R Programming

R Programming
₹1,000₹1,500
60 mins
One-To-One Session

“Let R reveal the story in your data.”


Learn R Programming step by step, even if you are new to coding. Start with the basics and move on to working with data, creating charts, and performing statistical analysis using tools like tidyverse, dplyr, ggplot2, and caret. Real examples help you build confidence for studies, projects, and work.


🎓 R Language Learning Roadmap (Step-by-Step)

📘Step 1: Introduction to R

  1. What is R and where it is used
  2. Installing R and RStudio
  3. R interface and basic commands

📘Step 2: R Basics

  1. Variables and data types
  2. Vectors, matrices, lists, and data frames
  3. Basic arithmetic and logical operations

📘Step 3: Data Handling in R

  1. Importing data (CSV, Excel, text files)
  2. Data indexing and sub setting
  3. Handling missing values

📘Step 4: Control Structures

  1. If–else statements
  2. For and while loops
  3. Apply family of functions

📘Step 5: Functions in R

  1. Writing user-defined functions
  2. Function arguments and returns
  3. Using built-in R functions

📘Step 6: Data Manipulation

  1. Data cleaning and transformation
  2. Using dplyr for filtering, selecting, grouping
  3. Working with tidyverse

📘Step 7: Data Visualization

  1. Basic plots in R
  2. Creating charts using ggplot2
  3. Customizing plots and themes

📘Step 8: Descriptive Statistics

  1. Mean, median, variance, and standard deviation
  2. Summary statistics
  3. Exploratory Data Analysis (EDA)

📘Step 9: Statistical Inference

  1. Probability distributions
  2. Hypothesis testing
  3. Confidence intervals

📘Step 10: Regression & Modeling

  1. Simple and multiple linear regression
  2. Logistic regression
  3. Model diagnostics and interpretation

📘Step 11: Advanced Statistical Methods

  1. ANOVA
  2. Non-parametric tests
  3. Multivariate analysis

📘Step 12: Machine Learning in R

  1. Classification and clustering
  2. Using caret and mlr
  3. Model evaluation techniques

📘Step 13: Working with Real Data

  1. Case studies and datasets
  2. Data analysis projects
  3. Report generation

📘Step 14: Reproducible Research

  1. R Markdown
  2. Creating reports and presentations
  3. Version control basics

📘Step 15: Practice & Projects

  1. Mini projects
  2. Research-based analysis
  3. Industry-oriented projects


❓Invitee Questions (R Programming)

🔹Question 1: What would you like to learn or discuss during this R Programming session?

🔹Question 2: Are you new to R, or do you already have some experience?

🔹Question 3: Do you want help with data analysis, data visualization, or statistical modeling in R?

🔹Question 4: Are you working on any assignment, project, or dataset that you would like to discuss?

🔹Question 5: Which R topics are you most interested in?

(Basics, data cleaning, ggplot2, tidyverse, statistics, models, etc.)

🔹Question 6: Is this session for learning basics, exam preparation, research work, or job-related skills?

🔹Question 7: Do you prefer a step-by-step explanation or hands-on practice during the call?

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