Get Started • SECFC

Getting Started

Install the package

#install.packages("remotes")
remotes::install_github("jianing-d/SECFC")

How to Use SECFC

Follow these steps to calculate survey-based carbon footprints using SECFC.

Step 1: Load the Package

After installation, load the package with:

library(SECFC)
#> Loading required package: tidyverse
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
#> ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
#> ✔ purrr     1.0.4     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Step 2: Import Your Survey Dataset

Make sure your dataset is in a data frame or tibble format. If you’re using the Qualtrics template we provided, ensure that variable names match exactly. Here is an example of the expected data structure:

library(tibble)

data <- tribble(
  ~StartDate, ~EndDate, ~Status, ~IPAddress, ~Progress, ~`Duration (in seconds)`, ~Finished, ~RecordedDate, ~ResponseId,
  ~RecipientLastName, ~RecipientFirstName, ~RecipientEmail, ~ExternalReference, ~LocationLatitude, ~LocationLongitude,
  ~DistributionChannel, ~UserLanguage, ~Q39, ~CL_01_ClothingPurcha, ~CL_03_MonthlyEx_9, ~CL_03_MonthlyEx_10, ~CL_03_MonthlyEx_11,
  ~CL_03_MonthlyEx_12, ~CL_03_MonthlyEx_13, ~CL_03_MonthlyEx_14, ~CL_03_MonthlyEx_15, ~F_01_DietaryHabits_5,
  ~F_01_DietaryHabits_6, ~F_01_DietaryHabits_7, ~F_01_DietaryHabits_4, ~T_01_CarUsage, ~T_02_CarType, ~T_03_CarDistance,
  ~T_04_PublicTransport, ~T_05_PublicTransport, ~T_06_AirTravelLong, ~T_07_AirTravelShort, ~T_08_LongDistanceTra,
  ~EH_02_ElectricityBil_1, ~EH_03_ElectricityBil_1, ~EH_05_NaturalGasBill_1, ~EH_06_NaturalGasBill_1, ~PETS_4, ~PETS_5,
  ~SD_06_HouseholdSize_17, ~SD_06_HouseholdSize_18, ~SD_06_HouseholdSize_19, ~SD_07_Country, ~SD_08_ZipCode,
  
  "3/20/25 9:43", "3/20/25 9:47", "IP Address", "111.187.123.25", 100, 240, TRUE, "3/20/25 9:47", "R_3lENNCNKNsOdbgd",
  NA, NA, NA, NA, 31.2222, 121.4581, "anonymous", "EN", "Yes, I accept to proceed to the study",
  "7-12 times (About every 1-2 months)", 30, 250, 50, 0, 50, 100, 200, 7, 3, 4, 6, "5-6 days",
  "Gasoline Vehicle", "11-50 km or 6.2-31 miles", "Weekly", "5-10 km or 3.1-6.2 miles", "1-3 flights",
  "1-3 flights", "Monthly", 120, 1440, 50, 600, 1, 1, 2, 1, 0, "United States", 10001
)

✅ Note The following columns are automatically generated when you export responses from Qualtrics: ~StartDate, ~EndDate, ~Status, ~IPAddress, ~Progress, ~Duration (in seconds), ~Finished, ~RecordedDate, ~ResponseId,
~RecipientLastName, ~RecipientFirstName, ~RecipientEmail, ~ExternalReference, ~LocationLatitude, ~LocationLongitude,
~DistributionChannel, ~UserLanguage

Step 3: Run Emission Calculations

Now you’re ready to calculate emissions. Choose the functions depending on whether you want only the calculation results or also the process data.

🛒Consumption Emissions

Calculate the carbon footprint from general consumption.

cons <- calc_cons_emissions(data)
#> New column `ConsEmissions` representing total consumption emissions has been added to the dataset.
#> [1] 248.4907

With process details:

cons_process <- calc_cons_emissions_process(data)
#> New column `ConsEmissions` representing total consumption emissions has been added to the dataset.
#> Process calculation result data have been added.
#> [1] 248.4907

🍽 Food Emissions ️

Calculate the carbon footprint from food consumption.

food <- calc_food_emissions(data)
#> New column `FoodEmissions` representing total food emissions has been added to the dataset.
#> [1] 1399.998

With process details:

food_process <- calc_food_emissions_process(data)
#> New column `FoodEmissions` representing total food emissions has been added to the dataset.
#> Process calculation result data have been added.
#> [1] 1399.998

🏠 Housing Emissions

Calculate the carbon footprint from housing energy use.

housing <- calc_housing_emissions(data)
#> New column `HousingEmissions` representing total housing emissions has been added to the dataset.
#> [1] 4299.323

With process details:

housing_process <- calc_housing_emissions_process(data)
#> New column `HousingEmissions` representing total housing emissions has been added to the dataset.
#> Process calculation result data have been added.
#> [1] 4299.323

🐶 Pet Emissions

Calculate the carbon footprint from pet ownership.

pet <- calc_pet_emissions(data)
#> New column `PetEmissions` representing total pet-related emissions has been added to the dataset.
#> [1] 1105

With process details:

pet_process <- calc_pet_emissions_process(data)
#> New column `PetEmissions` representing total pet-related emissions has been added to the dataset.
#> Process calculation result data have been added.
#> [1] 1105

🚗 Transport Emissions

Calculate the carbon footprint from daily and long-distance transportation.

transport <- calc_transport_emissions(data)
#> New column `TransportEmissions` representing total transport-related emissions has been added to the dataset.
#> [1] 7688.554

With process details:

transport_process <- calc_transport_emissions_process(data)
#> New column `TransportEmissions` representing total transport-related emissions has been added to the dataset.
#> Process calculation result data have been added.
#> [1] 7688.554

🌍 Total Emissions

Calculate the total carbon footprint across all domains.

total <- calc_total_emissions(data)
#> New column `ConsEmissions` representing total consumption emissions has been added to the dataset.
#> [1] 248.4907
#> New column `FoodEmissions` representing total food emissions has been added to the dataset.
#> [1] 1399.998
#> New column `HousingEmissions` representing total housing emissions has been added to the dataset.
#> [1] 4299.323
#> New column `PetEmissions` representing total pet-related emissions has been added to the dataset.
#> [1] 1105
#> New column `TransportEmissions` representing total transport-related emissions has been added to the dataset.
#> [1] 7688.554
#> All individual emission calculations have been completed.
#> New column `TotalEmissions` representing total carbon footprint has been added to the dataset.
#> [1] 11875.15

With process details:

total_process <- calc_total_emissions_process(data)
#> New column `ConsEmissions` representing total consumption emissions has been added to the dataset.
#> Process calculation result data have been added.
#> [1] 248.4907
#> New column `FoodEmissions` representing total food emissions has been added to the dataset.
#> Process calculation result data have been added.
#> [1] 1399.998
#> New column `HousingEmissions` representing total housing emissions has been added to the dataset.
#> Process calculation result data have been added.
#> [1] 4299.323
#> New column `PetEmissions` representing total pet-related emissions has been added to the dataset.
#> Process calculation result data have been added.
#> [1] 1105
#> New column `TransportEmissions` representing total transport-related emissions has been added to the dataset.
#> Process calculation result data have been added.
#> [1] 7688.554
#> All individual emission calculations have been completed.
#> New column `TotalEmissions` representing total carbon footprint has been added to the dataset.
#> Process calculation result data have been added.
#> [1] 11875.15