R Dplyr
Programming for Data Science Bootcamp
Welcome
Getting Started
Setting Up Your Programming Environment
Hello, World!
Python Introduction
Data and Code
Python Object Types
Data Types, Operators, and Expressions
Numbers
Booleans
Strings
Data Structures
Aside On Immutables
Exercises
Python Control Structures
Values, Variables, Expressions, and Statements
Control Structures
Iterables and Iterators
Comprehensions
Nested Comprehensions
Exercises
Python Functions
Introduction to Functions
Importing Functions
Lambda Functions
Recursion
Scope
Function Groups
Exercises
Python NumPy
Python Timing
Basic File I/O
Introducing NumPy
NumPy Indexing
NumPy Operations
NumPy Functions
Exercises
Python Pandas
Introducting Pandas
Exploring Pandas
Deeper Into Pandas
Narrow vs Wide Tables
Method Chaining
Pandas and SQL
Exercises
R Introduction
Introducing R
R Data Types and Operators
R Data Structures
R Data Frames
R Control Structures
R Functions
Exercises
R Dplyr
The Tidyverse and Tibbles
Dplyr
Exercises
R Visualization
GGPlot and the Grammar of Graphics
Getting Started with GGPlot2
R Markdown
Plotly and GGPlotly
GGPlot in Python with Plotnine
Exercises
Table of contents
Topics
Suggested Readings
Videos
The Tidyverse and Tibbles
Dplyr
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R Dplyr
Topics
The Tidyverse
Tibbles, a lightweight version of data frames
Data transformations using dplyr verbs
Suggested Readings
RFDS Explore 5 Data Transformation
RDFS Wrangle 10 Tibbles
RDFS Wrangle 11 Data Import
RDFS Wrangle 12 Tidy data
Peng, 2022, “Managing Data Frames with the dplyr package”
Videos
The Tidyverse and Tibbles
Dplyr
Exercises
The Tidyverse and Tibbles