Course Outline:
Introduction to R:
Overview of R and its history
Installing and setting up R and RStudio
Basic R commands and syntax
Understanding RStudio interface
Data Structures in R:
Vectors, lists, matrices, arrays
Data frames and tibbles
Factors
Data Manipulation:
Importing and exporting data (CSV, Excel, databases)
Data cleaning and preprocessing
Using dplyr for data manipulation (select, filter, mutate, summarize, arrange)
Working with dates and times using lubridate
Data Visualization:
Introduction to ggplot2
Creating various types of plots (scatter plots, bar charts, histograms, box plots)
Customizing plots (themes, labels, annotations)
Advanced visualizations (faceting, complex multi-layered plots)
Basic Programming Concepts:
Control structures (if, else, for, while, repeat)
Functions in R (writing and using functions)
Debugging and error handling
Statistical Analysis:
Descriptive statistics (mean, median, mode, variance, standard deviation)
Inferential statistics (hypothesis testing, t-tests, chi-square tests)
Regression analysis (linear regression, multiple regression)
ANOVA
Advanced Data Manipulation:
Reshaping data with tidyr (gather, spread, separate, unite)
Merging and joining datasets
Working with large datasets
Working with Strings:
String manipulation using stringr
Regular expressions
Handling Missing Data:
Identifying missing data
Techniques to handle missing data (imputation, omission)
Functional Programming:
Using purrr for functional programming
Applying functions to lists and vectors
Model Building:
Building predictive models
Model evaluation and validation
Introduction to machine learning with R (decision trees, random forests)
Time Series Analysis:
Understanding time series data
Decomposition of time series
Forecasting models (ARIMA, exponential smoothing)
Reporting and Communication:
Creating reports with R Markdown
Generating dynamic documents
Shiny for interactive web applications
Package Development:
Introduction to R packages
Writing and documenting your own package
Sharing and distributing packages
Case Studies and Practical Applications:
Real-world examples and case studies
Practical exercises and projects
Best practices and tips for efficient coding
Skills Gained:
Proficiency in using R and RStudio for data analysis and visualization
Ability to manipulate and preprocess data effectively
Skills in statistical analysis and model building
Competence in creating professional reports and interactive applications
Knowledge of advanced data manipulation and programming techniques in R
Target Audience:
Data scientists
Statisticians
Data analysts
Researchers
Students and professionals interested in data analysis and visualization