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R Programming

Approx. 57 hours to complete

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Course Summary

This course teaches the fundamentals of programming in R, a popular language for statistical computing and graphics. Students will learn how to write their own functions, control structures, and work with data structures like vectors and data frames.

Key Learning Points

Learn a popular language for statistical computing and graphics
Master the fundamentals of programming in R
Write your own functions and work with data structures

Job Positions & Salaries of people who have taken this course might have

- USA: $60,000 - $100,000
- India: INR 5 - 10 lakhs
- Spain: €25,000 - €45,000
- USA: $60,000 - $100,000
- India: INR 5 - 10 lakhs
- Spain: €25,000 - €45,000
- USA: $70,000 - $110,000
- India: INR 6 - 12 lakhs
- Spain: €30,000 - €50,000
- USA: $60,000 - $100,000
- India: INR 5 - 10 lakhs
- Spain: €25,000 - €45,000
- USA: $70,000 - $110,000
- India: INR 6 - 12 lakhs
- Spain: €30,000 - €50,000
- USA: $90,000 - $150,000
- India: INR 8 - 18 lakhs
- Spain: €40,000 - €70,000

Learning Outcomes

Understand the basics of R programming
Master the use of R for data analysis and visualization
Be able to write your own functions and work with data structures

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics
No prior programming experience necessary

Course Difficulty Level

Intermediate

Course Format

Self-paced online course
Video lectures
Interactive quizzes
Programming assignments

Similar Courses

Data Science Essentials
Applied Data Science with Python

Related Education Paths

Notable People in This Field

Hadley Wickham
Romain François

Related Books

Description

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Knowledge

Understand critical programming language concepts
Configure statistical programming software
Make use of R loop functions and debugging tools
Collect detailed information using R profiler

Outline

Week 1: Background, Getting Started, and Nuts & Bolts
Installing R on a Mac
Installing R on Windows
Installing R Studio (Mac)
Writing Code / Setting Your Working Directory (Windows)
Writing Code / Setting Your Working Directory (Mac)
Introduction
Overview and History of R
Getting Help
R Console Input and Evaluation
Data Types - R Objects and Attributes
Data Types - Vectors and Lists
Data Types - Matrices
Data Types - Factors
Data Types - Missing Values
Data Types - Data Frames
Data Types - Names Attribute
Data Types - Summary
Reading Tabular Data
Reading Large Tables
Textual Data Formats
Connections: Interfaces to the Outside World
Subsetting - Basics
Subsetting - Lists
Subsetting - Matrices
Subsetting - Partial Matching
Subsetting - Removing Missing Values
Vectorized Operations
Introduction to swirl
Welcome to R Programming
About the Instructor
Pre-Course Survey
Syllabus
Course Textbook
Course Supplement: The Art of Data Science
Data Science Podcast: Not So Standard Deviations
Getting Started and R Nuts and Bolts
Practical R Exercises in swirl Part 1
Week 1 Quiz

Week 2: Programming with R
Control Structures - Introduction
Control Structures - If-else
Control Structures - For loops
Control Structures - While loops
Control Structures - Repeat, Next, Break
Your First R Function
Functions (part 1)
Functions (part 2)
Scoping Rules - Symbol Binding
Scoping Rules - R Scoping Rules
Scoping Rules - Optimization Example (OPTIONAL)
Coding Standards
Dates and Times
Week 2: Programming with R
Practical R Exercises in swirl Part 2
Programming Assignment 1 INSTRUCTIONS: Air Pollution
Week 2 Quiz
Programming Assignment 1: Quiz

Week 3: Loop Functions and Debugging
Loop Functions - lapply
Loop Functions - apply
Loop Functions - mapply
Loop Functions - tapply
Loop Functions - split
Debugging Tools - Diagnosing the Problem
Debugging Tools - Basic Tools
Debugging Tools - Using the Tools
Week 3: Loop Functions and Debugging
Practical R Exercises in swirl Part 3
Week 3 Quiz

Week 4: Simulation & Profiling
The str Function
Simulation - Generating Random Numbers
Simulation - Simulating a Linear Model
Simulation - Random Sampling
R Profiler (part 1)
R Profiler (part 2)
Week 4: Simulation & Profiling
Practical R Exercises in swirl Part 4
Programming Assignment 3 INSTRUCTIONS: Hospital Quality
Post-Course Survey
Week 4 Quiz
Programming Assignment 3: Quiz

Summary of User Reviews

Learn R programming with Coursera. This course has received positive reviews from many users. Students found the course to be engaging and informative, with a strong emphasis on hands-on practice. One key aspect that many users thought was good is the instructor's clear and concise explanations of complex concepts.

Pros from User Reviews

Engaging and informative course
Strong emphasis on hands-on practice
Clear and concise explanations of complex concepts
Good for beginners and intermediate level learners
Flexible learning schedule

Cons from User Reviews

Some users found the course to be too basic
Limited interaction with the instructor and other students
Not suitable for advanced learners
Some technical issues with the platform reported
Lack of real-world application examples

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