University of Colorado Boulder - Introduction to R Programming and Tidyverse
- Offered byCoursera
- Public/Government Institute
Introduction to R Programming and Tidyverse at Coursera Overview
Duration | 23 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Introduction to R Programming and Tidyverse at Coursera Highlights
- Earn a Certificate upon completion
Introduction to R Programming and Tidyverse at Coursera Course details
- You will learn to do data visualization and analysis in a reproducible manner and use functions that allow your code to be easily read and understood
- You will use RMarkdown to create nice documents and reports that execute your code freshly every time it's run and that capture your thoughts about the data along the way
- This course has been designed for learners from non-STEM backgrounds to help prepare them for more advanced data science courses by providing an introduction to programming and to the R language
Introduction to R Programming and Tidyverse at Coursera Curriculum
Introduction to R, RStudio and RMarkdown
Course Introduction
Configuring RStudio
Installing R Packages
RMarkdown Overview
Creating an RMarkdown Document
Welcome and Course Organization
R Resources
Installing R and RStudio
Fundamentals of R Summary
Reproducible Research - Why and How
Helpful Tips and Resources on RMarkdown
R Fundamentals
RMarkdown
Functions
Our First Function
Naming a Function
Function Inputs
A Change in Function Requirements
Conditional Execution
Multiple Conditions
For Loops
Checking Inputs
Function Output
Introduction to Pipes
Resources
Writing a Function
Statement Conditions
The Switch Statement
Using the Stop Function
Return Specific Values
Using Pipes
Functions
Input Checking and Outputs
Data Visualization using ggplot2
Introduction to ggplot2
Aesthetics
Geometric Objects
Statistical Transformations
Position Adjustments
Facets
Coordinate Systems
Resources
Using ggplot2
Layers
Data Analysis with dplyr
Introduction to dplyr
Chaining Functions
Selecting Variables (Columns)
Conditionally Selecting Rows
Selecting Rows by Location
Arrange Rows by Value
Renaming Data
Distinct, Mutate, and Transmute
Rename, Relocate, and Summarize
Summary Functions
Counting Observations
Grouping Variables: Part 1
Grouping Variables: Part 2
Grouping Variables: Part 3
Main dplyr Functions
R and NA
Grouping datasets
dplyr Verbs
Select, Filter, and Arrange
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