IIRF Online > Development > Data Science > R (programming language) > Data Analysis with R Programming
Data Analysis with R Programming by Coursera
This course is part of Google Data Analytics Professional Certificate
Course Highlights
- Describe the R programming language and its programming environment.
- Explain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectors.
- Describe the options for generating visualizations in R.
- Demonstrate an understanding of the basic formatting in R Markdown to create structure and emphasize content.
Skills you will learn!
Curriculum
22 Topics
Introduction to the exciting world of programming
Fun with R
Carrie: Getting started with R
Programming languages
Introduction to R
Intro to RStudio
Course syllabus
The R-versus-Python debate
Learning Log: Get ready to explore R
Ways to learn about programming
From spreadsheets to SQL to R
When to use RStudio
Connecting with other analysts in the R community
Glossary: Terms and definitions
Module 1 challenge
Optional Hands-On Activity: Downloading and installing R
Optional Hands-On Activity: R Console
Test your knowledge on programming languages
Hands-On Activity: Cloud access to RStudio
Optional Hands-On Activity: Get started in RStudio Desktop
Test your knowledge on programming with RStudio
Refresher: Your data analytics certificate roadmap
24 Topics
Programming using RStudio
Programming fundamentals
Operators and calculations
The gift that keeps on giving
Welcome to the tidyverse
More on the tidyverse
Use pipes to nest code
Connor: Coding tips
Vectors and lists in R
Dates and times in R
Other common data structures
Logical operators and conditional statements
Guide: Keeping your code readable
Available R packages
R resources for more help
Glossary: Terms and definitions
Module 2 challenge
Test your knowledge on programming concepts
Hands-On Activity: R sandbox
Test your knowledge on coding in R
Hands-On Activity: Installing and loading tidyverse
Test your knowledge on R packages
Test your knowledge on the tidyverse
Basic Concepts of R
25 Topics
Data in R
R data frames
Working with data frames
Cleaning up with the basics
Organize your data
Transforming data
Same data different outcome
The bias function
More about tibbles
Data-import basics
File-naming conventions
More on R operators
Optional: Manually create a data frame
Wide to long with tidyr
Working with biased data
Glossary: Terms and definitions
Module 3 challenge
Hands-On Activity: Importing and working with data
Test your knowledge on R data frames
Hands-On Activity: Cleaning data in R
Test your knowledge on cleaning data
Hands-On Activity: Changing your data
Test your knowledge on R functions
Hands-on Activity: Create your own data frame
Clean organize and transform data with R
26 Topics
Visualizations in R
Visualization basics in R and tidyverse
Getting started with ggplot()
Joseph: Career path to people analytics
Enhancing visualizations in R
Doing more with ggplot
Aesthetics and facets
Annotation layer
Saving your visualizations
Common problems when visualizing in R
Aesthetic attributes
Smoothing
Filtering and plots
Adding annotations in R
Saving images without ggsave()
Glossary: Terms and definitions
Module 4 challenge
Hands-On Activity: Visualizing data with ggplot2
Hands-On Activity: Using ggplot
Test your knowledge on data visualizations in R
Hands-On Activity: Aesthetics and visualizations
Hands-On Activity: Filters and plots
Test your knowledge on aesthetics in analysis
Hands-On Activity: Annotating and saving visualizations
Test your knowledge on annotating and saving visualizations
Elements of ggplot
21 Topics
Documentation and reports
Overview of R Markdown
Using R Markdown in RStudio
Structure of markdown documents
Meg: Programming is empowering
Even more document elements
Code chunks
Exporting documentation
R Markdown resources
Optional: Jupyter notebooks
Output formats in R Markdown
Glossary: Terms and definitions
Reflect and connect with peers
Coming up next...
Module 5 challenge
Hands-On Activity: Your R Markdown notebook
Test your knowledge about documentation and reports
Test your knowledge about creating R Markdown documents
Hands-On Activity: Adding code chunks to R Markdown notebooks
Hands-On Activity: Exporting your R Markdown notebook
Test your knowledge on code chunks
Data Analysis with R Programming
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