---
title: 'Example workflow (`igraph` edition)'
subtitle: 'From data retrieval to an `igraph` object'
author: |
  | Fabio Ashtar Telarico*
  | University of Ljubljana, FDV
  | *Fabio-Ashtar.Telarico@fdv.uni-lj.si
output: 
  rmarkdown::html_vignette:
    toc: yes
vignette: >
  %\VignetteIndexEntry{workflow-igraph_financial}
  %\VignetteEngine{knitr::knitr}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

options(
  rmarkdown.html_vignette.check_title = FALSE
)

library(FinNet)
library(markdown)
```

# Introduction
For years now, authors and analysts have worked on financial data using *ad-hoc* tools or programming languages other than `R`. So, the package `FinNet` was born to provide all `R` users with the ability to study financial networks with a set of tool especially designed to this purpose. Specifically, `FinNet` offers both brand new tools and an interface to the almost limitless capabilities of `igraph` and `network`.

This vignette illustrates how to:

1. Retrieve the desired data using [`yahoofinancer`](https://CRAN.R-project.org/package=yahoofinancer );
2. Create the firm-firm (FF) matrix;
3. Create the corresponding graph;
4. Plot it with smart, nice-looking defaults.

# 1. Data retrieval

After having identified the firms of interest, the package can fetch all information on them as long as `yahoofinancer` is available. Otherwise, built-in data can be used:

```{r workflow_1, echo=TRUE}
# Check if `yahoofinancer` is installed
isTRUE(requireNamespace('yahoofinancer', quietly = TRUE))

# Create a list of the desired firms
data('firms_US')
```

```{r clean, echo=FALSE, include=FALSE}
firms <- firms_US
rm(firms_US)
```

# 2. Matrix construction
There are many function in the **`FF` function family** to rapidly build an adjacency matrix. In this step, `FF.norm.ownership()` will construct a normalised-valued matrix of common ownership

```{r workflow_3, echo=TRUE}
# Identify common-ownership relations in a firm-firm matrix
FF <- FF.norm.ownership(firms)
```

# 3. Graphing
A graph can be obtained easily using `FF.graph()`, which include **two preset aesthetics**: 'simple' and 'nice'

```{r workflow_4, echo=TRUE}
# Create a simple-looking graph
g <- FF.graph(FF, aesthetic = 'simple')
```

Some checks using the S3 methods implemented for `financial_matrix` objects and the extension of some `igraph` functions allow to verify the correctness of the graph:

```{r checks, echo=TRUE}
# The order of the graph equals the number of rows in the FF matrix
vcount(g) == nrow(FF)

# The names of its vertex match the row names of the FF matrix
V(g)$name == rownames(FF)
```


# 4. Plotting using default *nice* aesthetics 

The 'nice' defaults are more indicated for a visual inspection of the network.

```{r workflow_5, echo=TRUE}
# Load dataset
data('firms_BKB')

# Identify common-ownership relations in a firm-firm matrix
FF <- FF(firms_BKB, who = 'own',
                 ties = 'naive', Matrix = TRUE)

# Create a nice-looking graph
g <- FF.graph(FF, aesthetic = 'nice')

# Plot it
plot_igraph(g, vertex.label = NA, edge.arrow.size = .6, scale_vertex = 10)

```