corridor

CorridoR is a wrapper for the Northern Canadian Corridor data on the potential impact of the Northern Corridor on Canadian economy and global maritime traffic.

https://github.com/warint/corridor

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CorridoR is a wrapper for the Northern Canadian Corridor data on the potential impact of the Northern Corridor on Canadian economy and global maritime traffic.

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# corridoR





## Overview

Although maritime transport has lost its importance for the transport of people, it has become the most important mode of transport for goods. Indeed, approximately 80% of global merchandise is traded by sea. As an essential part of international trade, commercial shipping relies on strategic trade routes to move goods efficiently.

corridoR is an R wrapper to illustrate the reality of world maritime trade by offering researchers and students access to real maritime distances between the different major ports of the planet.  Too often, distances are calculated as the crow flies, but CorridoR uses existing sea routes to make its calculations. 

The corridoR project was also developed to analyze the potential impact of the Northern Corridor on the Canadian economy and global maritime traffic. We took more than 20 000 ship voyages passing through the Panama Canal and calculated their marine distances.

To analyze 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.

## 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

# Practical usage

## Quick start

First, install corridoR:

```{r, eval=FALSE}
devtools::install_github("warint/corridoR")
```


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

```{r}
library(corridoR)
```

## 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.

```{r, eval = FALSE}
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.

```{r, eval = FALSE}
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!

```{r,eval=FALSE}
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.

Owner

Citation (citation.cff)

cff-version: 1.2.0
message: "If you use this package, please cite it as below."
authors:
- family-names: "Warin"
  given-names: "Thierry"
  orcid: "https://orcid.org/0000-0002-5921-3428"
title: "corridoR: Client for accessing real maritime distances between the different major ports of the planet"
version: 0.1.0
doi: 10.6084/m9.figshare.16826563.v1
date-released: 2019
url: "https://warint.github.io/corridoR/"

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