# Welcome

Welcome to the website of the book *Handbook of Graphs and Networks in People Analytics* by Keith McNulty. This is the second volume in a series of technical textbooks for professionals working in analytics. To view or order a print copy of the first volume, please go to its website: Handbook of Regression Modeling in People Analytics.

**Note**: This book is published by Chapman & Hall/CRC and can be purchased directly from their website at a 20% discount with discount code HBG20, as well as from Amazon and other book retailers. *Please consider buying this book if you find it useful* - all the authorâ€™s royalties are donated to the Red Cross Ukraine Appeal. The online version of this book is free to read here (thanks to Chapman & Hall/CRC), and licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. If you have any feedback, please feel free to file an issue on GitHub. Thank you!

The book is available in bootstrap format or in a more plain gitbook format.

## Notes on data used in this book

For R and Python users, each of the data sets used in this book can be downloaded individually by following the code in each chapter. Alternatively, if you intend to work through all the chapters, all data sets can be downloaded in advance by installing the `onadata`

package.

For R users, this can be installed as follows:

```
# install onadata package
install.packages("onadata")
library(onadata)
# see a list of data sets
data(package = "onadata")
# find out more about a specific data set ('karate' example)
help(karate)
```

For Python users, use `pip install onadata`

to install the package into your environment. Then, to use the package:

## Technical note for R users

This book uses the new native pipe operator `|>`

which was introduced in R version 4.1.0. Users using an older version of R, or who wish to use the alternative pipe operator `%>%`

can simply replace this in all code. However, an appropriate library containing `%>%`

will need to be loaded, such as `magrittr`

, `dplyr`

or `tidyverse`

.

Many thanks!

*Last updated: 02 May 2024*