ntrade: Ntrade calculation

Description

Calculates the quantity of potentially infested imported commodity ($N_{trade}$) from third countries where the pest is present, based on the provided trade data (TradeData object output of the trade_data() function).

Usage

ntrade(
  trade_data,
  filter_IDs = NULL,
  filter_period = NULL,
  summarise_result = NULL
)

Value

A data frame with the quantity of commodity imported by each country of interest (country_IDs) from countries or regions where the pest is present. The result is returned for each time period if summarise_result is not specified (default is NULL). If a summary function is specified, the result will be summarised accordingly.

Arguments

trade_data: An object of class TradeData that can be the output of trade_data().
filter_IDs: A vector containing the country IDs to filter (identification codes of the countries of interest). By default, it is set to NULL, meaning all reporter countries in the data frames will be considered.
filter_period: A vector specifying the time periods to filter, based on the time_period column. By default, it is set to NULL, meaning all time periods in the data frames will be considered.
summarise_result: A character vector specifying functions to summarise the $N_{trade}$ result for the selected time periods (filter_period). It accepts the expressions "mean" for the mean, "sd" for the standard deviation, "median" for the median value and "quantile(p)" where p is the probability for the quantiles to the given probabilities. See examples.

Details

The calculation of $N_{trade_i}$ for each country of interest $i$ is based on the equation: $$N_{trade_i} = ExtraPest_i - ExtraPest_i \sum_{j \neq i} R_{ij} + \sum_{j \neq i} ExtraPest_j R_{ji},$$ where:

$N_{trade_i}$: quantity of commodity from third countries remaining in country $i$, taking into account the direct importation from third countries where the pest is present, the re-exportation to other countries of interest, and the indirect importation of the commodity from other countries of interest.
$ExtraPest_i$ and $ExtraPest_j$: quantity of commodity imported by country $i$ and country $j$ from third countries where the pest is present (direct import), during the period of time considered.
$R_{ij}$ and $R_{ji}$: proportion of intra-regional trade relative to the total available quantity in the exporting country defined as: $$R_{ij} = IntraExp_{ij}/(IP_i + ExtraTotal_i), \\ R_{ji} = IntraExp_{ji}/(IP_j + ExtraTotal_j).$$ Specifically, $R_{ij}$ indicates the proportion of the commodity that is exported from country $i$ to country $j$ ($IntraExp_{ij}$), while $R_{ji}$ indicates the proportion exported from country $j$ to country $i$ ($IntraExp_{ji}$), in both cases out of the total available commodity in the exporter country. The total available quantity is considered as tha sum of the internal production of the country ($IP$) and the total quantity imported from third countries ($ExtraTotal$). Thus, the quantity of $ExtraPest_i$ re-exported from country $i$ to all countries $j$ is approximated by $ExtraPest_i \sum_{j \neq i} R_{ij}$, and the quantity of $ExtraPest_j$ re-exported from all countries $j$ to country $i$ as $\sum_{j \neq i} ExtraPest_j R_{ji}$.

Examples

Run this code

## Example with simulated trade data for Northern America
library(dplyr)
data("datatrade_NorthAm")
# Total extra-import data: data contains imports from 5 third countries (column partner). 
extra_total <- datatrade_NorthAm$extra_import
# Extra-import data from countries where the pest is present (e.g., CNTR_1 and CNTR_2)
CNTR_pest <- c("CNTR_1", "CNTR_2")
extra_pest <- datatrade_NorthAm$extra_import %>% filter(partner%in%CNTR_pest)
# Intra-trade data
intra_trade  <- datatrade_NorthAm$intra_trade
# Internal production data
internal_production  <- datatrade_NorthAm$internal_production
# Generate trade data (TradeData object)
trade_NorthAm <- trade_data(extra_total = extra_total,
                            extra_pest = extra_pest,
                            intra_trade = intra_trade,
                            internal_production = internal_production)
# Calculation of the Ntrade for each time period
ntrade_NorthAm <- ntrade(trade_data = trade_NorthAm)
head(ntrade_NorthAm)
# Ntrade summary for the time periods
ntrade_NorthAm_summary <- ntrade(trade_data = trade_NorthAm,
                                 summarise_result = c("mean", "sd", 
                                                      "quantile(0.025)", 
                                                      "median",
                                                      "quantile(0.975)"))
head(ntrade_NorthAm_summary)
# Plot the median of Ntrade
library(ggplot2)
plot_countries(data = ntrade_NorthAm_summary,
               iso_col = "country_IDs", 
               values_col = "median") +
  xlim(-180,-20) + ylim(0,90)

## Example with simulated trade data for Europe 
# Load data
data("datatrade_EU")
# Total extra-import data: the total import is identified as partner "Extra_Total"
extra_total <- datatrade_EU$extra_import %>% filter(partner=="Extra_Total")
# Extra-import data from countries where the pest is present
extra_pest <- datatrade_EU$extra_import %>% filter(partner!="Extra_Total")
# Intra-trade data
intra_trade  <- datatrade_EU$intra_trade
# Internal production data
internal_production  <- datatrade_EU$internal_production
# Generate trade data (TradeData object)
trade_EU <- trade_data(extra_total = extra_total,
                       extra_pest = extra_pest,
                       intra_trade = intra_trade,
                       internal_production = internal_production)
# Ntrade mean and sd for the time periods
ntrade_EU <- ntrade(trade_data = trade_EU,
                    summarise_result = c("mean", "sd"))
# Plot Ntrade mean
plot_countries(data = ntrade_EU, 
               iso_col="country_IDs", 
               values_col="mean") +
  xlim(-40,50) + ylim(25,70)
# Ntrade for selected countries and a specific time period
# Sample 5 countries from trade data
country_IDs <- sample(unique(trade_EU$total_trade$country_IDs), 5)
ntrade_EU_s <- ntrade(trade_data = trade_EU,
                      filter_IDs = country_IDs,
                      filter_period = 2020)
head(ntrade_EU_s)
# Plot Ntrade result
plot_countries(data = ntrade_EU_s, 
               iso_col="country_IDs", 
               values_col="Ntrade_2020") +
  xlim(-40,50) + ylim(25,70)