Objectives:

– Understand what R is and why it is used.

– Install R and RStudio.

– Get familiar with the RStudio interface.

– Execute the first R commands and understand their syntax.

1.1. Introduction to R

– Overview, History, and Common Uses of R

– Advantages of R over other languages

1.2. Installation and Setup

– Downloading and Installing R

– Downloading and Installing RStudio

– Verifying the Installation: Launching RStudio and Checking R and RStudio versions

1.3. RStudio Interface

– Overview of the RStudio Interface

– Using Scripts: Creating, saving, and executing R scripts.

– Executing Basic Commands in R

1.4. Basic Syntax in R

– Variables and Data Types in R

– Basic Operations: Arithmetic and logical operations in R

– Basic Functions: Using predefined functions in R

1.5. Getting Started with Data in R

– Loading Data: Reading data files (CSV, Excel) in R

– Simple Data Manipulation in R                                   

Objectives:

– Understand the different data types available in R.

– Learn to create, manipulate, and transform these data types in R.

– Know the fundamental data structures and how to use them effectively in R.

2.1. Fundamental Data Types in R

– Numeric in R

– Character in R

– Logical: Boolean values TRUE and FALSE in R

– Factor: Categorical variables in R

2.2. Basic Data Structures

– Vectors in R

– Matrices in R

– Lists in R

– Data Frames in R

2.3. Conversion between Data Types in R

– Explicit Coercion: Converting objects from one type to another in R

– Handling Factors: Transforming levels in R

2.4. Operations on Data Structures in R

– Vectors: Indexing, arithmetic operations, manipulation in R

– Matrices: Indexing, row and column manipulation in R

– Lists: Accessing elements, manipulation in R

– Data Frames: Accessing columns, filtering, adding new columns in R

Objectives:

– Understand the basic principles of the dplyr package for data manipulation

– Learn to use the main dplyr functions to filter, select, sort, summarize, and modify data

– Integrate dplyr with other R ecosystem packages for seamless data analysis

3.1. Introduction to dplyr

– Overview and Benefits of the dplyr Package

– Installing and loading dplyr

3.2. Data Manipulation with dplyr

– Selecting Columns with select () in R

– Filtering Rows with filter () in R

– Sorting Data with arrange () in R

– Creating and Modifying Columns with mutate () in R

– Summarizing and Aggregating with summarise() and group_by() in R

– Combining Functions with %>% (Pipe Operator) in R

3.4. Optimizing Performance with dplyr

Objectives:

– Understand the basic principles of ggplot2 for creating data visualizations in R

– Learn to use the main ggplot2 functions to build and customize plots in R

– Integrate ggplot2 with other packages for advanced visualization in R

4.1. Introduction to ggplot2

– Overview of ggplot2 and Foundations of the Grammar of Graphics

– Installing and loading ggplot2

4.2. Creating Basic Plots in R

– Basic Syntax: Structure of a ggplot2 Plot in R

– Scatter Plot: Relationship between Two Numeric Variables in R

– Histogram: Distribution of a Numeric Variable in R

– Bar Chart: Comparison of Categorical Values in R

– Boxplot: Distribution of Data and Outlier Detection in R

4.3. Customizing Plots in R

– Adding Titles and Labels to Plots in R

– Modifying Colors and Shapes in Plots in R

– Customizing Plot Themes in R

4.4. Advanced Plots in R

– Faceting with facet_wrap() and facet_grid(): Creating Sub-Plots in R

– Time Series Plots in R

– Plots with geom_smooth() in R

4.5. Optimizing Plots: Creating Clear and Informative Plots in R

Objectives:

– Identify missing data in datasets in R

– Learn to manipulate missing values in R

– Apply techniques to manage missing data in R

5.1. Identifying Missing Data in R

– Missing Values in R

– Functions to Identify Missing Data in R

5.2. Manipulating Missing Data in R

– Removing Missing Values in R

– Replacing Missing Values in R

– Mean/Median Imputation in R

– Regression Imputation in R

5.3. Analyzing Missing Data in R

– Analyzing Patterns of Missing Data (MCAR, MAR, MNAR) in R

– Descriptive Statistics of Missing Data in R

Objectives:

– Understand and apply descriptive statistics to summarize data in R

– Perform hypothesis tests to draw conclusions from data in R

– Apply simple regression methods to model relationships between variables in R

6.1. Descriptive Statistics in R

– Statistical Summary of Data in R

– Data Distribution: Visualizing data distribution in R

– Frequency Tables in R

6.2. Hypothesis Tests in R

– Mean Test (Student’s t-test) in R

– Chi-Square Test: Testing independence between two categorical variables in R

– Normality Test: Checking if data follows a normal distribution in R

– Equality of Variances Test (Levene’s Test) in R

6.3. Simple Linear Regression in R

– Linear Regression Model in R

– Interpreting Results: Interpreting coefficients, p-values, etc. in R

– Prediction with the Model in R

6.4. Model Validation in R

– Residual Analysis: Checking residuals to evaluate model fit in R

– Cross-Validation: Evaluating model performance using cross-validation in R

Objectives:

– Understand the fundamental principles of linear models in R

– Learn to build, fit, and interpret linear models in R

– Evaluate and validate the performance of linear models in R

7.1. Foundations of Linear Models in R

– Concept of Linear Regression in R

– Assumptions of Linear Models in R

7.2. Building a Linear Model in R

– Creating a Linear Model in R

– Summary of the Linear Model in R

7.3. Interpreting Results

– Regression Coefficients: β coefficients in R

– p-values and Significance in R

– R² and Adjusted R²: proportion of variance explained by the model in R

– Standard Errors and Confidence Intervals in R

7.4. Validating the Linear Model in R

– Residual Analysis in R

– Residual Diagnostics in R

– Cross-Validation in R

7.5. Multiple Linear Regression in R

– Introduction to Multiple Regression in R

– Multicollinearity in R

Objectives

– Understand the fundamental concepts of classification models in R

– Learn to build, evaluate, and interpret classification models in R

– Assess the performance of classification models in R

8.1. Introduction to Classification Models in R

– Concept of Classification in R

– Types of Classification Models in R

8.2. Classification Models in R

– Logistic Regression in R

– Summary of the Model in R

– Decision Trees in R

– Random Forests in R

– Support Vector Machines (SVM) in R

– k-Nearest Neighbors (k-NN) in R

8.3. Evaluating Classification Models in R

– Confusion Matrix: Description and table() function in R

– Performance Metrics: Accuracy, Recall, F-measure, and AUC-ROC in R

– Cross-Validation: Description and cv.glm() function in R

Objectives

– Understand the fundamental concepts of PCA

– Learn to apply PCA in R

– Interpret the results of PCA in R

9.1. Foundations of Principal Component Analysis (PCA)

– Concept of PCA

– Properties of Principal Components: Orthogonality and Maximum Variance

– Key Elements: Covariance Matrix and Eigenvalues/Eigenvectors

9.2. Implementing PCA in R

– Preparing Data for PCA in R

– Calculating PCA: prcomp() function in R

– PCA Results in R

9.3. Interpreting Results

– Explained Variance in R

– Scree Plot: Description and screeplot() function in R

– Biplot: Description and biplot() function in R

– Loadings in R

9.4. Visualization and Applications of PCA in R

– Visualizing Principal Components in R

– Applications of PCA in R

Objectives

– Understand the fundamental concepts of time series  in R

– Learn to manipulate and visualize time series in R

– Apply models to analyze and forecast time series in R

10.1. Introduction to Time Series in R

– Concept of Time Series in R

– Components of Time Series in R

10.2. Manipulation and Visualization of Time Series in R

– Creating Time Series Objects in R

– Visualizing Time Series in R

10.3. Decomposition of Time Series in R

– Additive and Multiplicative Decomposition in R

– STL Model (Seasonal and Trend decomposition using Loess) in R

10.4. Modeling Time Series in R

– ARIMA Models (AutoRegressive Integrated Moving Average) in R

– Forecasting with ARIMA: forecast() function in R

– Exponential Smoothing Models (ETS): ets() function in R

– Forecasting with ETS in R

10.5. Evaluating Time Series Models in R

– Error Measures: RMSE, MAE, and MAPE in R

– Cross-Validation: tsCV() function in R

Objectives:

– Understand the fundamental concepts of clustering

– Learn to apply different clustering techniques in R

– Evaluate and interpret clustering results for further analysis

11.1. Introduction to Clustering in R

– Clustering Concept

– Similarity Measures: Euclidean Distance, Manhattan Distance, and Minkowski Distance

11.2. Preparing Data for Clustering in R

– Standardizing Data in R

– Selecting Variables in R

11.3. Hierarchical Clustering

– Description: Agglomerative/Divisive Methods in R

– Implementation in R: hclust() function and Dendrogram in R

– Cutting Clusters: cutree() function in R

11.4. K-means Clustering in R

– Principle and Initialization of K-means Clustering in R

– Implementation in R: kmeans() function and Cluster Visualization in R

11.5. DBSCAN Clustering

– Principle and Parameters of DBSCAN Clustering in R

– Implementation in R: dbscan() function and Cluster Visualization

11.6. GMM (Gaussian Mixture Models) Clustering in R

– Principle and Parameters of GMM Clustering in R

– Implementation in R: Mclust() function and Cluster Visualization

11.7. Evaluating and Validating Clusters in R

– Evaluation Indices: Silhouette Index and Davies-Bouldin Index in R

– Cross-Validation in R

Objectives

– Understand the fundamental concepts of network analysis

– Learn to build, visualize, and analyze networks in R

– Extract meaningful information from networks in R

12.1. Introduction to Networks

– Network Concept: Description and Examples

– Types of Networks: Undirected, Directed, Weighted, and Unweighted

12.2. Building Networks in R

– Used Packages: igraph, network, sna in R

– Creating a Network in R

12.3. Visualizing Networks in R

– Basic Visualization with igraph in R

– Customizing Visualization in R

– Advanced Visualization with ggraph in R

12.4. Analyzing Networks in R

– Centrality Measures in R

– Communities: Community Detection in R

– Path Analysis: Shortest Path in R

– Cohesion and Clustering: Clustering Coefficient in R

12.5. Advanced Methods in R

– Network Modeling in R

– Dynamic Network Analysis in R

Objectives

– Understand the fundamental principles of functional programming

– Learn to apply these principles in the R language

– Use high-level functions to manipulate data and create efficient solutions in R

13.1. Introduction to Functional Programming

– Principles of Functional Programming

– Advantages of Functional Programming

13.2. Functions in R

– Defining Functions: Syntax and Anonymous Functions in R

– Higher-Order Functions in R

13.3. Data Manipulation with High-Level Functions in R

– lapply(), sapply(), vapply(), and mapply() functions in R

– Map() and Reduce() functions in R

13.4. Advanced Functional Programming in R

– Partially Applied Functions and Currying in R

– Immutable Functions and Chaining in R

13.5. Practical Applications of Functional Programming in R

– Data Transformation with dplyr in R

– Creating Reusable Functions in R

Objectives

– Understand the basic concepts of creating R packages

– Learn to create, document, test, and distribute an R package

– Use tools and best practices for package development

14.1. Introduction to R Packages

– Definition of a Package

– Structure of an R Package

14.2. Creating a Basic Package in R

– Installing Tools: Recommended Packages (devtools, roxygen2, usethis) in R

– Creating a New Package in R

14.3. Developing Functions in R

– Writing Functions: Definition and Placement of Functions in R

– Documentation with roxygen2: Syntax and Generating Documentation in R

14.4. Managing Dependencies

– Declaring Dependencies in R

– Using Imports and Suggests to Specify Required Packages in R

14.5. Testing and Validation

– Unit Testing with testthat: Installation, Creating Tests, and Test Structure in R

– Running Tests in R

14.6. Creating Vignettes in R

– Creating Vignettes with usethis in R

– Generating Vignettes in R

14.7. Building and Installing the Package in R

– Building the Package: Commands and Generating a .tar.gz or .zip File in R

– Installing the Package: Commands in R

14.8. Publishing and Distributing in R

– Publishing on CRAN: Preparation and Submission in R

– Publishing on GitHub: Creating a Repository and Installing from GitHub in R

– Version Management: Using usethis for Versions in R

Objectives

– Learn to connect to databases from R

– Perform read, write, and data manipulation operations in R

– Understand best practices for interacting with databases in R

15.1. Introduction to Databases

– Types of Databases: Relational (SQL) and NoSQL in R

– Key Concepts: Tables, Schemas, and SQL Queries in R

15.2. Connecting to a SQL Database in R

– Recommended Packages: DBI, RSQLite, RMySQL, RPostgres, odbc in R

– Installing Packages in R

– Connecting to a SQLite Database in R

– Connecting to a MySQL Database in R

– Connecting to a PostgreSQL Database in R

15.3. Executing SQL Queries in R

– Executing Read Queries in R

– Executing Write Queries in R

– Manipulating Tables in R

15.4. Using dplyr with Databases in R

– Installing and Loading Packages: Installation and Loading

– Connecting with dbplyr: Installation and Loading

– Working with Databases via dplyr

15.5. Transaction Management in R

– Starting and Committing Transactions in R

– Rolling Back Transactions in R

15.6. Error Handling and Debugging in R

– Error Handling: Using tryCatch to Handle Errors in R

– Debugging: Example of Using dbplyr to Diagnose Generated Queries in R

15.7. Security and Performance in R

– Using Parameters to Avoid SQL Injection in R

– Optimizing Queries and Loading Data into Memory in R

15.8. Interacting with NoSQL Databases in R

– Connecting to MongoDB with mongolite in R

– Operations on MongoDB in R

Objectives

– Learn to manipulate and clean text data in R

– Explore text analysis and information extraction techniques in R

– Use R tools and packages for text processing and analysis

16.1. Introduction to Text Data Processing in R

– Key Concepts: Text Mining and NLP (Natural Language Processing) in R

– Sentiment Analysis, Named Entity Recognition, Text Classification, etc. in R

16.2. Preparing Text Data in R

– Loading Data: Example with a CSV File in R

– Cleaning Text: Removing Spaces and Converting to Lowercase in R

– Tokenization: Separating Words in R

16.3. Text Analysis in R

– Basic Text Analysis Statistics in R

– Word Clouds: Visualization with wordcloud in R

16.4. Sentiment Analysis in R

– Using Sentiment Lexicons in R

– Visualizing Sentiments in R

16.5. Information Extraction

– Named Entity Recognition: Using the spacyr Package in R

– Pattern Search with Regular Expressions in R

16.6. Text Modeling

– Creating Term Matrices in R

– Topic Modeling: Using topicmodels in R

16.7. Advanced Text Processing in R

– Syntax and Grammar Analysis in R

– Semantic Relationship Analysis in R

16.8. Using R for Text Processing with Unstructured Data

– Text Processing from Web Sources in R

– Analyzing PDF Documents in R

Objectives

– Learn to manipulate and analyze large datasets in R

– Use tools and techniques to optimize performance and memory management

– Explore specific approaches for working with large datasets

17.1. Introduction to Managing Large Datasets in R

– Challenges: Performance, Memory, Scalability in R

– Using optimization techniques for large dataset management in R

17.2. Memory Optimization in R

– Efficient Memory Use in R

– Managing Objects in Memory in R

17.3. Manipulating Large Datasets in R

– Using dplyr and data.table for operations in R

– Streaming data analysis in R

17.4. Data Management in Databases in R

– Storing data in databases in R

– Remote operations in R

17.5. Parallel Data Processing in R

– Using parallel for parallel processing in R

– Using furrr for parallel processing with purrr in R

17.6. Using Chunking Techniques in R : Batch processing in R

17.7. Optimization Techniques in R

– Optimizing queries in R

– Code profiling in R

17.8. Visualizing Large Datasets in R

– Using ggplot2 with subsamples in R

– Using packages for visualizing large datasets in R

Objectives

– Deepen knowledge in machine learning with advanced techniques

– Use complex models and optimization techniques in R

– Explore methods such as deep learning in R

18.1. Introduction to Advanced Techniques in R

– Understanding the need for advanced techniques

– Contextualizing advanced models in practical application scenarios

– Complex problems requiring non-linear models or high-dimensional approaches

18.2. Random Forest and Gradient Boosting Models in R

– Basic principles and implementation with randomForest in R

– Boosting principle and implementation with xgboost in R

– Random Forest vs Gradient Boosting in R

18.3. Ensemble Techniques in R

– Bagging (Bootstrap Aggregating) in R

– Boosting in R

– Stacking in R

18.4. Deep Learning in R

– Artificial Neural Networks (ANN) in R

– Convolutional Neural Networks (CNN) in R

– Recurrent Neural Networks (RNN) and LSTM in R

18.5. Optimization and Hyperparameter Tuning in R

– Hyperparameter Search in R

– Cross-Validation in R

– Advanced Optimization Techniques in R

18.6. Model Interpretation Techniques in R

– Model Interpretability in R

– Visualizing Variable Importance in R

18.7. Advanced Applications in R

– Advanced Unsupervised Learning in R

– Reinforcement Learning Basics and Libraries in R

Objectives:

– Understand the basic concepts and features of RMarkdown

– Learn to create dynamic documents, reports, and presentations in R

– Explore customization and export options to produce professional and interactive documents

19.1. Introduction to RMarkdown

– What is RMarkdown?

– Basic Structure of an RMarkdown Document

– Output Types: HTML, PDF, and Word

19.2. Creating Dynamic Documents

– Inserting R Code: Executing Code and Code Options

– Inserting Text and Markdown in R

19.3. Creating Reports in R

– Dynamic Reports with Descriptives and Visualizations in R

– Customizing Reports: Using Templates in R

– Format Options: HTML and PDF in R

19.4. Creating Presentations in R

– Presentations with ioslides: Creating Interactive HTML Presentations in R

– Presentations with slidy: Creating Simple HTML Presentations in R

– Presentations with beamer: Creating PDF Presentations with LaTeX in R

19.5. Advanced Options and Customization

– Customizing Layout: CSS for HTML and LaTeX Packages in R

– Adding Interactive Widgets: Using htmlwidgets for Interactivity in R

19.6. Integration with Other Tools in R

– Integration with Shiny: Creating Shiny Applications in RMarkdown

– Integration with knitr for Tables: Creating Dynamic Tables in R

19.7. Debugging and Deployment in R

– Debugging RMarkdown Documents: Checking Code Errors in R

– Deploying Online: Publishing on RStudio Connect or GitHub Pages in R