Part IV: Advanced Topics
42
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
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
17
Other Helpful Programming Resources
Part III: Data Wrangling
18
Data Input
19
Data Visualization Basics
20
Exploratory Data Analysis
21
Data Visualization
22
Creating Good Charts
23
Data Cleaning
24
Working with Strings
25
Reshaping Data
26
Normal Forms of Data
27
Joining Data
28
Dates and Times
29
Functional Programming
30
Spatial data
Part IV: Advanced Topics
31
Simulation and Reproducibility
32
Data Documentation: Codebooks, Reproducibility, and more
33
Web Scraping
34
Record-based Data and List Processing Strategies
35
Application Programming Interfaces
36
Working with PDFs
37
Interactive Graphics
38
Interactive Graphics
39
“Big” Data
40
Databases
41
Efficient Data Access: Arrow and Parquet Files
42
Memory and Computational Efficiency
43
Other Topics
Part IV: Advanced Topics
42
Memory and Computational Efficiency
42
Memory and Computational Efficiency
Published
June 18, 2025
41
Efficient Data Access: Arrow and Parquet Files
43
Other Topics