Get Started
Get-Started.RmdGetting 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 3.5.2 ✔ 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 errorsStep 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, ~EH_07_WaterBill, ~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, 150, 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.
calc_cons_emissions(data)
#> ✅ A new data frame 'data_cons' is now available in your R environment.
#> # A tibble: 1 × 51
#> StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 3/20/25 9:43 3/20/2… IP Ad… 111.187.… 100 240 TRUE
#> # ℹ abbreviated name: ¹`Duration (in seconds)`
#> # ℹ 44 more variables: RecordedDate <chr>, ResponseId <chr>,
#> # RecipientLastName <lgl>, RecipientFirstName <lgl>, RecipientEmail <lgl>,
#> # ExternalReference <lgl>, LocationLatitude <dbl>, LocationLongitude <dbl>,
#> # DistributionChannel <chr>, UserLanguage <chr>, Q39 <chr>,
#> # CL_01_ClothingPurcha <chr>, CL_03_MonthlyEx_9 <dbl>,
#> # CL_03_MonthlyEx_10 <dbl>, CL_03_MonthlyEx_11 <dbl>, …With process details:
calc_cons_emissions_process(data)
#> ✅ A new data frame 'data_cons_process' is now available in your R environment.
#> # A tibble: 1 × 60
#> StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 3/20/25 9:43 3/20/2… IP Ad… 111.187.… 100 240 TRUE
#> # ℹ abbreviated name: ¹`Duration (in seconds)`
#> # ℹ 53 more variables: RecordedDate <chr>, ResponseId <chr>,
#> # RecipientLastName <lgl>, RecipientFirstName <lgl>, RecipientEmail <lgl>,
#> # ExternalReference <lgl>, LocationLatitude <dbl>, LocationLongitude <dbl>,
#> # DistributionChannel <chr>, UserLanguage <chr>, Q39 <chr>,
#> # CL_01_ClothingPurcha <chr>, CL_03_MonthlyEx_9 <dbl>,
#> # CL_03_MonthlyEx_10 <dbl>, CL_03_MonthlyEx_11 <dbl>, …🍽 Food Emissions ️
Calculate the carbon footprint from food consumption.
calc_food_emissions(data)
#> ✅ A new data frame 'data_food' is now available in your R environment.
#> # A tibble: 1 × 51
#> StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 3/20/25 9:43 3/20/2… IP Ad… 111.187.… 100 240 TRUE
#> # ℹ abbreviated name: ¹`Duration (in seconds)`
#> # ℹ 44 more variables: RecordedDate <chr>, ResponseId <chr>,
#> # RecipientLastName <dbl>, RecipientFirstName <dbl>, RecipientEmail <dbl>,
#> # ExternalReference <dbl>, LocationLatitude <dbl>, LocationLongitude <dbl>,
#> # DistributionChannel <chr>, UserLanguage <chr>, Q39 <chr>,
#> # CL_01_ClothingPurcha <chr>, CL_03_MonthlyEx_9 <dbl>,
#> # CL_03_MonthlyEx_10 <dbl>, CL_03_MonthlyEx_11 <dbl>, …With process details:
calc_food_emissions_process(data)
#> ✅ A new data frame 'data_food_process' is now available in your R environment.
#> # A tibble: 1 × 55
#> StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 3/20/25 9:43 3/20/2… IP Ad… 111.187.… 100 240 TRUE
#> # ℹ abbreviated name: ¹`Duration (in seconds)`
#> # ℹ 48 more variables: RecordedDate <chr>, ResponseId <chr>,
#> # RecipientLastName <dbl>, RecipientFirstName <dbl>, RecipientEmail <dbl>,
#> # ExternalReference <dbl>, LocationLatitude <dbl>, LocationLongitude <dbl>,
#> # DistributionChannel <chr>, UserLanguage <chr>, Q39 <chr>,
#> # CL_01_ClothingPurcha <chr>, CL_03_MonthlyEx_9 <dbl>,
#> # CL_03_MonthlyEx_10 <dbl>, CL_03_MonthlyEx_11 <dbl>, …🏠 Housing Emissions
Calculate the carbon footprint from housing energy use.
calc_housing_emissions(data)
#> ✅ A new data frame 'data_housing' is now available in your R environment.
#> # A tibble: 1 × 51
#> StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 3/20/25 9:43 3/20/2… IP Ad… 111.187.… 100 240 TRUE
#> # ℹ abbreviated name: ¹`Duration (in seconds)`
#> # ℹ 44 more variables: RecordedDate <chr>, ResponseId <chr>,
#> # RecipientLastName <lgl>, RecipientFirstName <lgl>, RecipientEmail <lgl>,
#> # ExternalReference <lgl>, LocationLatitude <dbl>, LocationLongitude <dbl>,
#> # DistributionChannel <chr>, UserLanguage <chr>, Q39 <chr>,
#> # CL_01_ClothingPurcha <chr>, CL_03_MonthlyEx_9 <dbl>,
#> # CL_03_MonthlyEx_10 <dbl>, CL_03_MonthlyEx_11 <dbl>, …With process details:
calc_housing_emissions_process(data)
#> ✅ A new data frame 'data_housing_process' is now available in your R environment.
#> # A tibble: 1 × 64
#> StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 3/20/25 9:43 3/20/2… IP Ad… 111.187.… 100 240 TRUE
#> # ℹ abbreviated name: ¹`Duration (in seconds)`
#> # ℹ 57 more variables: RecordedDate <chr>, ResponseId <chr>,
#> # RecipientLastName <lgl>, RecipientFirstName <lgl>, RecipientEmail <lgl>,
#> # ExternalReference <lgl>, LocationLatitude <dbl>, LocationLongitude <dbl>,
#> # DistributionChannel <chr>, UserLanguage <chr>, Q39 <chr>,
#> # CL_01_ClothingPurcha <chr>, CL_03_MonthlyEx_9 <dbl>,
#> # CL_03_MonthlyEx_10 <dbl>, CL_03_MonthlyEx_11 <dbl>, …🐶 Pet Emissions
Calculate the carbon footprint from pet ownership.
calc_pet_emissions(data)
#> ✅ A new data frame 'data_pet' is now available in your R environment.
#> # A tibble: 1 × 51
#> StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 3/20/25 9:43 3/20/2… IP Ad… 111.187.… 100 240 TRUE
#> # ℹ abbreviated name: ¹`Duration (in seconds)`
#> # ℹ 44 more variables: RecordedDate <chr>, ResponseId <chr>,
#> # RecipientLastName <lgl>, RecipientFirstName <lgl>, RecipientEmail <lgl>,
#> # ExternalReference <lgl>, LocationLatitude <dbl>, LocationLongitude <dbl>,
#> # DistributionChannel <chr>, UserLanguage <chr>, Q39 <chr>,
#> # CL_01_ClothingPurcha <chr>, CL_03_MonthlyEx_9 <dbl>,
#> # CL_03_MonthlyEx_10 <dbl>, CL_03_MonthlyEx_11 <dbl>, …With process details:
calc_pet_emissions_process(data)
#> ✅ A new data frame 'data_pet_process' is now available in your R environment.
#> # A tibble: 1 × 53
#> StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 3/20/25 9:43 3/20/2… IP Ad… 111.187.… 100 240 TRUE
#> # ℹ abbreviated name: ¹`Duration (in seconds)`
#> # ℹ 46 more variables: RecordedDate <chr>, ResponseId <chr>,
#> # RecipientLastName <lgl>, RecipientFirstName <lgl>, RecipientEmail <lgl>,
#> # ExternalReference <lgl>, LocationLatitude <dbl>, LocationLongitude <dbl>,
#> # DistributionChannel <chr>, UserLanguage <chr>, Q39 <chr>,
#> # CL_01_ClothingPurcha <chr>, CL_03_MonthlyEx_9 <dbl>,
#> # CL_03_MonthlyEx_10 <dbl>, CL_03_MonthlyEx_11 <dbl>, …🚗 Transport Emissions
Calculate the carbon footprint from daily and long-distance transportation.
calc_transport_emissions(data)
#> ✅ A new data frame 'data_transport' is now available in your R environment.
#> # A tibble: 1 × 51
#> StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 3/20/25 9:43 3/20/2… IP Ad… 111.187.… 100 240 TRUE
#> # ℹ abbreviated name: ¹`Duration (in seconds)`
#> # ℹ 44 more variables: RecordedDate <chr>, ResponseId <chr>,
#> # RecipientLastName <lgl>, RecipientFirstName <lgl>, RecipientEmail <lgl>,
#> # ExternalReference <lgl>, LocationLatitude <dbl>, LocationLongitude <dbl>,
#> # DistributionChannel <chr>, UserLanguage <chr>, Q39 <chr>,
#> # CL_01_ClothingPurcha <chr>, CL_03_MonthlyEx_9 <dbl>,
#> # CL_03_MonthlyEx_10 <dbl>, CL_03_MonthlyEx_11 <dbl>, …With process details:
calc_transport_emissions_process(data)
#> ✅ A new data frame 'data_transport_process' is now available in your R environment.
#> # A tibble: 1 × 61
#> StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 3/20/25 9:43 3/20/2… IP Ad… 111.187.… 100 240 TRUE
#> # ℹ abbreviated name: ¹`Duration (in seconds)`
#> # ℹ 54 more variables: RecordedDate <chr>, ResponseId <chr>,
#> # RecipientLastName <lgl>, RecipientFirstName <lgl>, RecipientEmail <lgl>,
#> # ExternalReference <lgl>, LocationLatitude <dbl>, LocationLongitude <dbl>,
#> # DistributionChannel <chr>, UserLanguage <chr>, Q39 <chr>,
#> # CL_01_ClothingPurcha <chr>, CL_03_MonthlyEx_9 <dbl>,
#> # CL_03_MonthlyEx_10 <dbl>, CL_03_MonthlyEx_11 <dbl>, …🌍 Total Emissions
Calculate the total carbon footprint across all domains.
calc_total_emissions(data)
#> ✅ A new data frame 'data_total' is now available in your R environment.
#> # A tibble: 1 × 56
#> StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 3/20/25 9:43 3/20/2… IP Ad… 111.187.… 100 240 TRUE
#> # ℹ abbreviated name: ¹`Duration (in seconds)`
#> # ℹ 49 more variables: RecordedDate <chr>, ResponseId <chr>,
#> # RecipientLastName <dbl>, RecipientFirstName <dbl>, RecipientEmail <dbl>,
#> # ExternalReference <dbl>, LocationLatitude <dbl>, LocationLongitude <dbl>,
#> # DistributionChannel <chr>, UserLanguage <chr>, Q39 <chr>,
#> # CL_01_ClothingPurcha <chr>, CL_03_MonthlyEx_9 <dbl>,
#> # CL_03_MonthlyEx_10 <dbl>, CL_03_MonthlyEx_11 <dbl>, …With process details:
calc_total_emissions_process(data)
#> ✅ A new data frame 'data_total_process' is now available in your R environment.
#> # A tibble: 1 × 94
#> StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 3/20/25 9:43 3/20/2… IP Ad… 111.187.… 100 240 TRUE
#> # ℹ abbreviated name: ¹`Duration (in seconds)`
#> # ℹ 87 more variables: RecordedDate <chr>, ResponseId <chr>,
#> # RecipientLastName <dbl>, RecipientFirstName <dbl>, RecipientEmail <dbl>,
#> # ExternalReference <dbl>, LocationLatitude <dbl>, LocationLongitude <dbl>,
#> # DistributionChannel <chr>, UserLanguage <chr>, Q39 <chr>,
#> # CL_01_ClothingPurcha <chr>, CL_03_MonthlyEx_9 <dbl>,
#> # CL_03_MonthlyEx_10 <dbl>, CL_03_MonthlyEx_11 <dbl>, …✅ 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