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Computer Science
Data Visualization
Data Visualization in R
1. Introduction to Data Visualization in R
2. Foundations of R for Data Visualization
3. Base R Graphics
4. The Grammar of Graphics with ggplot2
5. Common Plot Types with ggplot2
6. Advanced ggplot2 Techniques
7. Interactive and Dynamic Visualizations
8. Specialized Visualizations
9. Design Principles and Best Practices
10. Output and Sharing
Foundations of R for Data Visualization
R Data Structures
Vectors
Numeric Vectors
Character Vectors
Logical Vectors
Indexing and Subsetting
Vector Operations
Factors
Creating Factors
Factor Levels
Ordered Factors
Factor Manipulation
Lists
Creating Lists
Accessing List Elements
Nested Lists
List Operations
Data Frames
Creating Data Frames
Accessing Rows and Columns
Data Frame Operations
Combining Data Frames
Tibbles
Differences from Data Frames
Creating Tibbles
Printing Behavior
Subsetting Behavior
Data Manipulation with dplyr
The Pipe Operator
Magrittr Pipe (%>%)
Native Pipe (|>)
Chaining Operations
Pipe Best Practices
Core dplyr Verbs
select() for Column Selection
Selecting by Name
Selecting by Position
Helper Functions
Renaming Columns
filter() for Row Filtering
Logical Conditions
Combining Conditions
Missing Value Handling
mutate() for Variable Creation
Creating New Variables
Modifying Existing Variables
Conditional Variables
Window Functions
summarise() for Data Summarization
Aggregation Functions
Multiple Summaries
Handling Missing Values
arrange() for Sorting
Single Column Sorting
Multiple Column Sorting
Ascending and Descending Order
Grouping Operations
group_by() Function
Grouped Operations
Multiple Grouping Variables
Ungrouping Data
Joining Data
Inner Joins
Left Joins
Right Joins
Full Joins
Semi Joins
Anti Joins
Data Tidying with tidyr
Tidy Data Principles
Variables in Columns
Observations in Rows
Values in Cells
Benefits for Visualization
Reshaping Data
pivot_longer() for Wide to Long
Basic Syntax
Multiple Variables
Handling Names and Values
pivot_wider() for Long to Wide
Basic Syntax
Handling Missing Values
Multiple Value Columns
Handling Missing Values
drop_na() Function
fill() Function
replace_na() Function
Separating and Uniting Columns
separate() Function
unite() Function
Data Import and Export
Reading Delimited Files
read.csv() from Base R
read_csv() from readr
Handling Different Delimiters
Character Encoding
Missing Value Specifications
Reading Excel Files
readxl Package
Reading Specific Sheets
Reading Cell Ranges
Handling Multiple Sheets
Reading from Statistical Software
SPSS Files with haven
Stata Files with haven
SAS Files with haven
Reading from Databases
DBI Package
Database Connections
SQL Queries in R
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1. Introduction to Data Visualization in R
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3. Base R Graphics