Skip to content


Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
This branch is 693 commits behind ropensci:master.

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

Build Status CRAN_Status_Badge rstudio mirror downloads

stplanr is a package for sustainable transport planning with R.

It provides functions for solving common problems in transport planning and modelling, such as how to best get from point A to point B. The overall aim is to provide a reproducible, transparent and accessible toolkit to help people better understand transport systems and inform policy.

The initial work on the project was funded by the Department of Transport (DfT) as part of the development of the Propensity to Cycle Tool (PCT). The PCT uses origin-destination data as the basis of spatial analysis and modelling work to identify where bicycle paths are most needed. See the package vignette (e.g. via vignette("introducing-stplanr")) or an academic paper on the Propensity to Cycle Tool (PCT) for more information on how it can be used. This README gives some basics.

stplanr should be useful to researchers everywhere. The function route_graphhopper(), for example, works anywhere in the world using the graphhopper routing API and read_table_builder() reads-in Australian data. We welcome contributions that make transport research easier worldwide.

Key functions

Data frames representing flows between origins and destinations must be combined with geo-referenced zones or points to generate meaningful analyses and visualisations of 'flows' or origin-destination (OD) data. stplanr facilitates this with od2line(), which takes flow and geographical data as inputs and outputs spatial data. Some example data is provided in the package:

data(cents, flow)

Let's take a look at this data:

flow[1:3, 1:3] # typical form of flow data
#>        Area.of.residence Area.of.workplace All
#> 920573         E02002361         E02002361 109
#> 920575         E02002361         E02002363  38
#> 920578         E02002361         E02002367  10
cents[1:3,] # points representing origins and destinations
#> class       : SpatialPointsDataFrame 
#> features    : 3 
#> extent      : -1.546463, -1.511861, 53.8041, 53.81161  (xmin, xmax, ymin, ymax)
#> coord. ref. : +init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
#> variables   : 4
#> names       :  geo_code,  MSOA11NM, percent_fem,  avslope 
#> min values  : E02002382, Leeds 053,    0.408759, 2.284782 
#> max values  : E02002393, Leeds 064,    0.458721, 2.856563

These datasets can be combined as follows:

travel_network <- od2line(flow = flow, zones = cents)
w <- flow$All / max(flow$All) *10
plot(travel_network, lwd = w)

The package can also allocate flows to the road network, e.g. with and the OpenStreetMap Routing Machine (OSRM) API interfaces. These are supported in route_*() functions such as route_cyclestreets and route_osrm():

Route functions take lat/lon inputs:

trip <-
  route_osrm(from = c(-1, 53), to = c(-1.1, 53))

and place names, found using the Google Map API:

We can replicate this call multiple times using line2route.

intrazone <- travel_network$Area.of.residence == travel_network$Area.of.workplace
travel_network <- travel_network[!intrazone,]
t_routes <- line2route(travel_network, route_fun = route_osrm)

Another way to visualise this is with the leaflet package:

leaflet() %>% addTiles() %>% addPolylines(data = t_routes)

For more examples, example("line2route").

overline is a function which takes a series of route-allocated lines, splits them into unique segments and aggregates the values of overlapping lines. This can represent where there will be most traffic on the transport system, as illustrated below.

t_routes$All <- travel_network$All
rnet <- overline(t_routes, attrib = "All", fun = sum)

lwd <- rnet$All / mean(rnet$All)
plot(rnet, lwd = lwd)


To install the stable version, use:


The development version can be installed using devtools:

# install.packages("devtools") # if not already installed

stplanr depends on rgdal, which can be tricky to install.

Installing rgdal on Ubuntu and Mac

On Ubuntu rgdal can be installed with:

sudo apt-get install r-cran-rgdal

Using apt-get ensures the system dependencies, such as gdal are also installed.

On Mac, homebrew can install gdal. Full instructions are provided here.

Funtions, help and contributing

The current list of available functions can be seen with:

lsf.str("package:stplanr", all = TRUE)

To get internal help on a specific function, use the standard way.



stplanr imports many great packages that it depends on. Many thanks to the developers of these tools:

desc = read.dcf("DESCRIPTION")
headings = dimnames(desc)[[2]]
fields = which(headings %in% c("Depends", "Imports", "Suggests"))
pkgs = paste(desc[fields], collapse = ", ")
pkgs = gsub("\n", " ", pkgs)
strsplit(pkgs, ",")[[1]]
#>  [1] "sp"                " R (>= 3.0)"       " curl"            
#>  [4] " readr"            " dplyr"            " httr"            
#>  [7] " jsonlite"         " stringi"          " stringr"         
#> [10] " lubridate"        " maptools"         " raster"          
#> [13] " rgdal"            " rgeos"            " openxlsx"        
#> [16] " methods"          " R.utils"          " geosphere"       
#> [19] " Rcpp (>= 0.12.1)" " igraph"           " nabor"           
#> [22] " rlang"            " sf"               " testthat"        
#> [25] " knitr"            " rmarkdown"        " dodgr"


  • Please report issues, feature requests and questions to the github issue tracker
  • License: MIT
  • Get citation information for stplanr in R doing citation(package = 'stplanr')
  • This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.


R package providing functions and data access for transport research







No packages published


  • R 96.0%
  • C++ 3.9%
  • Makefile 0.1%