tables to files. Data manipulation using base R. The which function. Handling missing data. Lists. Loops. The apply family of functions.

3. Installing packages and loading libraries. Tidyverse. The logic of tidyverse packages and how they differ from base R. tibble vs data.frame. Creating a tibble. Basic tabular data manipulation with dplyr. Working with groups. Reading and writing tibble to files in various formats. Using the pipe operator in base R and in tidyverse.

4. Working with strings — stringr. Regular expressions. Working with factors — forcats. ggplot2 — the layered plotting logic. Scatter plots, bar charts. Plot parameter settings. Wide and long data formats. Working with color, shape, and transparency. Saving plots to files in various formats.

5. ggplot2, continued. Plot types: line charts, histograms, pie charts, bubble charts, density plots, box plots, violin plots, raincloud plots. Adding additional data to plots. Combining multiple tables. Using metadata. Advanced tabular data techniques with dplyr and tidyr.

6. Properties of the normal distribution and the central limit theorem. What statistics is and why it is needed. Population and sample. The difference between parameter estimation and its true value. Sample representativeness. What exploratory data analysis is and how to perform it. The null hypothesis (H0) and the alternative hypothesis; differences. Using computational simulations to assess hypothesis validity. Type I and Type II errors. P‑value and significance level. Z‑test and Student’s t‑test; conditions for use. One‑sample and two‑sample Student’s t‑tests.

7. Differences between paired and two‑sample Student’s t‑tests. Chi‑square test, Fisher’s test. Non‑parametric tests.

8. Writing custom functions. Building functions, function parameters, default values. Functional programming (the map family of functions). Handling exceptions/errors. Importing functions from a file. Creating R scripts that accept multiple input variables.

9. ggplot2 and beyond. Histograms with multiple axes. Plot grids. Creating panels of plots. Simple and complex heatmaps. Visualizing flows. Visualizing networks. Specific considerations for preparing figures for publication or presentation.

10. Basics and logic of Quarto. Creating interactive visualizations and customizable reports.

11. Creating and using interactive dashboards.

12. The multiple testing problem. Methods for addressing the multiple testing problem. FDR and FWER. What correlation is and what it is not. Correlation analysis.

13. ANOVA. Introduction to regression analysis.