Example workflow (<code>igraph</code> edition)

# Example workflow (`igraph` edition)

## | Fabio Ashtar Telarico* | University of Ljubljana, FDV | *Fabio-Ashtar.Telarico@fdv.uni-lj.si

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# 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`;
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:

    # Check if `yahoofinancer` is installed
    isTRUE(requireNamespace('yahoofinancer', quietly = TRUE))
    #> [1] TRUE

    # Create a list of the desired firms
    data('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

    # 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’

    # 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:

    # The order of the graph equals the number of rows in the FF matrix
    vcount(g) == nrow(FF)
    #> [1] TRUE

    # The names of its vertex match the row names of the FF matrix
    V(g)$name == rownames(FF)
    #> [1] TRUE TRUE TRUE

# 4. Plotting using default nice aesthetics

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

    # 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)