various fixes to appease CRAN

.Rbuildignore

@@ -1,2 +1,43 @@
+^pkgdown$
 ^.*\.Rproj$
 ^\.Rproj\.user$
+^\.travis\.yml$
+^.Rprofile$
+^inst/db$
+^inst/bench$
+^man-roxygen$
+^demo/pandas$
+^\.httr-oauth$
+^cran-comments\.md$
+^README\.Rmd$
+^revdep$
+^README-.*\.png$
+^codecov\.yml$
+^appveyor\.yml$
+^Doxyfile$
+^clion-test\.R$
+^API$
+^ISSUE_TEMPLATE\.md$
+^data-raw$
+^LICENSE\.md$
+^BROWSE$
+^GPATH$
+^GRTAGS$
+^GTAGS$
+^TAGS$
+^\.dir-locals\.el$
+^vignettes/rsconnect$
+^docs$
+^_pkgdown\.yml$
+^issues$
+^CONDUCT\.md$
+^archive$
+^\.drake$
+^CRAN-RELEASE$
+^\.github$
+^\.github/workflows/R-CMD-check\.yaml$
+^\.github/workflows/pr-commands\.yaml$
+^\.github/workflows/pkgdown\.yaml$
+^doc$
+^Meta$
+^CRAN-SUBMISSION$

.gitignore

@@ -2,4 +2,5 @@
 .Rhistory
 .RData
 .Ruserdata
+tpm.Rcheck/
 test_data/*

DESCRIPTION

@@ -1,18 +1,18 @@
 Package: tpm
 Title: FHWA TPM Score Calculation Functions
-Version: 1.2.0
+Version: 2.0.1
 Authors@R: 
     person(given = "Mark",
            family = "Egge",
            role = c("aut", "cre"),
-           email = "egge@highstreetconsulting.com",
-           comment = c(ORCID = "YOUR-ORCID-ID"))
-Description: Contains functions for calculating TPM PM3 Level of Travel Time Reliability and Truck Travel Time Reliability metric scores from NPMRDS travel time data and for calculating statewide reliability performance measures.
+           email = "mark@eateggs.com",
+           comment = c(ORCID = "0009-0007-9128-2099"))
+Description: Contains functions for calculating the Federal Highway Administration (FHWA) Transportation Performance Management (TPM) performance measures. Currently, the package provides methods for the System Reliablity and Freight (PM3) performance measures calculated from travel time data provided by The National Performance Management Research Data Set (NPMRDS), including Level of Travel Time Reliability (LOTTR), Truck Travel Time Reliability (TTTR), and Peak Hour Excessive Delay (PHED) metric scores for calculating statewide reliability performance measures.
 License: Mozilla Public License Version 2.0
 Encoding: UTF-8
 URL: https://github.com/markegge/fhwa_pm3/
 BugReports: https://github.com/markegge/fhwa_pm3/issues
-LazyData: false
+LazyData: true
 Depends:
     R (>= 3.5.0)
 Imports:

R/data.R

@@ -0,0 +1,13 @@
+#' FIPS Codes
+#'
+#' This dataset provides a crosswalk between state names, postal codes, and
+#'  FIPS codes. Used by the hpms() function. The variables are as follows:
+#'
+#' @format A data frame with 56rows and 4 variables:
+#' \describe{
+#'   \item{State_Name}{state name (e.g. 'Alabama')}
+#'   \item{Postal_Code}{two character state postal code (e.g. 'AL')}
+#'   \item{FIPS_Code}{Census Bureau FIPS Code (e.g. 1)}
+#'   \item{STATE_NAME}{capitalized state name (e.g. 'ALAMABA')}
+#' }
+"fips_lookup"

R/functions.R

@@ -1,3 +1,5 @@
+utils::globalVariables(".")
+utils::globalVariables(c("errorMessage")) 
 #' Calculate LOTTR Metric Score
 #'
 #' Calculate LOTTR given a RITIS NPMRDS export of travel time data.
@@ -19,6 +21,10 @@
 #' 
 #' @export
 lottr <- function(travel_time_readings = NULL, monthly = FALSE, verbose = FALSE) {
+  # bind variables to an object to suppress R CMD check warnings
+  max_lottr <- score_weekday_am <- score_weekday_mid <- score_weekday_pm <- score_weekend <- NULL
+  weekday_am <- weekday_mid <- weekday_pm <- weekend <- reliable <- NULL
+
   scores <- score(travel_time_readings, metric = "LOTTR", monthly, verbose)
 
   if(verbose == TRUE) {
@@ -53,6 +59,10 @@ lottr <- function(travel_time_readings = NULL, monthly = FALSE, verbose = FALSE)
 #' 
 #' @export
 tttr <- function(travel_time_readings = NULL, monthly = FALSE, verbose = FALSE) {
+  # bind variables to an object to suppress R CMD check warnings
+  max_tttr <- score_weekday_am <- score_weekday_mid <- score_weekday_pm <- score_overnight <- score_weekend <- NULL
+  weekday_am <- weekday_mid <- weekday_pm <- weekend <- overnight <- NULL  
+
   scores <- score(travel_time_readings, metric = "TTTR", monthly, verbose)
 
   if(verbose == TRUE) {
@@ -92,6 +102,10 @@ tttr <- function(travel_time_readings = NULL, monthly = FALSE, verbose = FALSE)
 #' }
 
 score <- function(input_file = NULL, metric, monthly = FALSE, verbose = FALSE) {
+  # bind variables to an object to suppress R CMD check warnings
+  measurement_tstamp <- period <- dow <- nhpp_period <- hod <- travel_time_seconds <- NULL
+  numerator <- denominator <- NULL
+
   if (!is.null(input_file)) {
     DT <- fread(input_file)
   } else {
@@ -269,6 +283,11 @@ phed <- function(travel_time_readings, tmc_identification,
                  dow_factor = dow_factor_default,
                  hod_profile = hod_profile_default,
                  population = NA) {
+  # bind variables to an object to suppress R CMD check warnings
+  day <- faciltype <- nhs <- road_class <- f_system <- aadt_cars <- aadt <- NULL
+  aadt_singl <- aadt_combi <- aadp <- nhs_pct <- hod_factor <- speed_limit <- NULL
+  threshold_speed <- threshold_travel_time <- miles <- measurement_tstamp <- NULL
+  tmc <- delay_seconds <- travel_time_seconds <- delay_person_hours <- delay <- NULL
 
   if(as.integer(pm_peak) == 3)  {
     hours <- c(6, 7, 8, 9, 15, 16, 17, 18) # 3 - 7 pm
@@ -380,6 +399,11 @@ phed <- function(travel_time_readings, tmc_identification,
   }
 
   setnames(travel_time, "tmc_code", "tmc")
+
+  missing_tmcs <- tmcs$tmc[!tmcs$tmc %in% travel_time$tmc]
+  if(length(missing_tmcs) > 0) {
+    warning(paste("Warning: travel time data missing for ", missing_tmcs, collapse = ","))
+  }
 
   # join in threshold travel times with TMCs
   travel_time <- merge(travel_time, tmcs[, .(tmc, road_class, aadp, threshold_travel_time)], by = "tmc")
@@ -437,6 +461,15 @@ phed <- function(travel_time_readings, tmc_identification,
 #' 
 #' @export
 hpms <- function(tmc_identification, lottr_scores, tttr_scores, phed_scores = NULL, occ_fac = 1.7) {
+  # bind variables to an object to suppress R CMD check warnings
+  Postal_Code <- isprimary <- nhs <- tmc <- f_system <- urban_code <- NULL
+  faciltype <- miles <- nhs_pct <- direction <- aadt <- delay <- PHED <- NULL
+  tmc_code <- score_weekday_am <- denominator_weekday_am <- numerator_weekday_am <- NULL
+  score_weekday_mid <- denominator_weekday_mid <- numerator_weekday_mid <- NULL
+  score_weekday_pm <- denominator_weekday_pm <- numerator_weekday_pm <- NULL
+  score_weekend <- denominator_weekend <- numerator_weekend <- NULL
+  score_overnight <- denominator_overnight <- numerator_overnight <- NULL
+
   DT <- fread(tmc_identification)
 
   yr <- first(year(DT$active_start_date))
@@ -448,7 +481,7 @@ hpms <- function(tmc_identification, lottr_scores, tttr_scores, phed_scores = NU
 
   state <- unique(toupper(DT$state))
   stopifnot(length(state) == 1)
-  state_fips <- tpm:::fips_lookup[Postal_Code == state]$FIPS_Code
+  state_fips <- tpm::fips_lookup[Postal_Code == state]$FIPS_Code
 
   # Set NHS Value appropriately - no zeros allowed by FHWA!
   DT[isprimary == "0", nhs := -1]

R/tpm.R

@@ -16,7 +16,6 @@
 #' \code{\link{hpms}} generates an HPMS submission file in pipe delimited format
 #' 
 #'
-#' @docType package
 #' @name tpm
 #' 
 #' @import data.table

README.md

@@ -80,12 +80,12 @@ To calculate LOTTR or TTTR Metric scores:
 3. Choose the appropriate "TMC segments from" value (e.g. "NPMRDS INRIX 2019")
 4. Choose your region (e.g. Wyoming) click Add
 5. Specify appropriate date range, e.g 01/01/2019 – 12/31/2019
-![Massive Data Downloader Region and Dates](man/mdd_1.png)
+![Massive Data Downloader Region and Dates](man/figures/mdd_1.png)
 6. Select data sources and measures: 
     * "NPMRDS form INRIX (Trucks and Passenger Vehicles): Travel Time" for LOTTR Measure (the other fields are optional)
     * "NPMRDS from Inrix (Trucks): Travel Time" for TTTR Measure
 9. Set averaging to 15 minutes (per PM3 Final Rule) and Submit
-![Massive Data Downloader Data Sources and Units](man/mdd_2.png)
+![Massive Data Downloader Data Sources and Units](man/figures/mdd_2.png)
 10. Download and extract the resulting dataset
 11. Calculate scores using `lottr` and `tttr`. Monthly scores may be calculated using `monthly = TRUE` 
 

tests/testthat/test-hpms.R

@@ -1,13 +1,13 @@
 test_that("hpms runs correctly", {
   expect_equal({
-      lottr_scores <- lottr("Readings.csv", verbose = TRUE)
-      tttr_scores <- tttr("Readings.csv", verbose = TRUE)
-      phed_scores <- phed(travel_time_readings = "Readings.csv",
-                          tmc_identification = "TMC_Identification.csv",
-                          speed_limits = fread("speed_limits.csv"),
+      lottr_scores <- lottr(test_path("testdata", "Readings.csv"), verbose = TRUE)
+      tttr_scores <- tttr(test_path("testdata", "Readings.csv"), verbose = TRUE)
+      phed_scores <- phed(travel_time_readings = test_path("testdata", "Readings.csv"),
+                          tmc_identification = test_path("testdata", "TMC_Identification.csv"),
+                          speed_limits = fread(test_path("testdata", "speed_limits.csv")),
                           urban_code = 56139,
                           population = 50000)
-      hpms("TMC_Identification.csv", lottr_scores, tttr_scores, phed_scores)
+      hpms(test_path("testdata", "TMC_Identification.csv"), lottr_scores, tttr_scores, phed_scores)
     },
     TRUE
   )

tests/testthat/test-lottr.R

@@ -1,6 +1,6 @@
 test_that("null file_path throws error", {
   expect_warning(score(), )
 
-  scores_monthly <- lottr("Readings.csv", monthly = TRUE)
+  scores_monthly <- lottr(test_path("testdata", "Readings.csv"), monthly = TRUE)
 
 })

tests/testthat/test-phed.R

@@ -14,9 +14,9 @@ test_that("phed runs correctly", {
       9,0.99,0.99
       12,0.97,0.97")
 
-  expect_warning(phed(travel_time_readings = "Readings.csv",
-                      tmc_identification = "TMC_Identification.csv",
-                      speed_limits = fread("speed_limits.csv"),
+  expect_warning(phed(travel_time_readings = test_path("testdata", "Readings.csv"),
+                      tmc_identification = test_path("testdata", "TMC_Identification.csv"),
+                      speed_limits = fread(test_path("testdata", "speed_limits.csv")),
                       urban_code = 56139,
                       population = 52898,
                       moy_factor = moy_factor_test))
@@ -30,9 +30,9 @@ test_that("phed runs correctly", {
       4,1.05,1.05
       5,1.1,1.1")
 
-  expect_warning(phed(travel_time_readings = "Readings.csv",
-                      tmc_identification = "TMC_Identification.csv",
-                      speed_limits = fread("speed_limits.csv"),
+  expect_warning(phed(travel_time_readings = test_path("testdata", "Readings.csv"),
+                      tmc_identification = test_path("testdata", "TMC_Identification.csv"),
+                      speed_limits = fread(test_path("testdata", "speed_limits.csv")),
                       urban_code = 56139,
                       population = 52898,
                       dow_factor = dow_factor_test))
@@ -46,9 +46,9 @@ test_that("phed runs correctly", {
       5,0.05,0.05
       6,0.1,0.1")
 
-  expect_warning(phed(travel_time_readings = "Readings.csv",
-                      tmc_identification = "TMC_Identification.csv",
-                      speed_limits = fread("speed_limits.csv"),
+  expect_warning(phed(travel_time_readings = test_path("testdata", "Readings.csv"),
+                      tmc_identification = test_path("testdata", "TMC_Identification.csv"),
+                      speed_limits = fread(test_path("testdata", "speed_limits.csv")),
                       urban_code = 56139,
                       population = 52898,
                       dow_factor = dow_factor_test))
@@ -66,20 +66,21 @@ test_that("phed runs correctly", {
       18,0.0555,0.0575
       19,0.042,0.047")
 
-  expect_warning(phed(travel_time_readings = "Readings.csv",
-                      tmc_identification = "TMC_Identification.csv",
-                      speed_limits = fread("speed_limits.csv"),
+  expect_warning(phed(travel_time_readings = test_path("testdata", "Readings.csv"),
+                      tmc_identification = test_path("testdata", "TMC_Identification.csv"),
+                      speed_limits = fread(test_path("testdata", "speed_limits.csv")),
                       urban_code = 56139,
                       population = 52898,
                       hod_profile = hod_profile_test))
 
   expect_equal({
     phed_scores <- phed(urban_code = 56139,
        population = 52898,
-       travel_time_readings = "Readings.csv",
-       tmc_identification = "TMC_Identification.csv",
-       speed_limits = fread("speed_limits.csv"))
-    round(phed_scores[, sum(delay) / 52898], 2)
+       travel_time_readings = test_path("testdata", "Readings.csv"),
+       tmc_identification = test_path("testdata", "TMC_Identification.csv"),
+       speed_limits = fread(test_path("testdata", "speed_limits.csv"))
+       )
+    round(phed_scores[, sum(delay, na.rm = TRUE) / 52898], 2)
     }, 
        0.18,
        tolerance = 1

tests/testthat/test-tttr.R

@@ -5,7 +5,7 @@ test_that("null file_path throws error", {
 
 test_that("tttr runs correctly", {
   expect_equal({
-    tttr_scores <- tttr("Readings.csv")
+    tttr_scores <- tttr(test_path("testdata", "Readings.csv"))
     mean(tttr_scores$max_tttr)
   }, 
   1.638

tests/testthat/tmc_identification.csv → tests/testthat/testdata/TMC_Identification.csv

@@ -4,8 +4,8 @@ tmc,road,direction,intersection,state,county,zip,start_latitude,start_longitude,
 000+10003,US-3,WESTBOUND,,WY,LARAMIE,82009,,,,,0.54,,America/Denver,,USA,,2,N,3,56139,2,,4,3,3,1,3,28730,495,1920,1,100,0,0,1,1,2020-01-01T05:00:00Z,2021-01-01T05:00:00Z
 000P10004,US-4,EASTBOUND,,WY,LARAMIE,82009,,,,,0.08,,America/Denver,,USA,,2,N,3,99999,2,,2,4,4,1,4,2125,45,200,1,100,0,0,1,1,2020-01-01T05:00:00Z,2021-01-01T05:00:00Z
 000-10005,US-5,WESTBOUND,,WY,LARAMIE,82009,,,,,3.45,,America/Denver,,USA,,1,N,1,99999,2,,4,5,5,1,5,28380,425,8050,1,100,0,0,1,1,2020-01-01T05:00:00Z,2021-01-01T05:00:00Z
-000P10006,US-6,WESTBOUND,,WY,LARAMIE,82009,,,,,0.56,,America/Denver,,USA,,2,N,2,56139,2,,6,6,6,1,6,72120,705,1650,1,100,0,0,1,1,2020-01-01T05:00:00Z,2021-01-01T05:00:00Z
-000P10007,US-7,EASTBOUND,,WY,LARAMIE,82009,,,,,1,,America/Denver,,USA,,4,N,3,56139,2,,4,7,7,1,7,18800,325,210,1,100,0,0,0,1,2020-01-01T05:00:00Z,2021-01-01T05:00:00Z
+000P10006,US-6,WESTBOUND,,WY,LARAMIE,82009,,,,,0.56,,America/Denver,,USA,,2,N,2,56139,2,,6,6,6,1,6,12120,405,1250,1,100,0,0,1,1,2020-01-01T05:00:00Z,2021-01-01T05:00:00Z
+000+10007,US-6,WESTBOUND,,WY,LARAMIE,82009,,,,,0.56,,America/Denver,,USA,,2,N,2,56139,2,,6,6,6,1,6,72120,705,1650,1,100,0,0,1,1,2020-01-01T05:00:00Z,2021-01-01T05:00:00Z
 000+10008,US-8,EASTBOUND,,WY,LARAMIE,82009,,,,,1.96,,America/Denver,,USA,,3,N,3,56139,2,,2,8,8,1,8,1670,135,130,1,100,0,0,1,1,2020-01-01T05:00:00Z,2021-01-01T05:00:00Z
-000+10009,US-9,NORTHBOUND,,WY,LARAMIE,82009,,,,,1.83,,America/Denver,,USA,,2,N,3,56139,2,,2,9,9,1,9,1645,35,90,1,100,0,0,1,1,2020-01-01T05:00:00Z,2021-01-01T05:00:00Z
+000P10009,US-10,NORTHBOUND,,WY,LARAMIE,82009,,,,,0.09,,America/Denver,,USA,,2,N,2,99999,2,,4,10,10,1,10,20605,385,4290,1,100,0,0,1,1,2020-01-01T05:00:00Z,2021-01-01T05:00:00Z
 000P10010,US-10,NORTHBOUND,,WY,LARAMIE,82009,,,,,0.09,,America/Denver,,USA,,2,N,2,99999,2,,4,10,10,1,10,30605,585,3290,1,100,0,0,1,1,2020-01-01T05:00:00Z,2021-01-01T05:00:00Z

tests/testthat/speed_limits.csv → tests/testthat/testdata/speed_limits.csv

@@ -5,7 +5,7 @@ tmc,speed_limit
 000P10004,65
 000-10005,55
 000P10006,55
-000P10007,55
+000+10007,55
 000+10008,55
 000+10009,45
 000P10010,65