R Resources for Animal Breeding

This appendix provides a guide to R packages, tools, and learning resources specifically relevant to animal breeding and quantitative genetics.

Essential R Packages

Data Manipulation and Visualization

tidyverse

Purpose: Collection of packages for data science (includes dplyr, ggplot2, tidyr, readr)

Install:

install.packages("tidyverse")

Key functions:

read_csv(): Read data files
filter(), select(), mutate(): Data manipulation
ggplot(): Creating visualizations
group_by(), summarize(): Group operations

Learn more: tidyverse.org

Pedigree and Relationship Matrices

pedigree

Purpose: Pedigree manipulation, inbreeding calculation

Install:

install.packages("pedigree")

Key functions:

pedigree(): Create pedigree object
calcInbreeding(): Calculate inbreeding coefficients
calcA(): Calculate numerator relationship matrix (A)

nadiv

Purpose: (Non)Additive genetic relatedness matrices

Install:

install.packages("nadiv")

Key functions:

makeA(): Additive relationship matrix
makeD(): Dominance relationship matrix
makeAA(): Epistatic relationship matrix

Learn more: CRAN documentation

AGHmatrix

Purpose: Relationship matrices for diploid and autopolyploid species

Install:

install.packages("AGHmatrix")

Key functions:

Gmatrix(): Genomic relationship matrix (G)
Amatrix(): Pedigree relationship matrix (A)
Hmatrix(): Combined H matrix (pedigree + genomic)

Learn more: CRAN AGHmatrix

Mixed Models and Genetic Evaluation

lme4

Purpose: Linear mixed-effects models

Install:

install.packages("lme4")

Key functions:

lmer(): Fit linear mixed model
VarCorr(): Extract variance components
Random effects for animal breeding models

Learn more: lme4 documentation

lmerTest

Purpose: Tests and p-values for lme4 models

Install:

install.packages("lmerTest")

Provides: Significance tests for fixed and random effects

MCMCglmm

Purpose: Bayesian mixed models using MCMC

Install:

install.packages("MCMCglmm")

Key functions:

MCMCglmm(): Fit Bayesian mixed model
Variance component estimation
Pedigree-based animal models

Learn more: Course notes by Jarrod Hadfield

Genomic Prediction

BGLR

Purpose: Bayesian genomic linear regression

Install:

install.packages("BGLR")

Key functions:

BGLR(): Fit genomic prediction models
Methods: BayesA, BayesB, BayesC, Bayesian Lasso, RKHS

Learn more: BGLR GitHub

rrBLUP

Purpose: Ridge regression and genomic prediction

Install:

install.packages("rrBLUP")

Key functions:

kin.blup(): Genomic BLUP (GBLUP)
mixed.solve(): Solve mixed model equations
A.mat(): Genomic relationship matrix

Learn more: rrBLUP documentation

brms

Purpose: Bayesian regression models using Stan

Install:

install.packages("brms")

Key features:

Flexible Bayesian modeling
Can be used for genomic prediction
User-friendly interface to Stan

Learn more: brms website

Simulation

AlphaSimR

Purpose: Stochastic simulation of breeding programs

Install:

install.packages("AlphaSimR")

Key features:

Simulate populations, selection, mating
Genomic and conventional selection
Multi-generation breeding programs

Learn more: AlphaSimR documentation

Database Interfaces

DBI and RSQLite

Purpose: Interface to SQL databases

Install:

install.packages(c("DBI", "RSQLite"))

Key functions:

dbConnect(): Connect to database
dbGetQuery(): Execute SQL query
dbDisconnect(): Close connection

Used in: Chapter 14 (SQL demonstrations)

Additional Utilities

knitr

Purpose: Dynamic report generation

Install:

install.packages("knitr")

Used for: R Markdown and Quarto documents

kableExtra

Purpose: Enhanced tables for R Markdown

Install:

install.packages("kableExtra")

Installing All Packages

To install all packages used in this book at once:

# Core packages
install.packages(c(
  # Data manipulation and visualization
  "tidyverse",

  # Pedigree and relationships
  "pedigree", "nadiv", "AGHmatrix",

  # Mixed models
  "lme4", "lmerTest", "MCMCglmm",

  # Genomic prediction
  "BGLR", "rrBLUP", "brms",

  # Simulation
  "AlphaSimR",

  # Database
  "DBI", "RSQLite",

  # Utilities
  "knitr", "kableExtra", "MASS"
))

Learning R

Online Resources

R for Data Science (Free Online Book)

URL: r4ds.had.co.nz

Topics: tidyverse, data visualization, data transformation, R Markdown

Highly recommended for beginners and intermediate users

RStudio Education

URL: education.rstudio.com

Resources: Tutorials, cheatsheets, webinars

Swirl

Purpose: Learn R interactively within R

Install:

install.packages("swirl")
library(swirl)
swirl()

Animal Breeding-Specific Resources

Introduction to Quantitative Genetics (Book)

Authors: Falconer and Mackay

Focus: Theoretical foundations (some R code in newer editions)

Genetics and Analysis of Quantitative Traits (Book)

Authors: Lynch and Walsh

Focus: Advanced quantitative genetics

Linear Models for the Prediction of Animal Breeding Values (Book)

Author: Raphael Mrode

Focus: BLUP, mixed models, genetic evaluation

Includes: Numerical examples, some SAS code (can be translated to R)

Getting Help

Within R

# Get help on a function
?lmer
help(lmer)

# Search for topics
??genomic

# Package vignettes
vignette("lme4")
browseVignettes("BGLR")

Online Communities

Stack Overflow: stackoverflow.com (tag your questions with [r])
RStudio Community: community.rstudio.com
r/rstats (Reddit): Friendly community for R questions

Reproducibility and Best Practices

R Projects

Use RStudio Projects (.Rproj files) to organize your work:

Keeps all files in one directory
Sets working directory automatically
Easier to share and reproduce

R Markdown and Quarto

Create reproducible reports combining code, results, and narrative:

R Markdown: rmarkdown.rstudio.com
Quarto: quarto.org (used for this book)

Version Control (Git/GitHub)

Track changes to your code and data:

Git: Version control system
GitHub: Online platform for sharing code
Resource: happygitwithr.com

Summary

This appendix provides a starting point for R resources in animal breeding. As you progress through the book, refer back to this appendix for package documentation and learning resources.

Key packages to master:

tidyverse (data manipulation)
lme4 (mixed models)
AGHmatrix (relationship matrices)
rrBLUP or BGLR (genomic prediction)
AlphaSimR (simulation)

Happy coding!

# R Resources for Animal Breeding {#sec-r-resources .unnumbered} This appendix provides a guide to R packages, tools, and learning resources specifically relevant to animal breeding and quantitative genetics. ## Essential R Packages ### Data Manipulation and Visualization #### tidyverse **Purpose**: Collection of packages for data science (includes dplyr, ggplot2, tidyr, readr) **Install**: ```r install.packages("tidyverse") ``` **Key functions**: - `read_csv()`: Read data files - `filter()`, `select()`, `mutate()`: Data manipulation - `ggplot()`: Creating visualizations - `group_by()`, `summarize()`: Group operations **Learn more**: [tidyverse.org](https://www.tidyverse.org) --- ### Pedigree and Relationship Matrices #### pedigree **Purpose**: Pedigree manipulation, inbreeding calculation **Install**: ```r install.packages("pedigree") ``` **Key functions**: - `pedigree()`: Create pedigree object - `calcInbreeding()`: Calculate inbreeding coefficients - `calcA()`: Calculate numerator relationship matrix (A) --- #### nadiv **Purpose**: (Non)Additive genetic relatedness matrices **Install**: ```r install.packages("nadiv") ``` **Key functions**: - `makeA()`: Additive relationship matrix - `makeD()`: Dominance relationship matrix - `makeAA()`: Epistatic relationship matrix **Learn more**: CRAN documentation --- #### AGHmatrix **Purpose**: Relationship matrices for diploid and autopolyploid species **Install**: ```r install.packages("AGHmatrix") ``` **Key functions**: - `Gmatrix()`: Genomic relationship matrix (G) - `Amatrix()`: Pedigree relationship matrix (A) - `Hmatrix()`: Combined H matrix (pedigree + genomic) **Learn more**: [CRAN AGHmatrix](https://cran.r-project.org/package=AGHmatrix) --- ### Mixed Models and Genetic Evaluation #### lme4 **Purpose**: Linear mixed-effects models **Install**: ```r install.packages("lme4") ``` **Key functions**: - `lmer()`: Fit linear mixed model - `VarCorr()`: Extract variance components - Random effects for animal breeding models **Learn more**: [lme4 documentation](https://cran.r-project.org/package=lme4) --- #### lmerTest **Purpose**: Tests and p-values for lme4 models **Install**: ```r install.packages("lmerTest") ``` **Provides**: Significance tests for fixed and random effects --- #### MCMCglmm **Purpose**: Bayesian mixed models using MCMC **Install**: ```r install.packages("MCMCglmm") ``` **Key functions**: - `MCMCglmm()`: Fit Bayesian mixed model - Variance component estimation - Pedigree-based animal models **Learn more**: Course notes by Jarrod Hadfield --- ### Genomic Prediction #### BGLR **Purpose**: Bayesian genomic linear regression **Install**: ```r install.packages("BGLR") ``` **Key functions**: - `BGLR()`: Fit genomic prediction models - Methods: BayesA, BayesB, BayesC, Bayesian Lasso, RKHS **Learn more**: [BGLR GitHub](https://github.com/gdlc/BGLR-R) --- #### rrBLUP **Purpose**: Ridge regression and genomic prediction **Install**: ```r install.packages("rrBLUP") ``` **Key functions**: - `kin.blup()`: Genomic BLUP (GBLUP) - `mixed.solve()`: Solve mixed model equations - `A.mat()`: Genomic relationship matrix **Learn more**: [rrBLUP documentation](https://cran.r-project.org/package=rrBLUP) --- #### brms **Purpose**: Bayesian regression models using Stan **Install**: ```r install.packages("brms") ``` **Key features**: - Flexible Bayesian modeling - Can be used for genomic prediction - User-friendly interface to Stan **Learn more**: [brms website](https://paul-buerkner.github.io/brms/) --- ### Simulation #### AlphaSimR **Purpose**: Stochastic simulation of breeding programs **Install**: ```r install.packages("AlphaSimR") ``` **Key features**: - Simulate populations, selection, mating - Genomic and conventional selection - Multi-generation breeding programs **Learn more**: [AlphaSimR documentation](https://CRAN.R-project.org/package=AlphaSimR) --- ### Database Interfaces #### DBI and RSQLite **Purpose**: Interface to SQL databases **Install**: ```r install.packages(c("DBI", "RSQLite")) ``` **Key functions**: - `dbConnect()`: Connect to database - `dbGetQuery()`: Execute SQL query - `dbDisconnect()`: Close connection **Used in**: Chapter 14 (SQL demonstrations) --- ### Additional Utilities #### knitr **Purpose**: Dynamic report generation **Install**: ```r install.packages("knitr") ``` **Used for**: R Markdown and Quarto documents --- #### kableExtra **Purpose**: Enhanced tables for R Markdown **Install**: ```r install.packages("kableExtra") ``` --- ## Installing All Packages To install all packages used in this book at once: ```r # Core packages install.packages(c( # Data manipulation and visualization "tidyverse", # Pedigree and relationships "pedigree", "nadiv", "AGHmatrix", # Mixed models "lme4", "lmerTest", "MCMCglmm", # Genomic prediction "BGLR", "rrBLUP", "brms", # Simulation "AlphaSimR", # Database "DBI", "RSQLite", # Utilities "knitr", "kableExtra", "MASS" )) ``` --- ## Learning R ### Online Resources #### R for Data Science (Free Online Book) **URL**: [r4ds.had.co.nz](https://r4ds.had.co.nz) **Topics**: tidyverse, data visualization, data transformation, R Markdown **Highly recommended** for beginners and intermediate users --- #### RStudio Education **URL**: [education.rstudio.com](https://education.rstudio.com) **Resources**: Tutorials, cheatsheets, webinars --- #### Swirl **Purpose**: Learn R interactively within R **Install**: ```r install.packages("swirl") library(swirl) swirl() ``` --- ### Animal Breeding-Specific Resources #### Introduction to Quantitative Genetics (Book) **Authors**: Falconer and Mackay **Focus**: Theoretical foundations (some R code in newer editions) --- #### Genetics and Analysis of Quantitative Traits (Book) **Authors**: Lynch and Walsh **Focus**: Advanced quantitative genetics --- #### Linear Models for the Prediction of Animal Breeding Values (Book) **Author**: Raphael Mrode **Focus**: BLUP, mixed models, genetic evaluation **Includes**: Numerical examples, some SAS code (can be translated to R) --- ## Getting Help ### Within R ```r # Get help on a function ?lmer help(lmer) # Search for topics ??genomic # Package vignettes vignette("lme4") browseVignettes("BGLR") ``` ### Online Communities - **Stack Overflow**: [stackoverflow.com](https://stackoverflow.com) (tag your questions with `[r]`) - **RStudio Community**: [community.rstudio.com](https://community.rstudio.com) - **r/rstats** (Reddit): Friendly community for R questions --- ## Reproducibility and Best Practices ### R Projects Use RStudio Projects (`.Rproj` files) to organize your work: - Keeps all files in one directory - Sets working directory automatically - Easier to share and reproduce ### R Markdown and Quarto Create reproducible reports combining code, results, and narrative: - **R Markdown**: [rmarkdown.rstudio.com](https://rmarkdown.rstudio.com) - **Quarto**: [quarto.org](https://quarto.org) (used for this book) ### Version Control (Git/GitHub) Track changes to your code and data: - **Git**: Version control system - **GitHub**: Online platform for sharing code - **Resource**: [happygitwithr.com](https://happygitwithr.com) --- ## Summary This appendix provides a starting point for R resources in animal breeding. As you progress through the book, refer back to this appendix for package documentation and learning resources. **Key packages to master**: - **tidyverse** (data manipulation) - **lme4** (mixed models) - **AGHmatrix** (relationship matrices) - **rrBLUP** or **BGLR** (genomic prediction) - **AlphaSimR** (simulation) Happy coding!