R Programming Course - Master Data Analysis & Visualization
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Master R Programming for Data Science

Learn R Programming to analyze and visualize data, build statistical models, and perform machine learning tasks. Gain hands-on experience with R’s extensive libraries to solve real-world data problems and gain insights from large datasets.

R Programming Course

Description

Comprehensive R Programming Training

Urbancode's R Programming course provides a deep dive into data analysis, statistical modeling, and data visualization using the R language. The curriculum is designed to enable participants to manipulate large datasets, perform statistical analyses, and create compelling data visualizations. This course is essential for professionals in data science, analytics, and research fields, empowering them to make data-driven decisions and extract valuable insights from complex data sets.

This course includes

  • 32 hrs Instructor-Led Training & Project Work
  • Job Assistance
  • Mentor Support
  • Certificate of completion

Course Content

R Programming - Beginner Level

Introduction to R

  1. Overview of R Programming
    • Introduction to R and its applications in data analysis and statistics.
    • Understanding the RStudio environment and interface.

R Basics

  1. Data Structures in R
    • Introduction to vectors, matrices, lists, and data frames.
    • Working with different data types and structures in R.
  2. Basic Operations
    • Performing basic arithmetic operations and logical operations in R.
    • Using functions for data manipulation and analysis.

Data Import and Export

  1. Working with External Data
    • Importing data from various sources (CSV, Excel, databases, etc.).
    • Exporting processed data to different formats.

Data Cleaning and Transformation

  1. Data Manipulation
    • Using dplyr, tidyr, and other packages for data manipulation.
    • Handling missing data, data filtering, and sorting.
  2. Data Transformation
    • Data aggregation and summarization using R.
    • Reshaping data with pivoting techniques.

Data Visualization

  1. Introduction to Data Visualization
    • Creating basic plots and charts using base R plotting functions.
    • Using ggplot2 for advanced data visualization.
  2. Customizing Visuals
    • Adding labels, themes, and customizations to plots.
    • Saving and exporting visualizations for reports.
R Programming - Advanced Level

Advanced Data Analysis with R

  1. Statistical Analysis
    • Performing statistical tests and analyses in R (t-tests, ANOVA, regression).
  2. Time Series Analysis
    • Exploring time series data, decomposition, and forecasting models.

Advanced Data Manipulation

  1. Using Apply Functions
    • Applying lapply, sapply, tapply for data manipulation.
    • Vectorizing operations for efficient data processing.

Building R Functions

  • Writing custom functions for repetitive tasks.
  • Using conditional statements and loops within functions.

Building Interactive Dashboards

  1. Creating Shiny Apps
    • Introduction to Shiny for building interactive web applications with R.
    • Adding interactivity and custom widgets to dashboards.

Efficiency and Optimization

  1. Optimizing R Code
    • Techniques for improving the performance of R scripts.
    • Memory management and profiling for large datasets.

Data Reporting and Automation

  1. Generating Reports
    • Using R Markdown for dynamic report generation.
    • Automating data analysis workflows with R scripts.

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