Replace I with we (#150)

* Fix graphs introduction

* Fix goals

* Update Makefile with nams custom installation

* Add installation instructions for custom package

Makefile

@@ -14,6 +14,7 @@ conda:
 	set -ex
 	conda env create -f environment.yml
 	conda activate nams
+    python setup.py develop
 	python checkenv.py
 
 venv:
@@ -29,7 +30,7 @@ venv:
 	virtualenv network
 	source network/bin/activate && pip install -r requirements.txt
 	echo "Run 'source network/bin/activate' to begin"
-
+    python setup.py develop
 	python checkenv.py
 
 check:

docs/introduction/graphs.md

@@ -1,14 +1,14 @@
 ## Introduction
 
-In our world, networks are an immensely useful _data modelling tool_ 
+In our world, networks are an immensely useful _data modelling tool_
 to model complex _relational_ problems.
 Building on top of a network-oriented data model,
 they have been put to great use in a wide variety of settings.
 
 ## A _formal_ definition of networks
 
 Before we explore examples of networks,
-I wanted to first give you a more formal definition
+we want to first give you a more formal definition
 of what networks are.
 The reason is that knowing a _formal_ definition
 helps us refine our application of networks.
@@ -42,7 +42,7 @@ while the "edges" are the lines between the shapes.
 Now that we have a proper definition of a graph,
 let's move on to explore examples of graphs.
 
-One example I am fond of, based on my background as a biologist,
+One example I (Eric Ma) am fond of, based on my background as a biologist,
 is a protein-protein interaction network.
 Here, the graph can be defined in the following way:
 
@@ -62,7 +62,7 @@ With Twitter, the graph can be defined in the following way:
 - edges/relationships are defined as "one user has decided to follow another".
 
 Now that you've seen the framework for defining a graph,
-I'd like to invite you to answer the following question:
+we'd like to invite you to answer the following question:
 **What examples of networks have _you_ seen before in your profession?**
 
 Go ahead and list it out.
@@ -100,7 +100,7 @@ In that way, the relationship is automatically bi-directional.
 
 ## Edges define the interesting part of a graph
 
-While in graduate school, I once sat in a seminar
+While in graduate school, I (Eric Ma) once sat in a seminar
 organized by one of the professors on my thesis committee.
 The speaker that day was John Quackenbush,
 a faculty member of the Harvard School of Public Health.

docs/preface/goals.md

@@ -1,19 +1,19 @@
-My learning goals for you with this book
+Our learning goals for you with this book
 can be split into the technical and the intellectual.
 
 ## Technical Takeaways
 
-Firstly, I would like to equip you to be familiar
+Firstly, we would like to equip you to be familiar
 with the NetworkX application programming interface (API).
 The reason for choosing NetworkX is because
 it is extremely beginner-friendly,
 and has an API that matches graph theory concepts very closely.
 
-Secondly, I would like to show you how you can visualize graph data
+Secondly, we would like to show you how you can visualize graph data
 in a fashion that doesn't involve showing mere hairballs.
 Throughout the book, you will see examples of what we call
 _rational graph visualizations_.
-I have developed a companion package, `nxviz`,
+One of our authors, Eric Ma, has developed a companion package, `nxviz`,
 that provides a declarative and convenient API
 (in other words an attempt at a "grammar")
 for graph visualization.
@@ -32,16 +32,16 @@ such as statistical inference and linear algebra.
 ## Intellectual Goals
 
 Beyond the technical takeaways,
-I hope to broaden how you think about data.
+we hope to broaden how you think about data.
 
-The first idea I hope to give you
+The first idea we hope to give you
 the ability to think about your data
 in terms of "relationships".
 As you will learn,
 relationships are what give rise to the interestingness of graphs.
 That's where _relational insights_ can come to fore.
 
-The second idea I hope to give you
+The second idea we hope to give you
 is the ability to "think on graphs".
 This comes with practice.
 Once you master it, though,

docs/preface/setup.md

@@ -19,6 +19,16 @@ To go there, click on the following button:
 
 [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/ericmjl/Network-Analysis-Made-Simple/master)
 
+Once you're in there, do a final setup step,
+by opening up a terminal in the Jupyter session,
+and installing the custom package `nams` that we wrote,
+which contains data loaders and solutions.
+
+```bash
+# In the root directory of the repository
+python setup.py develop
+```
+
 ## `conda` environments
 
 We also recommend the use of `conda` environments!
@@ -55,6 +65,14 @@ Next, it activates the environment:
 conda activate nams
 ```
 
+We have a custom module for the project, which is called `nams`,
+that you will have to install.
+
+```bash
+# In the root directory of the cloned repository
+python setup.py develop
+```
+
 Finally, it runs a check on the environment
 to make sure everything is installed correctly: