IOPS {iopspackage}

IOPS

Description

Takes user inputted trade data, any acceptable ISO country code and industrial value chain mapping to calculate various metrics (Economic- and Product complexity metrics, distance metrics, opportunity gain, and inequality metrics) of a given country in order to facilitate better decision making regarding industrial policymaking.

Usage

IOPS(
  CountryCode,
  tradeData,
  ComplexMethod = "eigenvalues",
  iterCompl = 20,
  GVCMapping = NULL,
  tradedigit = 6
)

Arguments

CountryCode

(Type: character/integer) Any accepted ISO country code could be used, e.g. "United Kingdom", "GBR", "GB", 828 would all be accepted if the United Kingdom is the desired country. This version of the package uses country_codes_V202201 from th BACI Hs17 2020 dataset, but can be changed in inst/extdata.

tradeData

(Type: csv) Accepts any CEPII BACI trade data. NOTE: tradeData and GVCMapping must be from the same "H" Family, e.g. both are from H3, etc., in order for the program to work correctly.

ComplexMethod

(Type: character) Methods used to calculate complexity measures. Can be any one of these methods: "fitness", "reflections" or "eigenvalues". Defaults to "eigenvalues".

iterCompl

(Type: integer) The number of iterations that the chosen complexity measure must use. Defaults to iterCompl = 20.

GVCMapping

(Type: csv) The desired value chain to be analysed. With Columns "Tiers", "Activity", and "HSCode". NOTE: tradeData and GVCMapping must be from the same "H" Family, e.g. both are from H3, etc., in order for the program to work correctly.

tradedigit

(Type: integer) Indicate if the raw trade digit summation should be done on a 6- or 4-digit level. Defaults to tradedigit = 6.

Value

A list that constrains ECI, PCI, Opportunity_Gain, distance, density, M_absolute, M_binary, Tier_Results, Product_Category_Results, Product_Results, respectively.

Examples

# Create a temporary directory
temp_dir <- tempfile()
dir.create(temp_dir)

# Set the working directory to the temporary directory
old_dir <- setwd(temp_dir)

#Generate example trade data
GeneratedTradeData <- data.frame(
  t = c(2020, 2020, 2020),
  i = c(4, 710, 710),
  j = c(842, 124, 251),
  k = c(842410, 110220, 845210),
  v = c(4.776, 0.088, 0.057),
  q = c(0.025, 0.007, 0.005)
)

# Use your function with generated trade data
IOPS(
  CountryCode = 710,
  tradeData = GeneratedTradeData,
  ComplexMethod = "reflections",
  iterCompl = 2,
  GVCMapping = NULL,
  tradedigit = 6
)


# Use your function with real trade data
IOPS(
  CountryCode = 710,
  tradeData = ExampleTradeData,
  ComplexMethod = "reflections",
  iterCompl = 2,
  GVCMapping = NULL,
  tradedigit = 6
)


# Clean up the temporary directory
  setwd(old_dir)  # Restore the original working directory
  unlink(temp_dir, recursive = TRUE)