Package Data

library(economiccomplexity)

# partial view of trade matrix
head(world_trade_avg_1998_to_2000)

##   country product value
## 1     afg    0011 30068
## 2     afg    0012 16366
## 3     afg    0111 19273
## 4     afg    0112   893
## 5     afg    0113   350
## 6     afg    0116  1561

# partial view of gdp vector
head(world_gdp_avg_1998_to_2000)

##   country value
## 1     abw 19185
## 2     ago  1540
## 3     alb  1433
## 4     and 27765
## 5     arb  3312
## 6     are 43082

Balassa Index

You can obtain Balassa Index with balassa_index().

bi <- balassa_index(world_trade_avg_1998_to_2000)

# partial view of index
head(bi)

## [1] 0 0 0 0 1 0

Another possibility is to obtain Balassa Index without discretization.

bi_dec <- balassa_index(world_trade_avg_1998_to_2000, discrete = F)

# partial view of index
head(bi_dec)

## [1] 0.2312238 0.1777917 0.0000000 0.0000000 2.4825815 0.0000000

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 (Mariani et al. 2015) 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.

com_fit <- complexity_measures(bi)

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

##        afg        ago        aia        alb        and 
## 0.78605655 0.03999516 1.05645538 1.24261128 1.37321261

com_fit$complexity_index_product[1:5]

##      0011      0012      0013      0014      0015 
## 0.7538883 0.7491391 2.3018690 0.9903918 1.3120378

com_ref <- complexity_measures(bi, method = "reflections")

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

##        afg        ago        aia        alb        and 
## -0.5788151 -1.7710696  1.4074821 -0.1754989  1.0738736

com_ref$complexity_index_product[1:5]

##        0011        0012        0013        0014        0015 
## -0.66255107 -1.62169899 -0.07449487  0.20554720  0.15848845

com_eig <- complexity_measures(bi, method = "eigenvalues")

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

##        afg        ago        aia        alb        and 
## -0.5764283 -1.7770752  1.4090414 -0.1732606  1.0772452

com_eig$complexity_index_product[1:5]

##        0011        0012        0013        0014        0015 
## -0.66657613 -1.62657599 -0.08149436  0.19917280  0.14935653

Proximity

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

pro <- proximity(bi)

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

## 5 x 5 sparse Matrix of class "dsCMatrix"
##            afg         ago         aia        alb         and
## afg 1.00000000 0.015873016 0.181818182 0.19689119 0.192513369
## ago 0.01587302 1.000000000 0.006993007 0.01554404 0.005347594
## aia 0.18181818 0.006993007 1.000000000 0.16580311 0.251336898
## alb 0.19689119 0.015544041 0.165803109 1.00000000 0.310880829
## and 0.19251337 0.005347594 0.251336898 0.31088083 1.000000000

pro$proximity_product[1:5,1:5]

## 5 x 5 sparse Matrix of class "dsCMatrix"
##           0011      0012      0013      0014      0015
## 0011 1.0000000 0.3658537 0.1707317 0.2439024 0.2682927
## 0012 0.3658537 1.0000000 0.2500000 0.2250000 0.2500000
## 0013 0.1707317 0.2500000 1.0000000 0.2500000 0.1200000
## 0014 0.2439024 0.2250000 0.2500000 1.0000000 0.2250000
## 0015 0.2682927 0.2500000 0.1200000 0.2250000 1.0000000

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.

library(igraph)

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

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

## + 5/484 edges from 559a8f0 (vertex names):
## [1] zaf--zwe tza--zmb tza--uga tuv--wlf tuv--umi

E(net$network_product)[1:5]

## + 5/1505 edges from 18a7d3a (vertex names):
## [1] 8981--8982 8946--9510 8922--8932 8921--8922 8852--8959

Just two basic examples with ggraph.

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()

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()