# 13 Module Build Process

## 13.1 Introduction

This chapter describes the package build process for creating or modifying packages and their constituent modules within the VisionEval framework. The build process can be used to incorporate localized or custom data into VE by making a change to a module and then rebuilding the package that is it part of. This chapter includes two case studies that discuss examples of localizing estimation data and walk through the process of substituting data in a module and re-building the VisionEval package that it is part of.

The steps involved in the process include:

• Collect the input data for the package
• Preprocess the input data into the required format
• Update the data files in the VisionEval package source
• Build and install the package for use in VisionEval
• Use the re-built package in your model

## 13.2 Context

The VisionEval “build process” rebuilds VisionEval packages to incorporate modified data in the module’s model estimation and data processing steps in order to update data resources such as PUMS (Public Use Microdata Samples from the US Census) and estimated model coefficients. The build process can be conducted through R or RStudio starting with the VisionEval code available from its Github repository.

The reason that a modified module must be rebuilt before it is available for use in VisionEval is that the VisionEval framework relies on importing each module from an R package. The data resources in any R package cannot be updated directly. Instead the R package that contain the modified module must be rebuilt from its source. R packages are a fundamental unit of the R ecosystem and create a structured way to bundle code, data, and documentation together into a single unit for distribution. Packages can be easily installed and shared among R users. VisionEval itself is distributed as a set of R packages, and those packages are what must be rebuilt to incorporate any localized data or other user made modifications.

There are two ways to set up VisionEval to rebuild a package with localized data.

1. The most general approach is to start with the source code located in the VisionEval GitHub repository and to use the build system included with that code. Using that integrated build process will ensure that all the parts of VisionEval are consistent. This steps for rebuilding packages are documented in the “build/Building.md” file in the Github source tree.
• Install Git for Windows or equivalent
(optional: makes it easier to access the repositories)
• Install R
(usually the most recent version works; must be 3.6.0 or later, currently recommend 4.1.3)
• Install RTools 4.0
• Install RStudio
(a version compatible with RTools40, and with the version of R you’re using)
• Copy or clone the VisionEval (or VisionEval-Dev) repository
• Start RStudio and open the VisionEval-dev.Rproj project file (On most graphic display machines, just double-click it)
• Execute the ve.build() function:
(A full build from scratch takes from 45 minutes to an hour and a half on a typical Windows machine. A build on other architectures may take longer as many of the dependency packages will have to be built from source code.)
ve.build()
• Once the build is done, you can get into the runtime environment in several ways:
1. By executing ve.run() from the VisionEval-dev RStudio project.
2. By entering your file manager program, navigating to the runtime directory and double-clicking the VisionEval.Rproj RStudio project file down in the runtime directory (as you would for the standard binary release installer).
3. Starting the R GUI (or R terminal) for the version of R that you used to build VisionEval, setting the working directory (setwd) to the runtime folder you just built, and then source-ing Visioneval.R
• If the build is successful, you can make an installer .zip file by doing this:
ve.build("installer")
1. Alternatively, you can start with a binary VisionEval installation (unzipping the installer file) and then unzip the VisionEval source code file (“src”) to create a “src” directory within your runtime. The steps for rebuilding a package using this method are document in “build/RStudio.md” in the Github source tree.
• Pick the installer for your version of R and your Operating System
• Windows and Mac OSX are supported for binary installations
• Install the PackageSources (zip file) for the same release date, also from the VisionEval Download Page
• Just unzip the PackageSources into the exact same folder you put the VisionEval runtime
• It will create a new src sub-directory next to the other VisionEval folders
• On a Windows machine, make sure you have RTools40

### 13.2.1 Generic VisionEval Module

Using the GitHub approach with ve.build, the package source is located in VisionEval\sources\modules\VEGenericPackage and there will be no “data” folder, just the other elements. The key in both cases is to locate the inst\extdata folder and replace the key files in that location.

An exception to this is the VETravelDemandMM package, which has an offline step to construct data files (due to its dependency on non-public data).

VisionEval packages will generally have a structure similar to the following:

src/VEGenericPackage
├───data
│   ├─ GenericPackageSpecifications.rda
│   ├─ GenericPackage_df.rda
│   └─ GenericPackage_ls.rda
├───R
│   ├─ CreateEstimationDatasets.R
│   └─ GenericModel.R
└───inst
└─ extdata
├─ input_data1.csv
└─ input_data2.txt
• The root ‘src’ directory location depends on which build method was used. If built from the github source, it will reside in a built folder (e.g., GitCloneVisionEval\built\visioneval\4.x.x\src\VEGenericPackage), and if unzipped from the installer it will reside where it was unzipped (e.g., MyUnzippedVisionEval\src\VEGenericPackage)

• inst\extdata is where localized input data and reference files will be placed

• The R directory contains any R scripts used in the packages

• data contains the datastore files generated/estimated by the R scripts

• man and inst\module_docs, contain the markdown documentation generated during the build process.

Some VisionEval packages include helper scripts to facilitate formatting the input data required to build the packages, with names like CreateEstimationDatasets.R. Those should be run manually prior to rebuilding the package to ensure that the files in inst/extdata are correctly formatted.

### 13.2.2 Generic Package Building

There are several different ways to build an R package: 1. RStudio GUI 2. command line 3. devtools package

### 13.2.3 Build using Rstudio GUI

RStudio comes with a GUI for building packages. There are many tutorials and RStudio build documentations available on the internet.

1. Check current R runtime library path. It is critical to ensure that your VisionEval R project runtime environment is loaded and the current working directory is VESimHouseholds. It can be checked by entering .libPaths() in the console. If the VisionEval library is loaded, it will be listed as item 1, for example:
[1] "C:/git_clone_directory/VisionEval-Dev/built/visioneval/4.1.2/ve-lib"
[2] "C:/Program Files/R/R-4.1.2/library"
1. Check the current working directory, which should the package directory, using getwd() and can be set using:
setwd("C:/git_clone_directory/VisionEval-Dev/built/visioneval/4.1.2/src/VESimHouseholds")
1. Select Configure Build Tools from the Build menu:
2. Set the package directory to the VE module (e.g., src/VEGenericPackage) and the build source
3. Build and install the package.

### 13.2.4 Build using command line

The fundamental command to build an r package can be run from R console using system("R CMD INSTALL package_path -l lib_path"). The GUI method essentially constructs this command and executes it.

• package_path is the path to the package you are building. If your working directory is already located in the package, you can use . to denote the local directory.
• lib_path
system("R CMD INSTALL . -l C:\your_git_clone_folder\VisionEval-Dev\built\visioneval\4.1.2\ve-lib")


### 13.2.5 Build using devtools

Alternatively, the devtools provides a useful suite of tools for package building.

# This runs the scripts and creates documentation, if included
devtools::document()

# This builds the VESimHouseholds data and scripts into an R package
devtools::build()

# This then installs the VESimHouseholds package into VisionEval library
devtools::install()

## 13.3 Case Study 1 - PUMS data in VESimHouseholds

### 13.3.1 Using Local PUMS data in VESimHouseholds

The VESimHouseholds package contains a number of modules that work within the VisionEval framework to simulate households and their characteristics. The critical purpose of this package is that the data contained within the package are available throughout the VisionEval framework simply by referencing the VESimHouseholds package. The source code for this package is located on the VisionEval github repository sources/modules/VESimHouseholds.

It is recommended that package modifications be made within your local VisionEval build (i.e., after running ve.build()) located in VisionEval\built\visioneval\4.x.x\src\, and not the cloned source modules. That way any changes can be reverted to the default VisionEval version by re-running ve.build(). The “.x.x” will be replaced by whichever R version was used during the build.

### 13.3.2 Objective of the Case Study

The objective of this case study is to substitute the default Oregon-based Public Use Microdata Sample (PUMS) data with PUMS data from another state . This is done in three major steps:

1. Preprocess and format “raw” PUMS data into the comma separated value (.CSV) files for VisionEval input
2. Create the estimated dataset stored as R data files (.Rda) for VisionEval (commonly referred to as “datastore” format, see the lexicon documentation for more detailed terminologies). This element is part of building the package to generate the data for installation and may require building twice to ensure the changes take effect.
3. Re-build and install the VisionEval package to make the updated module available with local data.

### 13.3.3 Input files being modified

As mentioned, the default data inputs provided with VESimHouseholds are PUMS data for Oregon from the 2000 Decennial US Census. The input files are two .CSV files, with the following names and file paths:

• VESimHouseholds/inst/extdata/pums_households.csv
• VESimHouseholds/inst/extdata/pums_persons.csv

The two .CSV files contain disaggregated person and household data linked by a primary key id field. The files have the following fields and field values.

HOUSEHOLDS
----------
SERIALNO: Housing/Group Quarters Unit Serial Number
PUMA5: 5% Public Use Microdata Area code
HWEIGHT: Housing unit weight
UNITTYPE: Type of housing unit
0 = Housing unit
1 = Institutional group quarters
2 = Noninstitutional group quarters
PERSONS: Number of persons living in housing unit
BLDGSZ: Size of Building
blank = group quarters
1 = mobile home
2 = detached one-family house
3 = attached one-family house
4 = building with 2 apartments
5 = building with 3 or 4 apartments
6 = building with 5 to 9 apartments
7 = building with 10 to 19 apartments
8 = building with 20 to 49 apartments
9 = building with 50 or more apartments
10 = boat, RV, van, etc.
HINC: Household Total Income in 1999
PERSONS
-------
SERIALNO: Housing/Group Quarters Unit Serial Number
AGE: Age
WRKLYR: Worked in 1999
0 = Not in universe (Under 16 years)
1 = Yes
2 = No
INCTOT: Person's Total Income in 1999
NA = Not in universe (Under 15 years)
?019998 = Loss of $19,998 or more ?000001..019997 = Loss of$1 to $19,997 0000000 = No/none 0000001 =$1 or break even
0000002..4999999 = $2 to$4,999,999
5000000 = $5,000,000 or more The data are disaggregated records of individual persons and households. For example, household data might look like this: SERIALNO PUMA5 HWEIGHT UNITTYPE PERSONS BLDGSZ HINC 45 25080 22 0 1 9 68100 92 25060 17 0 3 5 105530 103 25090 21 0 2 6 359000 142 25100 25 0 2 2 141500 157 25100 0 1 1 NA 0 159 25070 22 0 1 2 14700 And an example of the persons data might look like this: SERIALNO AGE WRKLYR MILITARY INCTOT 45 66 1 4 68100 92 28 1 4 33000 92 28 1 4 37000 92 26 1 4 35530 103 53 1 4 33000 103 53 1 4 326000 142 49 1 4 111500 142 49 1 4 30000 157 39 1 4 2100 159 80 2 2 14700 Note that the two data files are also linked by a primary key field SERIALNO. For each unique household, there are one or more persons linked to that household by the SERIALNO key. ### 13.3.4 Step 1 - Preprocessing To start, we must download our new “raw” PUMS data and format it to match the current VisionEval input files. Processing and formatting can be done manually using spreadsheets and text editors, but some PUMS data are stored in a space saving format that most spreadsheets and humans cannot easily read. To help with this process, an R script was written with functions that both download the PUMS data and preprocess the data into .CSV files for VisionEval, located here: https://github.com/RSGInc/VEProcessPUMS NOTE: It is important to note here that VisionEval household and person fields are based on the 2000 Decennial Census PUMS. For year 2000 and earlier, PUMS data were based on the Decennial Census counted every ten years. Post-2000 PUMS are based on the American Community Survey (ACS), which is a sampling-based survey method collected continuously rather than from a full Decennial Census. Statistically, the ACS-based PUMS are reliable, but the fields differ and will need to be migrated to match the inputs used by VisionEval. More PUMA history can be read online at the US Census website. ### 13.3.5 2000 PUMS (aka Census PUMS) 2000 PUMS data comes in two forms, 1% and 5%, which the Census explains as: “The 1-percent super-PUMAs were used to present 1-percent PUMS files, were required to contain a minimum population of 400,000 persons, and had to nest within states. These super-PUMAs were a new geographic entity for Census 2000 and were aggregations of the smaller, 5-percent PUMAs. The 5-percent PUMAs were used to present the 5-percent PUMS files, were required to contain a minimum population of 100,000 persons, and had to nest within states. PUMAs could consist of a single county or an aggregation of one or more counties, census tracts, or minor civil divisions (MCDs) in the New England states. Additionally, an incorporated place with a minimum population of 100,000 persons could be defined as a PUMA.” Basically the 1% PUMS have more persons, but cover a larger geographic area than the 5% PUMS. In this case we are looking to download the revised 5% data. Within each state directory (e.g., https://www2.census.gov/census_2000/datasets/PUMS/FivePercent/California/) there will be several files. PUMEQ5-CA.TXT 30-Aug-2003 05:21 1.0M PUMS5_06.TXT 30-Aug-2003 05:35 677M REVISEDPUMS5_06.TXT 26-Oct-2010 14:24 676M all_California.zip 02-Sep-2003 23:08 98M The file named REVISEDPUMS5_06.TXT is the file to download. However, the data for both households and persons are stored in a continuous string, which is not an easy to parse delimited format (e.g., comma separated). Extracting the data and converting it into a tabular data frame is non-trivial. To help with this process, an R function called process_2000_pums() was written in the VEProcessPUMS package to read the .txt files, extract the data columns need, convert to R data frames, and then export the remaining data into the two person and household .csv files as VESimHousehold input. ### 13.3.6 Post-2000 PUMS (aka ACS PUMS) Post-2000 PUMS data are typically stored as separate .CSV files for persons and households. This makes parsing and importing the data into R very easy. However, the columns are different and the new column names will need to be identified and migrated over. Below is a column crosswalk to translate ACS PUMS to the required field names. #### Household 2000 PUMS Field ACS PUMS Field Name SERIALNO SERIALNO PUMA5 PUMA HWEIGHT WGTP UNITTYPE TYPE PERSONS NP BLDGSZ BLD HINC HINCP #### Persons 2000 PUMS Field ACS PUMS Field Name SERIALNO SERIALNO AGE AGEP WRKLYR WKL MILITARY MIL INCTOT PINCP ### 13.3.7 Preprocessing for VisionEval input data You can either manually download the text file and process the PUMS data, or use the automated R wrappers getACSPUMS() and getDecPUMS(). #### Manually download PUMS and process using process_2000_pums() & process_acs_pums() An example PUMS processing using the process_2000_pums and process_acs_pums: # For the 2000 data: # process_2000_pums() function reads in REVISEDPUMS5_06.TXT, # parses the data into two dataframes for persons and households, then # returns a list('p'= person_df, 'h' = household_df) # It has two input parameters: # - PumsFile: path to PUMS TXT data # - GetPumas: optional vector of PUMAS ids, defaults to 'ALL' PUMS_DATA_LIST <- process_2000_pums(PumsFile='REVISEDPUMS5_06.TXT', GetPumas='ALL') The process is similar using the process_acs_pums(). You can specify the path for the extracted .csv or the .zip file. It has one additional parameter to specify the file type as either ‘p’ for persons or ‘h’ for households. For example: # For the ACS data: PUMS_DATA_LIST <- process_acs_pums(PumsFile='csv_pca.csv', type='p' GetPumas='ALL') Then you only need to save the data frames to a csv: # Using base R write.csv write.csv(PUMS_DATA_LIST[['p']], file = file.path(output_dir, 'pums_persons.csv'), row.names = FALSE) # Using fwrite() from data.table fwrite(PUMS_DATA_LIST[['p']], file = file.path(output_dir, 'pums_persons.csv')) #### Automatically download PUMS and process using getDecPUMS and getACSPUMS To streamline the whole process, there is also a download wrapper function which both downloads and calls the pre-processing function. The user only needs to specify the State (and year if using ACS) that they want PUMS data from. For example: # This function downloads the PUMS txt data to a temporary file, # parses it using the process_2000_pums function, then # saves or returns the processed data. # It has two input parameters: # - STATE: This can be the State name, abbreviation, or fips code # (e.g., California, CA, or 6) # - output_dir: Optional path to save the files pums_persons.csv and pums_households.csv # NA path returns the dataframes in a list # Get 2000 Decennial Census PUMS # data_list <- getDecPUMS(STATE='CA', output_dir = NA) getDecPUMS(STATE='CA', output_dir = './output_folder') Similarly, ACS PUMS data for more recent years can be downloaded. For example: # Get ACS PUMS for specified year # data_list <- getACSPUMS(STATE='CA', YEAR='2020', output_dir = NA) getACSPUMS(STATE='CA', YEAR='2020', output_dir = './output_folder') ### 13.3.8 Step 2 - Create the VisionEval estimation data sets The next step is to use the R scripts in VESimHouseholds to re-generate the VisionEval .Rda data files stored in VESimHouseholds\data. These data files are stored in a structure and format (e.g., data frames) that VisionEval can use as part of the framework. 1. Start by instantiating the VisionEval R project environment by loading your model build’s VisionEval.Rproj. If you have already built your VisionEval using ve.build(), you will have a VisionEval.Rproj file in your built directory. For example, VisionEval\built\visioneval\4.1.2\runtime\VisionEval.Rproj. 2. Next navigate to the VESimHousehold package and set your current working directory to the your_path_to_this/VESimHouseholds folder. (hint Session -> Set Working Directory) 3. Run the R data generation scripts. Depending on the purpose of the module you are working with, the generation R script may vary. For example, the VESimHouseholds/R/CreateEstimationDatasets.R script reads in the .csv input data we created and creates the household data Hh_df.rda for VisionEval in the data folder. If another datastore is being modified, the respective generation script will need to be run. Within the package’s “R” folder, some will save a datastore with the command visioneval::savePackageDataset(), some may not need any data updates, and others might only contain model functions with no immediate output saved. ### 13.3.9 Step 3 - Build the VESimHouseholds package Although the previous step generated the R data files, your current VisionEval environment will still be using the old data from the built version of the package. You will need to re-build and re-install the VESimHousehold package for VisionEval to replace the package with a new version containing the new input data. Any of the three build processes described above can be used. Once the package re-build is complete, your new PUMS data will be ready to use in a VisionEval model run. ## 13.4 Case Study 2 - Powertrain data in VEPowertrainsandFuels ### 13.4.1 VEPowertrainsandFuels - Changing Inputs for a Scenario Test This second case study explores a common challenge when certain inputs are embedded in a package and users want to modify the model values within the package (often this is in order to create an alternative scenario). The critical purpose of the VEPowertrainsandFuels package is to process vehicle and fuel characteristics files that model users may optionally supply. Users can develop custom scenarios by modifying the relevant input files included in the ‘inst/extdata’ directory of the source package and build the package. ### 13.4.2 Objective of VEPowertrainsandFuels Case Study The objective of this case study is to define a custom function which alters the default data values as they are read into VisionEval during the build process. ### 13.4.3 Data VEPowertrainsAndFuels\inst\extdata\hh_powertrain_prop.csv are the default powertrain proportions contained in the package, which resembles the table below (the table is compressed to select years for clarity). The file’s purpose is to provide the proportion of vehicle powertrain types by vehicle type (auto and light trucks) and vehicle vintage year. ModelYear AutoPropIcev AutoPropHev AutoPropPhev AutoPropBev LtTrkPropIcev LtTrkPropHev LtTrkPropPhev LtTrkPropBev 1975 1 0 0 0 1 0 0 0 2000 1 0 0 0 1 0 0 0 2010 0.8786 0.1213 0 0.0001 0.9820 0.0180 0 0 2020 0.8212 0.0788 0.0202 0.0798 0.9524 0.0143 0.0067 0.0266 2030 0.6676 0.0908 0.0358 0.2058 0.9093 0.0179 0.0106 0.0622 2040 0.5701 0.0922 0.0403 0.2974 0.8996 0.0191 0.0114 0.0698 2050 0.5198 0.0895 0.0407 0.3500 0.8916 0.0193 0.0119 0.0772 The table contains two powertrain proportions, the left-most four columns are for automobiles (i.e., AutoProp) and the right-most are for light trucks (i.e., LtTrkProp). Each will sum up to 1 (for a rowsum of 2). ### 13.4.4 Input modification This section walks users through modifying the data as they are read in to VisionEval. Before modifying the code, a brief analysis is conducted to define a modifying function and demonstrate the effects if the modifications. The next subsection utilizes the function in the LoadDefaultValues.R script to modify hh_powertrain_prop.csv as the data are read in. Note: The same results of this case study can be achieved by simply modifying the hh_powertrain_prop.csv input file, similar to as done in Case Study 1, but in this demonstration purposes we will modify the generation script rather than the input data. ### 13.4.5 Analysis To begin, the code snippets below conduct a brief exploratory analysis to demonstrate visually what the data look like and how they will be modified. Using some helper packages ggplot2 and reshape we can format the data for visualization. Note: These packages are not necessary for modifying the data in VisionEval, but are used for formatting and plotting in this case study. # Additional packages library(ggplot2) library(reshape) # Load data, uncomment it to run in your project hh_powertrain_prop <- read.csv('.YOUR_PACKAGE_DIRECTORY/inst/extdata/hh_powertrain_prop.csv') melted_powertrains <- reshape::melt(hh_powertrain_prop, id.vars = 'ModelYear') ggplot(data = melted_powertrains) + geom_line(aes(x=ModelYear, y=value, color=variable)) + scale_y_continuous(labels=scales::percent) + theme_classic() We can see that battery electric vehicles (BEV), specifically automobiles, are projected to make up the majority of vehicles bought in future years. This causes the share of internal combustion engine to decline proportionally. Let us assume that the state government is deciding whether to aggressively promote BEV cars starting in 2025. The policies cause the share of alternative powertrains (BEV, HEV, and PHEV) to increase more over time. To model this increase, we will use an arbitrary function which adds to the current value of $$x$$ (i.e., the proportion) at a quadratic rate. $f(x) = x + x^2 (1 - x)$ We then create a wrapper function that adjusts each of the alternative powertrains using our custom modeling function. To ensure that the proportions sum up to 1 for autos and light trucks, respectively, we then calculate the remaining proportion of ICE powertrains by subtracting the proportion of alternative powertrains. # Wrapper function to adjust the powertrain data adjust_powertrain <- function(powertrain_df, start_year) { # Define our adjustment functions adj_fun <- function(xv) xv + (xv^2) * (1 - xv) # Which columns to modify cols <- colnames(powertrain_df) ev_cols <- cols[grepl('Hev|Phev|Bev', cols)] ice_cols <- cols[grepl('Icev', cols)] # Create the modified data frame df_mod <- powertrain_df yrs_idx <- df_mod$ModelYear > start_year

# Adjust the alternate powertrain data (BEV, PHEV, HEV) data
df_mod[yrs_idx, ev_cols] <- apply(powertrain_df[yrs_idx, ev_cols], 2, adj_fun)

# Subtract the amount of remaining internal combustion engines (ICE)
for(x in ice_cols){
sum_cols <- ev_cols[grepl(gsub('Icev', '', x), ev_cols)]
df_mod[yrs_idx, eval(x)] <- 1 - rowSums(df_mod[yrs_idx, sum_cols])
}

return(df_mod)
}


The adjusted inputs can be combined with the existing data. The combined plot shows an noticeable increase in alternative powertrain vehicle shares for later vintage years.

hh_powertrain_prop_high <- adjust_powertrain(hh_powertrain_prop, 2025)

melted_powertrains <- rbind(
cbind(reshape::melt(hh_powertrain_prop, id.vars = 'ModelYear'), type='existing'),
cbind(reshape::melt(hh_powertrain_prop_high, id.vars = 'ModelYear'), type='high')
)

ggplot(data = melted_powertrains) +
geom_line(aes(x=ModelYear, y=value, color=variable, linetype=type)) +
scale_y_continuous(labels=scales::percent) +
theme_classic()


### 13.4.6 Data modification

Once a model user is satisfied with the effects that are represented by applying the modifying function, the LoadDefaultValues.R can be edited to include the function. Beginning on line #422, the R script initializes the household powertrain datastore input, then loads in the CSV file using processEstimationInputs. We can insert the new function after the data has been loaded but before it has been stored and cleaned up at PowertrainFuelDefaults_ls$HhPowertrain_df <- HhPowertrain_df. #---------------------------------------- #Household vehicle powertrain proportions #---------------------------------------- #Specify input file attributes Inp_ls <- items( item( NAME = "ModelYear", TYPE = "integer", PROHIBIT = c("NA", "< 0"), ISELEMENTOF = "", UNLIKELY = "", TOTAL = "" ), item( NAME = items("AutoPropIcev", "AutoPropHev", "AutoPropPhev", "AutoPropBev", "LtTrkPropIcev", "LtTrkPropHev", "LtTrkPropPhev", "LtTrkPropBev"), TYPE = "double", PROHIBIT = c("NA", "< 0", "> 1"), ISELEMENTOF = "", UNLIKELY = "", TOTAL = "" ) ) #Load and process data HhPowertrain_df <- processEstimationInputs( Inp_ls, "hh_powertrain_prop.csv", "LoadDefaultValues.R") #Check whether all years are present Years_ <- 1975:2050 if (!all(Years_ %in% HhPowertrain_df$ModelYear)) {
stop(paste(
"File 'hh_powertrain_prop.csv' must have values for at least the years",
"from 1975 through 2050", sep = " "))
}
#Check that powertrain proportion are 0 when powertrain characteristics are NA
Msg_ <- character(0)
for (ty in c("Auto", "LtTrk")) {
for (pt in c("Icev", "Hev", "Phev", "Bev")) {
PtType <- paste0(ty, pt)
PropName <- paste0(ty, "Prop", pt)
CharName <- paste0(ty, pt, "Mpg")
if (pt == "Bev") CharName <- paste0(ty, pt, "Mpkwh")
Prop_ <- HhPowertrain_df[[PropName]]
Char_ <- PowertrainFuelDefaults_ls$LdvPowertrainCharacteristics_df[[CharName]] if (any(Prop_[is.na(Char_)] != 0)) { Msg <- paste0( "hh_powertrain_prop.csv file error. Non-zero proportion(s) for ", PropName, " where NA values in ldv_powertrain_characteristics.csv for ", CharName) Msg_ <- c(Msg_, Msg) } } } if (length(Msg_) != 0) { stop(paste(Msg_, collapse = ", ")) } #>>>>>>>>> NEW FUNCTION BEGINS adjust_powertrain <- function(powertrain_df, start_year) { # Define our adjustment functions adj_fun <- function(xv) xv + (xv^2) * (1 - xv) # Which columns to modify cols <- colnames(powertrain_df) ev_cols <- cols[grepl('Hev|Phev|Bev', cols)] ice_cols <- cols[grepl('Icev', cols)] # Create the modified data frame df_mod <- powertrain_df yrs_idx <- df_mod$ModelYear > start_year

# Adjust the alternate powertrain data (BEV, PHEV, HEV) data
df_mod[yrs_idx, ev_cols] <- apply(powertrain_df[yrs_idx, ev_cols], 2, adj_fun)

# Subtract the amount of remaining internal combustion engines (ICE)
for(x in ice_cols){
sum_cols <- ev_cols[grepl(gsub('Icev', '', x), ev_cols)]
df_mod[yrs_idx, eval(x)] <- 1 - rowSums(df_mod[yrs_idx, sum_cols])
}

return(df_mod)
}

#<<<<<<<<< END OF NEW FUNCTION

#Add to PowertrainFuelDefaults_ls and clean up
PowertrainFuelDefaults_ls\$HhPowertrain_df <- HhPowertrain_df
rm(Inp_ls, HhPowertrain_df)`

### 13.4.7 Build the VEPowertrainsAndFuels package

The previous step generated the R data files, and you will need to re-build and re-install the VEPowertrainsAndFuels package for VisionEval to use this new input data. Any of the three build processes described above can be used.

Once the package re-build is complete, your new powertrain data will be ready to use in a VisionEval model run.