---
title: "How To Use This Package"
author: "Mauricio Vargas"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
bibliography: ../inst/REFERENCES.bib
vignette: >
  %\VignetteIndexEntry{How To Use This Package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, cache = FALSE, echo = FALSE, message = FALSE, warning = FALSE}
knitr::opts_chunk$set(eval = TRUE, message = FALSE, warning = FALSE)
```

# World Trade and Per-Capita GDP

This example is inspired after the visualizations from @atlas2014 with some
ggplot additions.

## Package Data

```{r}
library(economiccomplexity)

# partial view of trade matrix
head(world_trade_avg_1998_to_2000)

# partial view of gdp vector
head(world_gdp_avg_1998_to_2000)
```

## Balassa Index

You can obtain Balassa Index with `balassa_index()`.

```{r}
bi <- balassa_index(world_trade_avg_1998_to_2000)

# partial view of index
bi[1:5,1:5]
```

Another possibility is to obtain Balassa Index without discretization.

```{r}
bi_dec <- balassa_index(world_trade_avg_1998_to_2000, discrete = F)

# partial view of index
bi_dec[1:5,1:5]
```

## Complexity Measures

You can compute complexity indexes (e.g. such as the Economic Complexity Index and Product Complexity Index) by using `complexity_measures()`. The calculations methods are *fitness* (default), *reflections*, *eigenvalues*. See [@measuringcomplexity2015] for the methodological details.

The eigenvalues also calls the reflections methods in order to correct the index sign
in some special cases when the correlation between the output from both methods is negative.

###  Complexity-Fitness

```{r}
com_fit <- complexity_measures(bi)

# partial view of indexes
com_fit$complexity_index_country[1:5]
com_fit$complexity_index_product[1:5]
```

### Reflections

```{r}
com_ref <- complexity_measures(bi, method = "reflections")

# partial view of indexes
com_ref$complexity_index_country[1:5]
com_ref$complexity_index_product[1:5]
```

### Eigenvalues

```{r}
com_eig <- complexity_measures(bi, method = "eigenvalues")

# partial view of indexes
com_eig$complexity_index_country[1:5]
com_eig$complexity_index_product[1:5]
```

## Proximity

Proximity matrices are used to create projections e.g. (country-country and product-product
networks) for bipartite networks. Using `proximity()` is straightforward.

```{r}
pro <- proximity(bi)

# partial view of proximity matrices
pro$proximity_country[1:5,1:5]
pro$proximity_product[1:5,1:5]
```

## Projections

The `projections()` function is designed to use `igraph` for the internal
computations and also to pass proximity-based networks to `igraph`, `ggraph`
or export to Cytoscape by saving the output as csv/tsv.

```{r}
library(igraph)

net <- projections(pro$proximity_country, pro$proximity_product)

# partial view of projections
E(net$network_country)[1:5]
E(net$network_product)[1:5]
```

Just two basic examples with `ggraph`.
```{r, fig.width=7, fig.height=7}
set.seed(200100)

library(Matrix)
library(ggraph)

aggregated_countries <- aggregate(
  world_trade_avg_1998_to_2000$value,
  by = list(country = world_trade_avg_1998_to_2000$country),
  FUN = sum
)

aggregated_countries <- setNames(aggregated_countries$x, aggregated_countries$country)

V(net$network_country)$size <- aggregated_countries[match(V(net$network_country)$name, names(aggregated_countries))]

ggraph(net$network_country, layout = "kk") +
  # geom_edge_link(aes(edge_width = weight), edge_colour = "#a8a8a8") +
  geom_edge_link(edge_colour = "#a8a8a8") +
  geom_node_point(aes(size = size), color = "#86494d") +
  geom_node_text(aes(label = name), size = 2, vjust = 2.2) +
  ggtitle("Proximity Based Network Projection for Countries") +
  theme_void()
```

```{r, fig.width=10, fig.height=10}
set.seed(200100)

aggregated_products <- aggregate(
  world_trade_avg_1998_to_2000$value,
  by = list(country = world_trade_avg_1998_to_2000$product),
  FUN = sum
)

aggregated_products <- setNames(aggregated_products$x, aggregated_products$country)

V(net$network_product)$size <- aggregated_products[match(V(net$network_product)$name, names(aggregated_products))]

ggraph(net$network_product, layout = "kk") +
  # geom_edge_link(aes(edge_width = weight), edge_colour = "#a8a8a8") +
  geom_edge_link(edge_colour = "#a8a8a8") +
  geom_node_point(aes(size = size), color = "#86494d") +
  geom_node_text(aes(label = name), size = 2, vjust = 2.2) +
  ggtitle("Proximity Based Network Projection for Products") +
  theme_void()
```

# Complexity Outlook

Both the Complexity Outlook Index and Complexity Outlook Gain are obtained
after the `complexity_outlook()` function.

```{r}
co <- complexity_outlook(
  economiccomplexity_output$balassa_index,
  economiccomplexity_output$proximity$proximity_product,
  economiccomplexity_output$complexity_measures$complexity_index_product
)

# partial view of complexity outlook
co$complexity_outlook_index[1:5]
co$complexity_outlook_gain[1:5,1:5]
```

# Productivy Levels

The `productivity_levels()` dataset follows the definitions from @atlas2014
and @exportmatters2005.

I don't have a per-capita GDP dataset for the Galactic Federation, so
I'll create simulated data for the example.

```{r}
pl <- productivity_levels(world_trade_avg_1998_to_2000, world_gdp_avg_1998_to_2000)

# partial view of productivity levels
pl$productivity_level_country[1:5]
pl$productivity_level_product[1:5]
```

# References
