• Wifi password: ullyfo
• Network: UoLConferences

• Introduction to visualising spatial data with R (printed)
• An introduction to simple features (printed)
• Point pattern analysis: vignettes/point-pattern.md

Aditional material

• Advanced visualisation: vignettes/vspd-base-shiny.md

Coffee & set-up: 09:00 - 09:15

• Introduction - R for spatial data (Handout Part I): 09:30 - 09:40
• Loading and interrogating spatial data (Part II): 09:40 - 10:00
• Manipulating spatial objects (Part III): 10:00 - 10:45

Coffee break: 10:45 - 11:00

• Attribute joins and clipping (Part III) 11:00: - 11:30
• Map making and visualisation (Part IV) 11:30: - 12:30

LUNCH and looking at your data: 12:30 - 13:30

• Wrap-up introductory tutorial: 13:30 - 14:00
• An introduction to sf (Handout 2): 14:00 - 14:45

Coffee break: 14:45 - 15:00

• Acquiring spatial data (demonstration): 15:00 - 15:15

• Point pattern analysis and models (online tutorial): 15:15 - 16:00

• Web-maps - the Propensity to Cycle Tool: 16:15

• Your data or additional tutorials: 16:15 onwards

• Extensive and rapidly expanding spatial packages
• Interface with 'conventional' GIS software
• The advantages of the command-line:

"With the advent of “modern” GIS software, most people want to point and click their way through life. That’s good, but there is a tremendous amount of flexibility and power waiting for you with the command line. Many times you can do something on the command line in a fraction of the time you can do it with a GUI (Sherman 2008, p. 283)

• R's visualisation capabilities have evolved over time
• Used to create plots in the best academic journals
• ggplot2 has revolutionised the visualisation of quantitative information in R, and (possibly) overall
• Thus there are different camps with different preferences when it comes to maps in R

If you cannot visualise your data, it is very difficult to understand your data. Conversely, visualisation will greatly aid in communicating your results.

Human beings are remarkably adept at discerning relationships from visual representations. A well-crafted graph can help you make meaningful comparisons among thousands of pieces of information, extracting patterns not easily found through other methods. … Data analysts need to look at their data, and this is one area where R shines. (Kabacoff, 2009, p. 45).

base graphics

Source: Cheshire and Lovelace (2014) - available online

ggplot2 way

Source: This tutorial!

census

Before progressing further: Any questions?

Course materials are all available online from a GitHub repository. Click "Download ZIP" to download all the test data, ready to procede.

The main document to accompany this tutorial is a pdf within the main repository. This is to be made available for free worldwide - any comments/corrections welcome.

• All course material can be found online at github.com/Robinlovelace/Creating-maps-in-R

• In there course notes for today can be found in a file called 2017-03-31-spatial data-lnd.Rmd in the course-info folder.

# get data (if you don't have it already)
u = "https://github.com/Robinlovelace/Creating-maps-in-R/archive/master.zip"
download.file(u, "master.zip")
unzip("master.zip")

• Open the .Rproj file in the resulting folder

library(rgdal)
lnd = readOGR("data/london_sport.shp")

install.packages("devtools") # if not already installed
devtools::install_github("nowosad/spData")
library(dplyr)
lnd@data %>%
  filter(grepl("s", name)) %>% 
  arrange(Partic_Per)

install.packages("rnaturalearth")
countries = rnaturalearth::countries110
plot(countries)

library(stplanr)
r = route_cyclestreet(from = "Senate House, London", to = "Leeds")
qtm(r)
rg = route_graphhopper(from = "Senate House, London", "London King's cross")
rg = route_graphhopper(from = "Senate House, London", "London King's cross")
rc = route_graphhopper(from = "Senate House, London", "London King's cross", vehicle = "car")
qtm(rg)+
  qtm(rc, lines.col = "green")