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

Category: Emissions
Sub Category: Carbon Intensity and Accounting

Last synced: about 10 hours ago
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Repository metadata

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

• Host: GitHub
• URL: https://github.com/acircleda/footprint
• Owner: acircleda
• License: cc0-1.0
• Created: 2020-12-07T13:39:28.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2024-07-31T20:17:12.000Z (over 1 year ago)
• Last Synced: 2025-11-18T18:05:10.216Z (about 1 month ago)
• Language: R
• Homepage:
• Size: 8.4 MB
• Stars: 17
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Releases: 0
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • License: LICENSE.md

README.Rmd

          ---
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()
```


        

---

Owner metadata

• Name: Anthony Schmidt
• Login: acircleda
• Email:
• Kind: user
• Description:
• Website:
• Location:
• Twitter:
• Company:
• Icon url: https://avatars.githubusercontent.com/u/51770624?u=b9e7f6b6fa6d4d31cfa1e1738f6fc8b7e542ba3d&v=4
• Repositories: 5
• Last ynced at: 2023-02-28T07:42:56.132Z
• Profile URL: https://github.com/acircleda

---

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Last synced: 3 days ago

Name	Email	Commits
kkulma	k****a@g****m	29
acircleda	a****n@y****m	25
Your	y****l@e****m	6
Anthony Schmidt	5****a	1

---

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Last synced: 3 months ago

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/acircleda/footprint

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• Total packages: 1
• Total downloads:
  • cran: 244 last-month
• Total dependent packages: 0
• Total dependent repositories: 0
• Total versions: 2
• Total maintainers: 1

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Dependencies

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