Example #1

Running FrEDI with Default Parameters

FrEDI is commonly used to project the annual, climate-driven physical and economic impacts associated with user-defined trajectories of U.S. temperature change (relative to a 1985-2005 baseline), population, and GDP.

This vignette provides a simple example of how to run and analyze data from FrEDI’s default scenario.

This example script:

Installs the FrEDI R package from GitHub
Sets FrEDI input and run parameters.
Runs FrEDI with inputs specified in Step 2
Shows example types of analyses using FrEDI output data.

For more information about FrEDI, see the About page and FrEDI Technical Documentation

Step 1. Install `FrEDI` R package

When installing for the first time, see Installing & Running FrEDI page.

Load package

library(FrEDI)

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After successfully installing FrEDI, documentation for FrEDI functions can be accessed in the same way as for other R packages.

For an overview of FrEDI’s user-defined functions, type library(help="FrEDI") into an R console (this command will show documentation for FrEDI even if the package is not installed).

For documentation for a specific function, type help("*functionName*", package="FrEDI") into an R console, where functionName is the name of one of the functions in FrEDI (e.g., help("aggregate_impacts", package="FrEDI")).

If FrEDI has been installed, users can also search for function-specific documentation in RStudio through the Help window. Move the focus to the Help window using the keyboard shortcut Ctrl+3 or toggle the search field in Help using Ctrl+Alt+F1. Documentation for each function includes examples.

Step 2. Set `FrEDI` Runtime parameters

First, use this chunk to specify & format input projections. Either provide paths to specific files, or set to NULL to use default projections for each variable

Use this chunk to specify the input trajectories (temperature, population, GDP) and runtime parameters for FrEDI.

# To run FrEDI for more than one scenario, the code below can be
# adapted into a loop to format the inputs for each scenario. 

#***********************************************
#1. Specify & Format Input Trajectories (temperature, population, U.S. GDP)

## Input Files
tempInputFile <- NULL
# Description: csv file with time series of temperature relative to 1986-2005 average 
# (units: degC, values: >=0)
# data must start in 2000 or earlier and can be global or CONUS
# If global --> must convert to CONUS temperature using the import_inputs() helper function
# column 1 = 'year', column 2 = 'temp_C'

slrInputFile <- NULL 
# Description: csv file with time series of global mean sea level rise relative to 2000
# (units: cm, values: >= 0 and <= 250)
# data must start in 2000 or earlier
# column 1 = 'year', column 2 = 'slr_cm'
# If NULL - slr is calculated from the input temperature trajectory

gdpInputFile <- NULL
# Description: csv file with time series of U.S. Gross Domestic Product (units: 2015$, values: >= 0) 
# data must start in 2010 or earlier
# column 1 = 'year', column 2 = 'gdp_usd' 
# If NULL - use default GDP trajectory

popInputFile <- NULL
# Description: csv file with time series of annual U.S. population (values >= 0) 
# data must start in 2010 or earlier
# column 1 = 'year', column 2 = 'pop', column 3 = depends on "popArea"
# If NULL - use default population trajectory (from ICLUS)


## Input Trajectory parameters
popAreaflag = 'state'   
# Description: Use this to specify the geographical scale of the population data
# Options: "state","regional","national".
# For more information, see `?import_inputs()` for more information

temptypeflag <- 'global' 
# Description: Use this to specify whether the input temperature is global or CONUS
# import_inputs() will convert to global to CONUS temperature
# Options: global (input is global T), conus (input is CONUS T)


## Use the import_inputs() helper function to format the input trajectories for use in FrEDI
inputs_list <- import_inputs(tempfile = tempInputFile,
                             slrfile = slrInputFile,
                             popfile = popInputFile,
                             gdpfile = gdpInputFile,
                             temptype = temptypeflag,
                             popArea = popAreaflag)

If no input files are specified, run_fredi() will use default temperature, U.S. population, and GDP projections. In this case, run_fredi() will calculate annual projected sea level rise based on the default temperature change.

Default population scenarios are based on UN Median Population projection (United Nations, 2015) and EPA’s ICLUSv2 model (Bierwagen et al., 2010; EPA 2017), and GDP from the EPPA version 6 model (Chen et al., 2015). Default temperature projections are from the GCAM reference scenario. Current default projections of U.S. GDP, national population, and U.S. temperature in the year 2090 are 70 trillion (2015USD), 438 million, and 3.4°C respectively.

Next, set FrEDI runtime parameters

thru2300Flag = FALSE
# Purpose: 
#   Specify whether to run FrEDI through 2100 (default) or extend to 2300
# Default: FALSE (will run to 2100)

SectorListFlag <- NULL  
# Purpose: 
#   Specify the vector of sectors to calculate results for
# Default: report output for all sectors
# See FrEDI:get_sectorInfo() for list of all sectors

aggLevelFlag <- c('national','modelaverage','impactyear')
# Purpose: 
#   Specify the desired level of results aggregation. For example,
#   to report national total results across all underlying climate-model 
#   damage functions, set the flag to c('national','modelaverage')
# Options: at least one from c('national', 'modelaverage', 'impactyear',
#   'impacttype', 'all'), or "none". 
# Default: c('national', 'modelaverage', 'impactyear','impacttype')

maxYearFlag <- 2100
# Purpose: 
#   Specify the last year of the analysis 
# Default: 2100

elasticityFlag <- 1   
# Purpose: 
#   Specify the income elasticity used to adjust the Value of a
#   Statistical Life (VSL)
# Options: any numeric value
# Default: 1

outputListFlag <- FALSE
# Purpose:
#   Specify whether to return input arguments in addition to results data frame
# Options: TRUE/FALSE. 
# Default: FALSE

allColsFlag <- TRUE
# Purpose:
#   Specify whether to include physical and economic multipliers (used in testing)
# Options: TRUE/FALSE. 
# Default: FALSE

silentFlag <- TRUE       
# Purpose:
#   Specify the level of messaging to the user
# Options: TRUE/FALSE. 
# Default: TRUE

Step 3. Run `FrEDI`

Run FrEDI using the main run_fredi() function

Default parameter options are used for any parameters that are not specified in run_fredi().

#Run FrEDI using inputs and parameters set in Step #2

output_df <- run_fredi(inputsList = inputs_list, 
                       sectorList = SectorListFlag,
                       aggLevels  = aggLevelFlag,
                       elasticity = elasticityFlag,
                       maxYear    = maxYearFlag,
                       thru2300   = thru2300Flag,
                       outputList = outputListFlag,
                       allCols    = allColsFlag,
                       silent     = silentFlag
)

## Checking input values...

##  No temperature scenario provided...using default temperature scenario...

##  Creating SLR scenario from temperature scenario...

##  No GDP scenario provided...Using default GDP scenario...

##  Creating population scenario from defaults...

## Updating scalars...

## Calculating impacts...

## Formatting results...

## 
## Finished.

# Option: write output
## Write Full Dataframe to CSV (or feather)
# write.csv(output_df, './output/example_output.csv')

#First five lines of output dataframe
#output_df[1:5,]

Step 4. Analyze `FrEDI`

The following chunks provide three examples options for how to analyze the raw output dataframe from FrEDI::run_fredi()

Disclaimer: These results only provide an illustrative example and should NOT be used for further analysis.

Example 1

Extract national average of economic damages (in billions of dollars in 2015$) for all FrEDI sectors for a specified year

Output of this chunk includes a snapshot of a subset of the resulting dataframe

*Note: Use the sectorprimary ==1 and includeaggregate >=1 flags to filter the FrEDI dataframe for impacts from only the default impact sectors and variants. For recommendations on aggregating impacts across sectors, see the FrEDI Technical Documentation (Chapter 2) and FrEDI::run_fredi() documentation.

#specify year
c_years = c(2100)

df_filter <- output_df %>%
  # filter for primary sectors and variants (included to avoid 
  #  double counting final impacts)
  filter(sectorprimary ==1,
         includeaggregate >=1) %>%
  # use helper function to aggregate across the impact types of 
  #  each sector
  aggregate_impacts(aggLevels = 'impactType') %>%
  # filter for national aggregate results averaged across all GCMs, 
  #  for the specified year
  filter(model %in% c('Average','Interpolation'),
         region == "National Total",
         year %in% c_years) %>%
  # convert to billions of dollars
  mutate(annual_impacts = annual_impacts /1e9) %>% 
  # round results
  mutate(across(annual_impacts, round, 0)) %>%
  # select (and rename) relevant columns
  select('sector','region','year','annual_impacts') %>%
  rename('billions_2015$' = annual_impacts) 


#Display first 10 sectors
library('kableExtra')
kable(df_filter[1:10,], 
      caption = 'Example *Subset* of 2100 Annual National Sectoral Climate-Driven Impacts') %>%
  kable_styling(font_size = 12, full_width = F)

Example *Subset* of 2100 Annual National Sectoral Climate-Driven Impacts
sector	region	year	billions_2015$
ATS Temperature-Related Mortality	National Total	2100	2767
CIL Agriculture	National Total	2100	9
CIL Crime	National Total	2100	2
Climate-Driven Changes in Air Quality	National Total	2100	291
Coastal Properties	National Total	2100	14
Electricity Demand and Supply	National Total	2100	19
Electricity Transmission and Distribution	National Total	2100	15
Inland Flooding	National Total	2100	1
Labor	National Total	2100	56
Marine Fisheries	National Total	2100	0

Example 2

Extract national average of physical damages for all FrEDI sectors that include physical impacts, for a specified year

Output of this chunk includes a snapshot of a subset of the resulting dataframe

*Note: Use the sectorprimary ==1 and includeaggregate >=1 flags to filter the FrEDI dataframe for impacts from only the default impact sectors and variants.For recommendations on aggregating impacts across sectors, see the FrEDI Technical Documentation (Chapter 2) and FrEDI::run_fredi() documentation.

#specify year
c_years = c(2100)

df_filter <- output_df %>%
  # filter for primary sectors and variants (included to avoid 
  #  double counting final impacts)
  filter(sectorprimary ==1,
         includeaggregate >=1) %>%
  # filter for national aggregate results averaged across all GCMs
  #  and only include sectors that include physical impacts
  filter(model %in% c('Average','Interpolation'),
         region == "National Total",
         year %in% c_years,
         !(is.na(physicalmeasure))) %>%
  # round results
  mutate(across(c('physical_impacts'), round, 0)) %>%
  # select relevant columns to display
  select('sector','region','year','physicalmeasure',
         'impactType','physical_impacts')

#display first 10 rows
kable(df_filter[c(1,6:9,14),], 
      caption = 'Example *Subset* of 2100 Annual National Sectoral Climate-Driven Physical Impacts') %>%
  kable_styling(font_size = 12, full_width = F)

Example *Subset* of 2100 Annual National Sectoral Climate-Driven Physical Impacts
sector	region	year	physicalmeasure	impactType	physical_impacts
ATS Temperature-Related Mortality	National Total	2100	Premature Mortality	N/A	83858
CIL Crime	National Total	2100	Crimes	Property	3264
CIL Crime	National Total	2100	Crimes	Violent	5713
Climate-Driven Changes in Air Quality	National Total	2100	Premature Mortality	Ozone	2108
Climate-Driven Changes in Air Quality	National Total	2100	Premature Mortality	PM2.5	6717
Labor	National Total	2100	Lost Work Hours	N/A	659458039

Example 3

Extract state economic impacts for FrEDI sectors for a specified year

Output of this chunk includes a snapshot of a subset of the resulting dataframe

*Note: Use the sectorprimary ==1 and includeaggregate >=1 flags to filter the FrEDI dataframe for impacts from only the default impact sectors and variants. For recommendations on aggregating impacts across sectors, see the FrEDI Technical Documentation (Chapter 2) and FrEDI::run_fredi() documentation.

#specify year
c_years = c(2100)

#filter and aggregate data, assign to aggregate categories
df_filter <- output_df %>%
  # filter for primary sectors and variants (included to avoid 
  #  double counting final impacts)
  filter(sectorprimary ==1,
         includeaggregate >=1) %>%
  # use helper function to aggregate across the impact types of 
  #  each sector
  aggregate_impacts(aggLevels = 'impactType') %>%
  # filter for regional results, averaged across all GCMs, 
  #  for the specified year
  filter(model %in% c('Average','Interpolation'),
         !(region == "National Total"),
         year %in% c_years) %>%
  # convert to billions (2015$)
  mutate(annual_impacts = annual_impacts /1e9) %>%
  # round results
  mutate(across(c('annual_impacts'), round, 0)) %>%
  # sum across all sectors for each state
  group_by_at(.vars = c('state','year')) %>%
  ##  SEE RECOMMENDATIONS FOR AGGREGATION in FrEDI TECHNICAL DOCUMENTATION ##
  mutate(annual_impacts = case_when(sector == 'ATS Temperature-Related Mortality' ~ 
                             annual_impacts - annual_impacts[sector == 'Suicide'],
                             .default = annual_impacts)) %>%
  summarize_at(c("annual_impacts"), sum, na.rm=T) %>%
  ungroup %>%
  # select (and rename) relevant columns
  select('state','year','annual_impacts')%>%
  rename('billions_2015$' = annual_impacts)


kable(df_filter[1:10,], 
      caption = 'Example 2100 Annual Climate-Driven Damages, by State') %>%
  kable_styling(font_size = 12, full_width = F)

Example 2100 Annual Climate-Driven Damages, by State
state	year	billions_2015$
Alabama	2100	63
Arizona	2100	89
Arkansas	2100	36
California	2100	404
Colorado	2100	70
Connecticut	2100	28
Delaware	2100	9
District of Columbia	2100	6
Florida	2100	217
Georgia	2100	159

Please contact the FrEDI developers with additional questions.

August, 2024

Running FrEDI with Default Parameters

Step 1. Install FrEDI R package

Step 2. Set FrEDI Runtime parameters

Step 3. Run FrEDI

Step 4. Analyze FrEDI

Example 1

Example 2

Example 3

Step 1. Install `FrEDI` R package

Step 2. Set `FrEDI` Runtime parameters

Step 3. Run `FrEDI`

Step 4. Analyze `FrEDI`