The aim of R for Highways Research and Transport Planning is to teach how R can be used as a powerful tool for processing, visualising and modelling transport data.
Course contents can be found online at: https://github.com/ITSLeeds/highways-course
Leeds Institute for Transport Studies, 40 University Road, LS2 9JT. It can be seen in this web map, highlighted in red below, just opposite Woodhouse Moore (it’s a 20 minute walk from the station):
09:00-09:30 Arrival and set-up
09:30-11:00 Introduction to the course and software
- Introduction to R
- R installation questions/debugging
- How to use RStudio (practical in groups of 2)
- R classes and working with data frames (CC)
11:15-12:30 Statistics and packages
- Stats refresher: plots and descriptive statistics
- Predictive models
- Using packages: examples with the tidyverse
Lunch
13:30-15:00 Spatial data in R
- Spatial data in R
- R’s spatial ecosystem (see section 1.4 of Geocomputation with R - package ecosystem)
- Practical: Section 3.2 to 3.2.2 of handouts
- Exercises: 1 to 3 in the hand-outs
- Further reading: Chapter 7 of Geocomputation with R - data import/export and exercises
- Bonus (relies on internet/data): read-in and analyse datasets you use at work as data frames or sf objects.
15:15-16:30 Real-work example and consolidation
- Demo of roadworks data with R
- Practical: working-through sections 3.2.3 to 3.2.4 of hand-outs
- Exercises: 4 to 6 onwards
- Advanced option: Section 4.2 - Spatial operations on vector data of Geocomputation with R
Day 2 transport data
09:30-11:00 An introduction to point (Stats19) data
- Point data: Stats19
- Spatial and temporal subsetting
- Aggregation
11:15-12:30 Desire lines and routing
- Desire lines: using origin-destination data
- Routing
- Bonus: finding crash hotspots
Lunch
13:30-15:00 Road traffic data
- Traffic data introduction (Josh Manning)
- Temporal analysis
- Spatial analysis
15:15-16:30 Practical application
- Working on real datasets such as flooding or traffic data, or improving specific skills such as visualisation
If you are new to R, ensure you have attempted a basic introductory course such as DataCamp’s introduction to R free course or equivalent.
If you’re interested in R for ‘data science’ and installing/updating/choosing R packages, these additional resources are recommended (these optional resources are all freely availble online):
- The introductory chapter of R for Data Science
- Chapter 2 on setting-up R and section 4.4 on package selection in the book Efficient R Programming
- The Transport chapter of the new book Geocomputation with R
Attendees are expected to bring their own laptop with the following
packages installed and working. You can check these are all installed,
and install those that are not installed, as follows (you can also just
type install.packages("sf") etc):
install.packages("osmdata") # for working with open street map data
install.packages("sf") # a package for working with spatial data
install.packages("spData") # provides example data
install.packages("stplanr") # a transport data package
install.packages("tidyverse") # metapackage for data science
install.packages("tmap") # a mapping package
# Make sure your packages are up-to-date with:
update.packages()In addition, it would be useful to have oneminutetraffic, which can be installed with:
devtools::install_github("RACFoundation/oneminutetrafficdata")The code in the following example checks you have the necessary packages installed. It results in a map that will guide you to the location of the course.
Attach the packages:
library(sf)
library(stplanr)
library(tidyverse)The overall route assuming you’re travelling from London:
uk = spData::world %>%
filter(name_long == "United Kingdom")
origin_lnd = c(-0.1, 51.5)
destination = c(-1.55, 53.8)
odmatrix = matrix(c(origin_lnd, destination), ncol = 2, byrow = TRUE)
line_lnd = st_linestring(odmatrix) %>%
st_sfc() %>%
st_sf(crs = 4326)
plot(st_geometry(uk), col = "grey")
plot(line_lnd, add = TRUE, lwd = 5, col = "red")