library(corridoR)

Overview

corridoR is an R wrapper to easily access the Northern Corridor project database. The project was developed in order to analyse the potential impact of the Northern Corridor on Canadian economy and global maritime traffic. In order to do so, we took more than 20 000 ship voyages passing through the Panama Canal and calculated their marine distances.

To analyse the applicability and the profitability of the Canadian Northwest Passage, we took the same 20 000 routes and make them passed hypothetically through the Canadian Arctic. We then compared the distances to see which trips were shorter by the Northern Corridor. Distances are in nautical miles.

Practical usage

Quick start

First, install corridoR:

devtools::install_github("warint/corridoR")

Next, call corridoR to make sure everything is installed correctly.

library(corridoR)

Data Summary

The data are divided as follows:

MMSI: The numerical code associated with each ship voyages.

Previous_Port1: The name of the first port of the trip (PP1).

Previous_Port2: The name of the second port of the trip (PP2).

Next_Port1: The name of the third port of the trip (NP1).

Next_Port2: The name of the fourth port of the trip (NP2).

Country: The country associated to the each port (PP1,PP2,NP1,NP2).

ISO: Country’s ISO code for each port (PP1,PP2,NP1,NP2).

Longitude and Latitude: for each port.

Distance_PA: Distance calculated for ships passing through the Panama Canal.

Distance_PA_PP1toPP2: Distance between Previous_Port1 and Previous_Port2. Same logic applies for other ports.

Distance_PA_Total: Total distance for a selected ship voyage through Panama Canal.

Distance_NC: Distance calculated for ships passing through the Northern Corridor.

Distance_NC_PP1toPP2: Distance between Previous_Port1 and Previous_Port2. Same logic applies for other ports.

Distance_NC_Total: Total distance for a selected ship voyage through the Northern Corridor.

Distance_Analysys: Distance_NC_Total minus Distance_PA_Total to analyse the shortest distance. If the value is negative, th Northern Corridor is shorter

How-To

Step 1: Getting the country’s ISO code

A user needs to enter the ISO code of a country. To have access to this code, the following function provides this information.

corridor_country() # A list of all countries ISO code will be produced

corridor_country(country = "Canada") # The ISO code for Canada will be produced

corridor_country("Canada") # The ISO code for Canada will be produced

Step 2: Getting the port’s name

To have access to the ports included in the data, the following function provides this information.

corridor_port() # A list of all existing ports will be produced

corridor_port(country = "HOUS") # The port's name containing HOUS will be produced

corridor_port("HOUSTON") # The port's name containing HOUS will be produced

Step 3: Getting the data

Once the user knows the ISO code , s.he can collect the data in a very easy way through this function:

Be careful, you must put the empty arguments ("") to collect the data!

corridor_data() # It generates a data frame of the complete dataset

corridor_data(country = "CAN") # It generates a data frame of all the ships voyages containing a Canadian port

corridor_data(port = "QUEBEC") # It generates a data frame of all the ships voyages containing Houston.

corridor_data(country = "CAN", port = "QUEBEC") # It generates a data frame of all the ships voyages containing the country Canada and the port Quebec.

Acknowledgments

The author would like to thank the Center for Interuniversity Research and Analysis of Organizations (CIRANO, Montreal) for its support, as well as Thibault Senegas, Marine Leroi and Martin Paquette. The usual caveats apply.

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