mult_ctries: Identifies seizures with multiple countries of origin
In fmunderwood/ETISdbase: Provides functions to create outputs from ETIS database

Description Usage Arguments Value See Also Examples

View source: R/mult_ctries.R

The weight of a seizure with multiple countries of origin is allocated according to the proportion allocated to each country. It also identifies the countries listed at each stage in the trade chain. This is required for trade flows and le ratio accounting. This function interacts with the ETIS database and the seizures stored in df.1 created by df_quantities_RIE_separate.fn to identify all seizures with multiple countries of origin. For each seizure with multiple countries of origin it extracts countries of origin, discovery and transit (including export). The main use of this function is to contribute to the calculation of two new database tables for law enforcement ratio and trade flow accounting.

1 2	mult_ctries(year.from = 1900, year.to = 2100, statusMin = 3, df.1 = df.1)

`year.from`	Starting year
`year.to`	Ending year
`statusMin`	Minimum status of seizure record to include in summary (usually 3)
`df.1`	Dataframe with list of seizure records to be considered. Created using df_quantities_RIE_separate.fn

A list with four items. Each item is a dataframe with information about the seizures for which there are multiple countries of origin. The dataframes are:

disc	Country of discovery
	One row for each seizure
transit	Trade route countries
	Multiple rows for each seizure to give all combinations of export and transit countries (with seizure opportunity)
orig_raw	Countries of origin for raw ivory
	One row for each country of origin for each seizure. Includes the proportion of shipment allocated to each country
orig_wkd	Countries of origin for worked ivory
	One row for each country of origin for each seizure. Includes the proportion of shipment allocated to each country

df_quantities_RIE_separate, inout_tables_LE_TF_prep, inout_tables_LE_TF_final

year.from <- 1900
year.to <- 2100
statusMin <- 3

df.1 <- df_quantities_RIE_separate(year.from = year.from, year.to = year.to,
statusMin = statusMin, reg.model = 'wt est.Rdata')

mult.dat <- mult_ctries(year.from = 1900, year.to = 2100, statusMin = 3, df.1 = df.1)