9 International Trade of EU

Libraries we will need:

library(tidyverse)
library(eurostat)
library(scales)
library(leaflet)
library(sf)
library(ggrepel)
library(ggthemes)
library(wesanderson)
library(igraph)

As usual, we will modify the EU countries tables:

EU_countries <- select(eu_countries, geo = code, country = name)

And we will get the shapefiles of EU countries to be used in maos and choropleths:

EU_SHP_0 <- get_eurostat_geospatial(resolution = 10, nuts_level = 0, year = 2016) %>%
  filter(geo %in% EU_countries$geo) %>%
  select(geo, geometry)

9.1 Exports of EU countries inside and outside EU

Table ext_tec03 contains annual data about international trade of EU countries, trade by partner country and NACE Rev. 2 activity. Let’s download:

ext_tec03 <- get_eurostat(id = "ext_tec03", 
                          stringsAsFactors = FALSE, 
                          time_format = "num")

Let us examine a little bit the data. We choose Germany for the year 2016 as example:

ext_tec03 %>% 
  filter(geo == 'DE') %>% 
  filter(time == 2016)

## # A tibble: 1,560 × 7
##    unit   stk_flow nace_r2 partner   geo    time values
##    <chr>  <chr>    <chr>   <chr>     <chr> <dbl>  <dbl>
##  1 NR_ENT EXP      A_F_H-U AE        DE     2016   1173
##  2 NR_ENT EXP      A_F_H-U AFR_N     DE     2016   1115
##  3 NR_ENT EXP      A_F_H-U AFR_OTH   DE     2016   1545
##  4 NR_ENT EXP      A_F_H-U AME_C_CRB DE     2016   1049
##  5 NR_ENT EXP      A_F_H-U AME_N     DE     2016   4458
##  6 NR_ENT EXP      A_F_H-U AME_S     DE     2016   1247
##  7 NR_ENT EXP      A_F_H-U AR        DE     2016    269
##  8 NR_ENT EXP      A_F_H-U ASI_NME   DE     2016   3084
##  9 NR_ENT EXP      A_F_H-U ASI_OTH   DE     2016   5361
## 10 NR_ENT EXP      A_F_H-U AT        DE     2016   1778
## # … with 1,550 more rows

We get a lot of data because we the dataset provides multiple entries for : - unit - flow - nace_r2 - partner

ext_tec03 %>% 
  filter(geo == 'DE') %>% 
  filter(time == 2016) %>%
  filter(unit == 'THS_EUR')

## # A tibble: 780 × 7
##    unit    stk_flow nace_r2 partner   geo    time   values
##    <chr>   <chr>    <chr>   <chr>     <chr> <dbl>    <dbl>
##  1 THS_EUR EXP      A_F_H-U AE        DE     2016  307960.
##  2 THS_EUR EXP      A_F_H-U AFR_N     DE     2016  345850.
##  3 THS_EUR EXP      A_F_H-U AFR_OTH   DE     2016  583489.
##  4 THS_EUR EXP      A_F_H-U AME_C_CRB DE     2016  531009.
##  5 THS_EUR EXP      A_F_H-U AME_N     DE     2016 3561899.
##  6 THS_EUR EXP      A_F_H-U AME_S     DE     2016  676355.
##  7 THS_EUR EXP      A_F_H-U AR        DE     2016   73745.
##  8 THS_EUR EXP      A_F_H-U ASI_NME   DE     2016 1123428.
##  9 THS_EUR EXP      A_F_H-U ASI_OTH   DE     2016 5338715.
## 10 THS_EUR EXP      A_F_H-U AT        DE     2016 2723769.
## # … with 770 more rows

9.2 Intra EU trade

Get the data

ext_tec03 <- get_eurostat(id = "ext_tec03", 
                          stringsAsFactors = FALSE, 
                          time_format = "num")

Filter:

ext_tec03 %>%
  filter(unit == 'THS_EUR') %>%
  filter(stk_flow == 'EXP') %>%
  filter(nace_r2 == 'TOTAL') %>%
  filter(partner %in% c('INT_EU', 'WORLD')) %>%
  filter(time == 2017)

## # A tibble: 70 × 7
##    unit    stk_flow nace_r2 partner geo    time     values
##    <chr>   <chr>    <chr>   <chr>   <chr> <dbl>      <dbl>
##  1 THS_EUR EXP      TOTAL   INT_EU  AT     2017 104803571.
##  2 THS_EUR EXP      TOTAL   INT_EU  BA     2017   4021610.
##  3 THS_EUR EXP      TOTAL   INT_EU  BE     2017 273351525.
##  4 THS_EUR EXP      TOTAL   INT_EU  BG     2017  16985170.
##  5 THS_EUR EXP      TOTAL   INT_EU  CH     2017 120059878.
##  6 THS_EUR EXP      TOTAL   INT_EU  CY     2017   1111658.
##  7 THS_EUR EXP      TOTAL   INT_EU  CZ     2017  91528346.
##  8 THS_EUR EXP      TOTAL   INT_EU  DE     2017 634176947.
##  9 THS_EUR EXP      TOTAL   INT_EU  DK     2017  54134399.
## 10 THS_EUR EXP      TOTAL   INT_EU  EE     2017   9242929.
## # … with 60 more rows

Join with eu_countries:

ext_tec03 %>%
  filter(unit == 'THS_EUR') %>%
  filter(stk_flow == 'EXP') %>%
  filter(nace_r2 == 'TOTAL') %>%
  filter(partner %in% c('INT_EU', 'WORLD')) %>%
  filter(time == 2017) %>%
  inner_join(EU_countries, by = "geo") 

## # A tibble: 56 × 8
##    unit    stk_flow nace_r2 partner geo    time     values country 
##    <chr>   <chr>    <chr>   <chr>   <chr> <dbl>      <dbl> <chr>   
##  1 THS_EUR EXP      TOTAL   INT_EU  AT     2017 104803571. Austria 
##  2 THS_EUR EXP      TOTAL   INT_EU  BE     2017 273351525. Belgium 
##  3 THS_EUR EXP      TOTAL   INT_EU  BG     2017  16985170. Bulgaria
##  4 THS_EUR EXP      TOTAL   INT_EU  CY     2017   1111658. Cyprus  
##  5 THS_EUR EXP      TOTAL   INT_EU  CZ     2017  91528346. Czechia 
##  6 THS_EUR EXP      TOTAL   INT_EU  DE     2017 634176947. Germany 
##  7 THS_EUR EXP      TOTAL   INT_EU  DK     2017  54134399. Denmark 
##  8 THS_EUR EXP      TOTAL   INT_EU  EE     2017   9242929. Estonia 
##  9 THS_EUR EXP      TOTAL   INT_EU  EL     2017  15134594. Greece  
## 10 THS_EUR EXP      TOTAL   INT_EU  ES     2017 172651570. Spain   
## # … with 46 more rows

Pivot the the table to add more columns:

eu_trade_intra <- ext_tec03 %>%
  filter(unit == 'THS_EUR') %>%
  filter(stk_flow == 'EXP') %>%
  filter(nace_r2 == 'TOTAL') %>%
  filter(partner %in% c('INT_EU', 'WORLD')) %>%
  filter(time == 2017) %>%
  inner_join(EU_countries, by = "geo") %>%
  pivot_wider(id_cols = c(geo, country),
              names_from = partner,
              values_from = values) 

Mutate and store the data:

eu_trade_intra <- ext_tec03 %>%
  filter(unit == 'THS_EUR') %>%
  filter(stk_flow == 'EXP') %>%
  filter(nace_r2 == 'TOTAL') %>%
  filter(partner %in% c('INT_EU', 'WORLD')) %>%
  filter(time == 2017) %>%
  inner_join(EU_countries, by = "geo") %>%
  pivot_wider(id_cols = c(geo, country),
              names_from = partner,
              values_from = values) %>%
  mutate(perc = round(100*INT_EU/WORLD, 1)) %>%
  select(geo, country, perc)

Plot the data as points:

ggplot(data = filter(eu_trade_intra, country != 'Malta'), 
       aes(x = reorder(country, perc), y = perc)) +
  geom_point(size = 3, color = wes_palette("BottleRocket1", 1)[[1]]) +
  coord_flip() +
  labs(x = "Country", y = "% of intra-EU exports",
       title = "Intra-EU exports of EU countries",
       subtitle = "% of total exports during 2017",
       caption = "Source: Eurostat table ext_tec03
               Apllied Informatics and Computational Economics Lab") +
  theme_wsj() +
  theme(text           = element_text(size = 14),
        axis.text      = element_text(size = 12),
        plot.title     = element_text(size = 18, face = "bold"),
        plot.subtitle  = element_text(size = 14, face = "bold"),
        plot.caption   = element_text(size = 12)) 

Or plot the data as chorepleth map:

eu_trade_intra_SHP <- inner_join(eu_trade_intra, EU_SHP_0, by = "geo") %>%
  st_as_sf()

ggplot(eu_trade_intra_SHP) +
  geom_sf() +
  geom_sf(aes(fill = perc), color = "white", size = 0) +
  geom_sf(data = EU_SHP_0, fill = NA, color = "white", size = 0.5) +
  scale_fill_continuous_tableau(palette = "Classic Red", breaks = seq(0, 100, by = 10), na.value = "gray60") +
  xlim(-10.0, 38.0) + ylim(35.5, 66.0) +
  labs(title = "EU Country exports to other EU countries",
       subtitle = "% of Total Exports", 
       caption = "Source: Eurostat database ext_tec01 
                  Apllied Informatics and Computational Economics Lab",
       fill = "% Exports.") +
  theme_void() +
  theme(text = element_text(size = 16, family = "Arial")) +
  theme(legend.key.height = unit(2, "cm")) +
  theme(legend.position = c(0.95, 0.5))+
  theme(plot.title = element_text(hjust = 0.0, vjust = -5)) +
  theme(plot.subtitle = element_text(hjust = -0.1, vjust = -14))

9.3 Network of connected countries

to_perc <- function(x, y=1) {
  if (is.na(x) == TRUE) x <- 0
  p <- round(100*x/y, 1) 
}

x1 <- ext_tec03 %>%
  inner_join(EU_countries, by = "geo") %>%
  filter(unit == 'THS_EUR') %>%
  filter(stk_flow == 'EXP') %>%
  filter(nace_r2 == 'TOTAL') %>%
  filter(partner != 'WORLD') %>%
  filter(time == 2017) %>%
  pivot_wider(id_cols = c(geo, country),
              names_from = partner,
              names_prefix = "p_",
              values_from = values) 

x2 <- ext_tec03 %>%
  inner_join(EU_countries, by = "geo") %>%
  filter(unit == 'THS_EUR') %>%
  filter(stk_flow == 'EXP') %>%
  filter(nace_r2 == 'TOTAL') %>%
  filter(partner == 'WORLD') %>%
  filter(time == 2017) %>%
  select(geo, WORLD = values)

trade_partner <- inner_join(x1, x2, by = "geo") %>%
  mutate_at(vars(starts_with("p_")), list( ~round(. *100 / WORLD, 1)))  %>%
  select(-WORLD) %>%
  pivot_longer(cols = starts_with("p_"), 
               names_to = "partner", 
               values_to = "values", 
               values_drop_na = TRUE
              ) %>%
  mutate(partner = str_sub(partner, 3, -1)) %>%
  filter(partner %in% EU_countries$geo) %>% 
  filter(values > 5) %>%
  select(from = geo, to = partner, weight = values)

g <- graph_from_data_frame(trade_partner, directed = TRUE, vertices = NULL)
g <- set_edge_attr(g, "weight", value = trade_partner$weight)
E(g)$width <- E(g)$weight/4 
plot(g,  edge.curved = TRUE,
      edge.label = E(g)$weight, 
      edge.width = 0.2*E(g)$weight)