pavdata is an R package for storing, validating, and exploring transportation infrastructure data with a lightweight, human-readable .pavdata format based on JSON.

It is designed for pavement and materials workflows where researchers need to:

create structured objects for samples, binders, aggregates, mixtures, and tests
validate required fields and plausible numeric ranges
serialize data to a portable file format
reload collections into an indexed in-memory library
inspect objects interactively with familiar R methods such as print(), summary(), and plot()

Installation

Install from a local source tree:

install.packages("path/to/pavdata", repos = NULL, type = "source")

After its first release on CRAN, install it with:

install.packages("pavdata")

Why pavdata?

Laboratory and field datasets in pavement engineering are often fragmented across spreadsheets, scripts, and reports. pavdata provides a small relational layer in R so those records can be created, checked, saved, and reused more consistently.

The package focuses on four practical ideas aligned with the FAIR Principles:

explicit object types for common pavement entities
built-in validation for required fields and plausible ranges
reproducible read/write support through .pavdata files
simple tools for browsing collections during analysis

Quick Start

Create a few linked objects:

library(pavdata)
binder <- pav_new(
  "binder",
  id = "binder-cap-50-70",
  name = "CAP 50/70",
  binder_type = "CAP 50/70",
  penetration_mm = 52,
  softening_point_c = 49
)
aggregate <- pav_new(
  "aggregate",
  id = "aggregate-basalt",
  name = "Basalt aggregate",
  bulk_specific_gravity = 2.71,
  water_absorption_pct = 1.2
)
mixture <- pav_new(
  "mixture",
  id = "mixture-dense-graded",
  name = "Dense graded mix",
  binder_id = binder$id,
  aggregate_id = aggregate$id,
  binder_content_pct = 5.3
)
volumetrics <- pav_new(
  "mixture_test",
  id = "test-volumetrics-dense-graded",
  name = "Dense graded mix volumetrics",
  mixture_id = mixture$id,
  test_type = "volumetrics",
  volumetrics = list(
    air_voids_pct = 4.1,
    voids_mineral_aggregate_pct = 15.4,
    voids_filled_asphalt_pct = 73.4,
    filler_binder_ratio = 1.1
  )
)

Validate the objects:

pav_check(binder)
pav_check(mixture)
pav_check(volumetrics)

Save them to disk and read them back:

path <- tempfile(fileext = ".pavdata")
pav_write(list(binder, aggregate, mixture, volumetrics), path)
objects <- pav_read(path)
names(objects)

Load them into a library for indexed access:

lib <- pav_library()
pav_library_load(lib, path)
print(lib)
pav_list(lib, type = "mixture")
pav_view(lib, mixture$id)

Inspecting Objects

PavData objects support standard S3 methods.

`print()`

Use print() for a compact object overview:

print(binder)

<pavdata_binder> CAP 50/70
  id: binder-cap-50-70 | v1 | 13 fields

`summary()`

Use summary() to display the populated metadata and data fields:

summary(volumetrics)

MIXTURE_TEST: Dense graded mix volumetrics
ID: test-volumetrics-dense-graded | v1 | Created: <timestamp>
Source:
mixture_id                     mixture-dense-graded
test_type                      volumetrics
volumetrics
  air_voids_pct                4.1
  voids_mineral_aggregate_pct  15.4
  voids_filled_asphalt_pct     73.4
  filler_binder_ratio          1.1

`plot()`

Use plot() to compare numeric fields. Short labels can be passed to the underlying base-R bar plot with names.arg:

plot(
  volumetrics,
  names.arg = c("Air voids (%)", "VMA (%)", "VFA (%)", "Filler/binder"),
  ylim = c(0, 80)
)

Main Functions

Function	Purpose
`pav_new()`	Create a new PavData object
`pav_check()`	Validate one object
`pav_check_integrity()`	Validate a collection and its foreign keys
`pav_write()`	Write objects to a `.pavdata` file
`pav_read()`	Read objects from a `.pavdata` file
`pav_load()`	Read a file and validate all objects
`pav_library()`	Create an in-memory indexed library
`pav_library_load()`	Load a file into a library
`pav_list()`	List objects in a library
`pav_view()`	Display one object from a library

Object Types

pavdata currently supports these object families:

sample
binder
aggregate
mixture
binder_test
aggregate_test
mixture_test
reference

Each object shares common metadata such as id, name, type, version, created_at, source, and notes.

UML Data Model

The UML diagram below summarizes the S3 classes and their relationships.

Built-In Example Data

The package ships with an example .pavdata file:

path <- pavdata_sample_path()
path

You can use it to test import, browsing, and validation workflows.

Related Publications

Melo, C. D. R., Carvalho, P. H. J., Mariano, L. G., Babadopulos, L. F. A. L., Parente Junior, E., and Soares, J. B. (2025). Proposta preliminar de um repositório nacional aberto de ensaios de misturas asfálticas. In Anais do 39º Congresso de Pesquisa e Ensino em Transportes (39º ANPET) [electronic book]. Associação Nacional de Pesquisa e Ensino em Transportes. Goiânia, GO.

Authors and Affiliation

PavData is developed by:

Creator

Vilmar Faustino do Nascimento

Authors

Contributors

Jorge Barbosa Soares

Affiliation

Universidade Federal do Ceará (UFC)