Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/acircleda/footprint

An R package to calculate carbon footprints from air travel based on IATA airport codes or latitude and longitude.
https://github.com/acircleda/footprint

Last synced: 11 days ago
JSON representation

An R package to calculate carbon footprints from air travel based on IATA airport codes or latitude and longitude.

Awesome Lists containing this project

README 

        
---

output: github_document

---



[![R-CMD-check](https://github.com/acircleda/footprint/workflows/R-CMD-check/badge.svg)](https://github.com/acircleda/footprint/actions)

[![CRAN status](https://www.r-pkg.org/badges/version/footprint)](https://CRAN.R-project.org/package=footprint)



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# footprint 




The goal of footprint is to calculate carbon footprints from air travel based on IATA airport codes or latitude and longitude.



## Installation



You can install the development version from [GitHub](https://github.com/) with:



```{r, warning=FALSE, message=FALSE, error=FALSE, eval=FALSE}

# install.packages("remotes")

remotes::install_github("acircleda/footprint")

```



## Data and Methodology



Package `footprint` uses the the Haversine great-circle distance formula

to calculate distance between airports or distance between latitude and

longitude pairs. This distance is then used to derive a carbon footprint

estimate, which is based on conversion factors from the Department for

Environment, Food & Rural Affairs (UK) 2019 Greenhouse Gas Conversion

Factors for Business Travel (air):

.



## Example Usage



Load `footprint` using



```{r}

library(footprint)

library(tidyverse)

```



### Using Airport Codes



You can use pairs of three-letter IATA airport codes to calculate distance. This function uses the [`airportr`](https://github.com/dshkol/airportr) package, which contains the data and does the work of getting the distance between airports. *Note*: the `airportr` package offers a number of useful functions for looking up airports by city or name and getting the IATA airport codes.



#### Calculating a Single Trip



The example below calculates a simple footprint estimation for an economy flight from Los Angeles International (LAX) to Heathrow (LHR). The estimate will be in CO~2~e (carbon dioxide equivalent, including radiative forcing). The output is always in kilograms.



```{r, warning=FALSE, message=FALSE, error=FALSE}

airport_footprint("LAX", "LHR", "Economy", "co2e")

```



If there is a layover in Chicago, you could calculate each leg of the trip as follows:



```{r}

airport_footprint("LAX", "ORD", "Economy", "co2e") + 

  airport_footprint("ORD", "LHR", "Economy", "co2e")

```



#### Calculating More than One Trip



We can calculate the footprint for multiple itineraries at the same time and add to an existing data frame using `mutate`. Here is some example data:



```{r}

library(tibble)



travel_data <- tibble(

  name = c("Mike", "Will", "Elle"),

  from = c("LAX", "LGA", "TYS"),

  to = c("PUS", "LHR", "TPA")

)

```



```{r echo=FALSE, message=FALSE, warning=FALSE}

library(dplyr)



travel_data |>

  knitr::kable()

```



Here is how you can take the `from` and `to` data and calculate emissions for each trip. The following function calculates an estimate for CO~2~ (carbon dioxide with radiative forcing).



```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}

travel_data |>

  rowwise() |>

  mutate(emissions = airport_footprint(from, to, "Economy", output = "co2"))

```



```{r echo=FALSE}

travel_data |>

  rowwise() |>

  mutate(emissions = airport_footprint(from, to, "Economy", output = "co2")) |>

  knitr::kable()

```



#### Calculating More than One Trip and Different years



We can calculate the footprint for multiple itineraries at the same time and add to an existing data frame using `mutate`. Here is some example data:



```{r}

library(tibble)



travel_data <- tibble(

  name = c("Mike", "Will", "Elle", "Elle"),

  from = c("LAX", "LGA", "TYS", "TYS"),

  to = c("PUS", "LHR", "TPA", "TPA"),

  date = c("2024-04-05", "2023-04-02", "2024-06-12", "2019-06-12")

)

```



```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}

travel_data |>

  rowwise() |>

  mutate(emissions = airport_footprint(from, to, "Economy", output = "co2", year=year(date)))

```



```{r echo=FALSE}

travel_data |>

  rowwise() |>

  mutate(emissions = airport_footprint(from, to, "Economy", output = "co2", year=year(date))) |>

  knitr::kable()

```



## From Latitude and Longitude



If you have a list of cities, it might be easier to calculate emissions based on longitude and latitude rather than trying to locate the airports used. For example, one could take city and state data and join that with data from `maps::us.cities` to quickly get latitude and longitude. They can then use the `latlong_footprint()` function to easily calculate emissions based on either a single itinerary or multiple itineraries:



### Calculating a Single Trip



The following example calculates the footprint of a flight from Los Angeles (34.052235, -118.243683) to Busan, South Korea (35.179554, 129.075638). It assumes an average passenger (no `flightClass` argument is included) and its output will be in kilograms of CO~2~e (the default)



```{r}

latlong_footprint(34.052235, -118.243683, 35.179554, 129.075638)

```



### Calculating Multiple Trips



You can use `mutate` to calculate emissions based on a dataframe of latitude and longitude pairs.



Here is some example data:



```{r}

travel_data2 <- tribble(~name, ~departure_lat, ~departure_long, ~arrival_lat, ~arrival_long,

         # Los Angeles -> Busan

        "Mike", 34.052235, -118.243683, 35.179554, 129.075638,

        # New York -> London

        "Will", 40.712776, -74.005974, 51.52, -0.10)

```



```{r echo=FALSE}

travel_data2 |> knitr::kable()

```



And here is code to apply it to a dataframe:



```{r eval=FALSE, include=TRUE}

travel_data2 |>

  rowwise() |>

  mutate(emissions = latlong_footprint(departure_lat,

                                       departure_long,

                                       arrival_lat,

                                       arrival_long))

```



```{r echo=FALSE}

travel_data2 |>

  rowwise() |>

  mutate(emissions = latlong_footprint(departure_lat,

                                       departure_long,

                                       arrival_lat,

                                       arrival_long)) |>

  knitr::kable()

```