R Programing (R Language) – Complete Course
About R Programing
This complete R programming course covers data analysis, statistical computing, data visualization, and real-world applications using R language, helping learners gain practical skills for research, analytics, and data-driven decision making.
Module 1: Introduction to R
- What is R?
- Applications of R (Data Analysis, Statistics, ML)
- R vs Python
- Installing R & RStudio
- R environment & workspace
- R packages & CRAN
Module 2: R Basics
- R syntax & structure
- Variables & data types
- Operators
- Comments
- Input & output
- Type conversion
Module 3: Data Structures in R
- Vectors
- Lists
- Matrices
- Arrays
- Data Frames
- Factors
- Attributes
Module 4: Control Structures
- Conditional statements (if, ifelse, switch)
- Loops (for, while, repeat)
- break & next
- Vectorized operations
Module 5: Functions in R
- Built-in functions
- User-defined functions
- Function arguments
- Scope & environments
- Anonymous functions
- Apply family (apply, lapply, sapply, tapply)
Module 6: Data Import & Export
- Reading data from CSV, Excel, TXT
- Importing from databases
- Reading JSON & XML
- Writing data to files
- Data handling best practices
Module 7: Data Manipulation
- Data subsetting
- Data cleaning
- Handling missing values
- Sorting & filtering
- Data transformation
- dplyr package
- filter, select, mutate, arrange, summarize
Module 8: Data Visualization
- Base R plotting
- ggplot2 basics
- Bar charts, line graphs, histograms
- Box plots & scatter plots
- Customizing plots
- Data visualization best practices
Module 9: Exploratory Data Analysis (EDA)
- Descriptive statistics
- Correlation analysis
- Outlier detection
- Data distribution analysis
- Summary reports
Module 10: Statistical Analysis in R
- Probability distributions
- Hypothesis testing
- t-test, z-test
- ANOVA
- Chi-square test
- Correlation & regression analysis
Module 11: Regression Models
- Linear regression
- Multiple regression
- Logistic regression
- Model interpretation
- Model diagnostics
Module 12: Machine Learning with R (Intro)
- ML concepts overview
- Supervised learning
- Unsupervised learning
- k-NN
- Decision Trees
- Random Forest (intro)
- Model evaluation
Module 13: Time Series Analysis
- Time series concepts
- Trend & seasonality
- Moving averages
- ARIMA models
- Forecasting
Module 14: Text Mining & NLP (Intro)
- Text preprocessing
- Tokenization
- Term frequency
- Sentiment analysis
- tm & tidytext packages
Module 15: Working with Big Data (Intro)
- Big data concepts
- Integration with Hadoop/Spark
- Using sparklyr
- Large dataset handling
Module 16: R for Data Reporting
- R Markdown
- Creating reports
- Dashboards using Shiny (intro)
- Data storytelling
Module 17: Advanced R Concepts
- Object-oriented programming in R
- S3, S4, R6 classes
- Performance optimization
- Parallel computing
- Memory management
Module 18: Hands-On Projects
- Data analysis project
- Sales data analysis
- Customer segmentation
- Forecasting project
- Statistical case study
- Final capstone project
Module 19: R Packages & Ecosystem
- tidyverse
- caret
- ggplot2
- shiny
- data.table
- zoo, forecast
Module 20: Career & Certification Guidance
- R-related job roles
- Resume & portfolio building
- Interview questions
- Certification roadmap
R Programming Master
Instructor
An R programming instructor teaches data analysis, statistics, visualization, and practical applications using R language for analytics and research.