Part IV: Advanced Topics
40
Memory and Computational Efficiency
Statistical Computing using R and Python
Preface
Part I: Tools
1
Computer Basics
2
Setting Up Your Computer
3
RStudio’s Interface
4
Scripts and Notebooks and Terminals
5
Version Control with Git
6
Reproducibility and Professional Communication
Part II: General Programming
7
Introduction to Programming
8
Variables and Basic Data Types
9
Mathematical and Logical Operators
10
Functions, Packages, and Environments
11
Data Structures
12
Matrix Calculations
13
Control Structures
14
Writing Functions
15
Debugging
16
Programming With Data
Part III: Data Wrangling
17
Data Input
18
Data Visualization Basics
19
Exploratory Data Analysis
20
A Grammar of Graphics
21
Creating Good Charts
22
Data Cleaning
23
Working with Strings
24
Reshaping Data
25
Normal Forms of Data
26
Joining Data
27
Dates and Times
28
Functional Programming
Part IV: Advanced Topics
29
Simulation
30
Data Documentation
31
Web Scraping
32
Record-based Data and List Processing Strategies
33
Application Programming Interfaces
34
Working with PDFs
35
Animated and Interactive Graphics
36
JavaScript Graphics
37
“Big” Data
38
Databases
39
Efficient Data Access: Arrow and Parquet Files
40
Memory and Computational Efficiency
41
Other Topics
Part IV: Advanced Topics
40
Memory and Computational Efficiency
40
Memory and Computational Efficiency
Published
September 10, 2026
39
Efficient Data Access: Arrow and Parquet Files
41
Other Topics