The core aim of the book is to provide a self-contained introduction to R (both Base R and the tidyverse) and show how this knowledge can be applied to a range of OR challenges in the domains of public health, infectious diseases, and energy generation, and so provide a platform to develop actionable insights to support decision making.
Exploring Operations Research with R shows how the R Programming language can be a valuable tool – and way of thinking – that can be successfully applied to the field of operations research (OR). This approach is centred on the idea of the future OR professional as someone who can combine knowledge of key OR techniques (e.g. simulation, linear programming, data science, and network science) with an understanding of R, including tools for data representation, manipulation, and analysis. The core aim of the book is to provide a self-contained introduction to R (both Base R and the tidyverse) and show how this knowledge can be applied to a range of OR challenges in the domains of public health, infectious diseases, and energy generation, and so provide a platform to develop actionable insights to support decision making.
Features
- Can serve as a primary textbook for a comprehensive course in R, with applications in Operations Research
- Suitable for post-graduate students in operations research and data science, with a focus on the computational perspective of operations research. The book will also be of interest to professional OR practitioners as part of their continuing professional development
- Linked to a Github repository including code, solutions, data sets and other ancillary material.
1. Getting Started with R. 1.1. Introduction. 1.2. Exploring R via the
RStudio console. 1.3. Calling functions. 1.4. Installing packages. 1.6. Next
Steps.
2. Vectors. 2.1. Introduction. 2.2. Atomic Vectors. 2.3.
Vectorisation. 2.4. Lists. 2.5. Mini-Case: Two-dice rolls with Atomic
Vectors. 2.6. Summary of R Functions from
Chapter
2. 2.7. Exercises.
3.
Subsetting Vectors. 3.1. Introduction. 3.2. Subsetting Atomic Vectors. 3.3.
Subsetting Lists. 3.4. Iteration using Loops, and the if statement. 3.5.
Mini-Case: Starwars Movies. 3.6. Summary of R Functions from
Chapter
3. 3.7.
Exercises.
4. Functions, Functionals and the R Pipe. 4.1. Introduction. 4.2.
Functions. 4.3. Passing arguments to functions. 4.4. Error checking for
functions. 4.5. Environments and Functions. 4.6. Functionals with lapply.
4.7. Mini-Case: Starwars Movies (Revisited) using Functionals. 4.8. Creating
a data processing pipeline using Rs native pipe operator. 4.9. Summary of R
Functions from
Chapter
4. 4.10. Exercises.
5. Matrices and Data Frames. 5.1.
Introduction. 5.2. Matrices. 5.3. Data Frames. 5.4. R functions for
processing data frames: subset() and transform(). 5.5. Tibbles. 5.6.
Functionals on matrices and data frames. 5.8. Mini-Case 2: A Pipeline for
Processing data frames. 5.9. Summary of R Functions from
Chapter
5. 5.10.
Exercises.
6. The S3 Object System in R. 6.1. Introduction. 6.2. S3 in
Action. 6.3. Objects, Attributes and Defining S3 Classes. 6.4. The Generic
Function Approach. 6.5. Using an Existing Generic Function. 6.6. Custom-Built
Generic Functions. 6.7. Inheritance with S3. 6.8. Mini-Case: Creating a Queue
S3 Object. 6.9. Summary of R Functions from
Chapter
6. 6.10. Exercises. I.
Base R.
7. Visualisation with ggplot2. 7.1. Introduction. 7.2. Two datasets
from ggplot2 - mpg and diamonds. 7.3. Exploring relationships with a
scatterplot. 7.4. Aesthetic mappings. 7.5. Subplots with Facets. 7.6.
Statistical Transformations. 7.7. Themes. 7.8. Adding lines to a plot. 7.9.
Mini-Case: Visualising the Impact of Storm Ophelia. 7.10. Summary of R
Functions from
Chapter
7. 7.11. Exercises.
8. Data Transformation with dplyr.
8.1. Introduction. 8.2. The tidyverse Pipe magrittr. 8.3. Filtering rows
with filter(). 8.4. Sorting rows with arrange(). 8.5. Choosing columns with
select(). 8.6. Adding columns with mutate(). 8.7. Summarising observations
with summarise(). 8.8. Additional dplyr functions. 8.9. Mini-Case:
Summarising total rainfall in
2017. 8.10. Summary of R Functions from
Chapter
8. 8.11. Exercises.
9. Relational data with dplyr and tidying data with
tidyr. 9.1. Introduction. 9.2. Relational data. 9.3. Mutating joins. 9.4.
Filtering joins. 9.5. Tidy Data. 9.6. Making data longer using pivot_longer.
9.7. Making data wider using pivot_wider(). 9.8. Mini-Case: exploring
correlations relating to wind energy generation. 9.9. Summary of R Functions
from
Chapter
9. 9.10. Exercises.
10. Processing data with purr. 10.1.
Introduction. 10.2. Iteration using map(). 10.3. Additional map_* functions.
10.4. Iterating over two inputs using map2() and pmap(). 10.5. Integrating
purrr with dplyr and tidyr to process tibbles. 10.6. Additional purrr
functions. 10.7. Mini-Case: generating linear models from the mpg dataset.
10.8. Summary of R Functions from
Chapter
10. 10.9. Exercises.
11. Shiny.
11.1. Introduction. 11.2. Reactive Programming. 11.3. Example one: hello
shiny. 11.4. Example two: squaring an input number. 11.5. Example three:
exploring a weather station. 11.6 Example four: comparing two weather
stations. 11.7. Example five: creating a scatter plot. 11.8 Example six:
improving design by adding reactive expressions. 11.9. Summary of R Functions
from
Chapter
11. 11.10. Exercises. II. The tidyverse and Shiny.
12.
Exploratory Data Analysis. 12.1. Introduction. 12.2. Exploratory Data
Analysis. 12.3 Identifying species of iris using plant measurements. 12.4.
Exploring electricity demand in Victoria, Australia. 12.5. Exploring housing
values in the Boston suburbs. 12.6. Exploring passenger survival chances on
board the Titanic. 12.7. Exploring the effect of wind direction on winter
temperatures in Ireland. 12.8. Summary of R Functions from
Chapter
12. 13.
Linear Programming. 13.1. Introduction. 13.2. Linear Programming - An
overview. 13.3. The Reddy Mikks example. 13.4. Exploring a two-variable
decision space using R. 13.5. A Graphical Solution to the Reddy Mixx Problem.
13.6. lpSolve: Generating Optimal Solutions in R. 13.7. Sensitivity Analysis
using lpSolve. 13.8. Summary of R Functions from
Chapter
13. 14. Agent Based
Simulation. 14.1. Introduction. 14.2. Networks and the igraph package. 14.3
Agent Design - The Adopter Marketing Problem. 14.4. Simulator Design and Data
Structures. 14.5. Simulation Code. 14.6. Summary of R Functions from
Chapter
14.
15. System Dynamics. 15.1. Introduction. 15.2. Stocks, Flows and
Feedback. 15.3. deSolve. 15.4. The Susceptible-Infected-Removed Model. 15.5.
The Susceptible-Infected-Recovered-Hospital Model. 15.6. Policy Exploration
of the SIRH Model using Sensitivity Analysis. 15.7. Summary of R Functions
from
Chapter.
Jim Duggan is a Personal Professor in Computer Science at the University of Galway, Ireland. He lectures on R, MATLAB®, and system dynamics, and he is a certified RStudio tidyverse instructor. His research interests are interdisciplinary and focus on the use of simulation and computational methods to support public health policy. You can learn more about his work on R and computation modelling on his GitHub site https://github.com/JimDuggan.
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