Data Analysis with R Programming
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What Will You Learn?
- 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.
Course Content
Module 1: Programming and data analytics
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Introduction to the exciting world of programming
00:00 -
Fun with R
00:00 -
Carrie: Getting started with R
00:00 -
Programming languages
00:00 -
Introduction to R
00:00 -
Intro to RStudio
00:00
8 readings
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Course syllabus
00:00 -
The R-versus-Python debate
00:00 -
Learning Log: Get ready to explore R
00:00 -
Ways to learn about programming
00:00 -
From spreadsheets to SQL to R
00:00 -
When to use RStudio
00:00 -
Connecting with other analysts in the R community
00:00 -
Glossary: Terms and definitions
00:00
7 quizzes
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Module 1 challenge
00:00 -
Optional Hands-On Activity: Downloading and installing R
00:00 -
Optional Hands-On Activity: R Console
00:00 -
Test your knowledge on programming languages
00:00 -
Hands-On Activity: Cloud access to RStudio
00:00 -
Optional Hands-On Activity: Get started in RStudio Desktop
00:00 -
Test your knowledge on programming with RStudio
00:00
2 discussion prompts
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Meet and greet
00:00 -
R&R…Studio!
00:00
1 plugin
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Refresher: Your data analytics certificate roadmap
00:00
Module 2: Programming using RStudio
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Programming using RStudio
00:00 -
Programming fundamentals
00:00 -
Operators and calculations
00:00 -
The gift that keeps on giving
00:00 -
Welcome to the tidyverse
00:00 -
More on the tidyverse
00:00 -
Working with pipes
00:00 -
Connor: Coding tips
00:00
8 readings
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Vectors and lists in R
00:00 -
Dates and times in R
00:00 -
Other common data structures
00:00 -
Logical operators and conditional statements
00:00 -
Guide: Keeping your code readable
00:00 -
Available R packages
00:00 -
R resources for more help
00:00 -
Glossary: Terms and definitions
00:00
7 quizzes
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Module 2 challenge
00:00 -
Test your knowledge on programming concepts
00:00 -
Hands-On Activity: R sandbox
00:00 -
Test your knowledge on coding in R
00:00 -
Hands-On Activity: Installing and loading tidyverse
00:00 -
Test your knowledge on R packages
00:00 -
Test your knowledge on the tidyverse
00:00
1 discussion prompt
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Queries and programming
00:00
1 plugin
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Basic Concepts of R
00:00
Module 3: Working with data in R
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Data in R
00:00 -
R data frames
00:00 -
Working with data frames
00:00 -
Cleaning up with the basics
00:00 -
Organize your data
00:00 -
Transforming data
00:00 -
Same data, different outcome
00:00 -
The bias function
00:00
8 readings
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More about tibbles
00:00 -
Data-import basics
00:00 -
File-naming conventions
00:00 -
More on R operators
00:00 -
Optional: Manually create a data frame
00:00 -
Wide to long with tidyr
00:00 -
Working with biased data
00:00 -
Glossary: Terms and definitions
00:00
8 quizzes
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Module 3 challenge
00:00 -
Hands-On Activity: Create your own data frame
00:00 -
Hands-On Activity: Importing and working with data
00:00 -
Test your knowledge on R data frames
00:00 -
Hands-On Activity: Cleaning data in R
00:00 -
Test your knowledge on cleaning data
00:00 -
Hands-On Activity: Changing your data
00:00 -
Test your knowledge on R functions
00:00
2 discussion prompts
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Data in R versus SQL
00:00 -
Compare data cleaning on different platforms
00:00
1 plugin
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Clean, organize, and transform data with R
00:00
Module 4: More about Visualizations, aesthetics, and annotations
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Visualizations in R
00:00 -
Visualization basics in R and tidyverse
00:00 -
Getting started with ggplot()
00:00 -
Joseph: Career path to people analytics
00:00 -
Enhancing visualizations in R
00:00 -
Doing more with ggplot
00:00 -
Aesthetics and facets
00:00 -
Annotation layer
00:00 -
Saving your visualizations
00:00
7 readings
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Common problems when visualizing in R
00:00 -
Aesthetic attributes
00:00 -
Smoothing
00:00 -
Filtering and plots
00:00 -
Adding annotations in R
00:00 -
Saving images without ggsave()
00:00 -
Glossary: Terms and definitions
00:00
9 quizzes
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Module 4 challenge
00:00 -
Hands-On Activity: Visualizing data with ggplot2
00:00 -
Hands-On Activity: Using ggplot
00:00 -
Test your knowledge on data visualizations in R
00:00 -
Hands-On Activity: Aesthetics and visualizations
00:00 -
Hands-On Activity: Filters and plots
00:00 -
Test your knowledge on aesthetics in analysis
00:00 -
Hands-On Activity: Annotating and saving visualizations
00:00 -
Test your knowledge on annotating and saving visualizations
00:00
1 discussion prompt
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Visualizations in Tableau versus R
00:00
1 plugin
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Elements of ggplot
00:00
Module 5: Documentation and reports
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Documentation and reports
00:00 -
Overview of R Markdown
00:00 -
Using R Markdown in RStudio
00:00 -
Structure of markdown documents
00:00 -
Meg: Programming is empowering
00:00 -
Even more document elements
00:00 -
Code chunks
00:00 -
Exporting documentation
00:00
5 readings
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R Markdown resources
00:00 -
Optional: Jupyter notebooks
00:00 -
Output formats in R Markdown
00:00 -
Glossary: Terms and definitions
00:00 -
Coming up next…
00:00
9 quizzes
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Module 5 challenge
00:00 -
Course challenge
00:00 -
Hands-On Activity: Your R Markdown notebook
00:00 -
Test your knowledge about documentation and reports
00:00 -
Test your knowledge about creating R Markdown documents
00:00 -
Hands-On Activity: Adding code chunks to R Markdown notebooks
00:00 -
Hands-On Activity: Exporting your R Markdown notebook
00:00 -
Hands-On Activity: Using R Markdown templates
00:00 -
Test your knowledge on code chunks
00:00
1 discussion prompt
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Using R Markdown notebooks
00:00
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