In geocompr/geocompkg: Geocomputation with R Metapackage
# publish slides
cp -Rv slides/2* ~/geocompr/geocompr.github.io/static/presentations
cd ~/geocompr/geocompr.github.io
git status
git add -A
git commit -am 'Add slides'
git push
# hugo
# cd public
# git status
# git diff
# git add -A
# git commit -am 'Update site'
# git push
cd ~/geocompr/geocompkg
# setup
options(htmltools.dir.version = FALSE)
library(RefManageR)
BibOptions(check.entries = FALSE,
bib.style = "authoryear",
cite.style = 'alphabetic',
style = "markdown",
first.inits = FALSE,
hyperlink = FALSE,
dashed = FALSE)
my_bib = ReadBib("refs-geostat.bib", check = FALSE)
About the course
About us
Robin Lovelace
- Experience: 10 years using R, ~5 years programming + developing R packages
- Teaching: many workshops, co-author of Efficient R Programming and Geocomputation with R
- Application: energy and transport, e.g. cycling potential
--
Nikée Groot
- Biologist
- Researching sustainable futures
--
You?
--
Course home: https://git.io/geocompr-2-day-course
Housekeeping
- The venue + facilities
- The agenda + further info can be found at:
- https://git.io/geocompr-2-day-course
library(leaflet)
library(osmdata)
library(tmap)
lida = opq("Leeds") %>%
add_osm_feature(key = "name", value = "Worsley Building") %>%
osmdata_sf()
# m = mapview::mapview(lida$osm_polygons, zoom = 16)
lida_centroid = lida$osm_polygons %>%
sf::st_centroid()
lon = sf::st_coordinates(lida_centroid)[1]
lat = sf::st_coordinates(lida_centroid)[2]
m = tm_shape(lida$osm_polygons) +
tm_polygons(col = "red") +
tm_view(set.view = c(lon + 0.005, lat - 0.005, 15)) +
tm_basemap(server = leaflet::providers$CartoDB.Positron) +
tm_minimap(server = leaflet::providers$OpenTopoMap, zoomLevelOffset = -10) +
tm_scale_bar()
tmap_mode("view")
m
Learning objectives
By the end of the course, you will be able to:
- Be able to use R and RStudio as a powerful Geographic Information System (GIS)
- Know how R's spatial capabilities fit within the landscape of open source GIS software
- Be confident with using R's command-line interface (CLI) and scripting capabilities for geographic data processing
- Understand how to import a range of data sources into R
- Be able to perform a range of attribute operations such as subsetting and joining
- Understand how to implement a range of spatial data operations including spatial subsetting and spatial aggregation
- Have the confidence to output the results of geographic research in the form of static and interactive maps
Agenda
.pull-left[
Day 1: Foundations
09:30-11:00 Introduction to the course and software
11:15-12:30: R's spatial ecosystem
Lunch
13:30-15:00 The structure of vector geographic data in R and working with attribute data
15:15-16:30 Spatial data operations
]
--
.pull-right[
Day 2 GIS, viz, modelling
09:30-11:00 Reading and writing geographic data
11:15-12:30: Geometric operations
Lunch
13:30-15:00 Visualisation
15:15-16:30 Working on your own data
]
--
Updated challenges/examples: git.io/geocompr-2-day-course
What is geocomputation?
.pull-left[
GeoComputation is about using the various different types of geodata and about developing relevant geo-tools within the overall context of a 'scientific' approach r Citep(my_bib, "openshaw_geocomputation_2000", .opts = list(cite.style = "authoryear")).
]
.pull-right[
knitr::include_graphics("http://www.ccg.leeds.ac.uk/people/s.openshaw/s.openshaw.png")
]
--
- But we do differ from early definitions in one important way:
At the turn of the 21st Century it was unrealistic to expect readers to be able to reproduce code examples, due to barriers preventing access to the necessary hardware, software and data
Other aspects of the definition
--
What distinguishes geocomputation from the older quantitative geography, is its emphasis on "creative and experimental" GIS applications r Citep(my_bib, "longley_geocomputation_1998", .opts = list(cite.style = "authoryear")).
--
It's about doing "practical work that is beneficial or useful" r Citep(my_bib, "openshaw_geocomputation_2000", .opts = list(cite.style = "authoryear")).
--
about harnessing the power of modern computers to do things with geographic data.
Introduction to the course and software
- Source: rstudio.com
--
- Key elements: Source, Console, Environment, Plot panes
--
- Projects and short-cuts (e.g. try
Alt-Shift-J)
An introduction to R
See slides at https://geocompr.github.io/presentations/intro-to-R.html
- Work through Exercises in Section 1 of handouts
R's spatial ecosystem
-
See slides at https://geocompr.github.io/presentations/satRday-newcastle.html#19
-
Rs capabilities have evolved substantially:
--
- rgdal, released in 2003
--
- sp, released 2005
-
spverse: gstat, geosphere, adehabitat
-
raster, released 2010
Spatial ecosystem: demo and practical
- Live demo: using R as a GIS
- Spatial data in R
- R's spatial ecosystem (see section 1.4 of Geocomputation with R - package ecosystem)
- Work through Section 2 exercises in hand-out
- Follow-on practical: Work through the exercise 1:3 in Chapter 2
- Advanced: complete exercises in Chapter 2
- Bonus: reproduce the results in the
sf vignette sf1
Attribute operations
-
See slides at https://geocompr.github.io/presentations/attr.html#1
-
Work through the following sections in Geocomputation with R:
-
Vector attribute subsetting (3.2.1)
- Vector attribute aggregation (3.2.2)
- Vector attribute joining (3.2.3)
-
Creating attributes and removing spatial information (3.2.4)
-
Answer questions in Section 3 in the handouts
-
Bonus: try remaining exercises in Chapter 3
Spatial data operations
- Live demo using stats19 data in Leeds
Work through the following sections in Geocomputation with R
- Spatial subsetting (4.2.1)
- Topological relations (4.2.2)
-
Spatial joining (4.2.3)
-
Work through section 4 in handouts
-
Bonus: Non-overlapping joins (4.2.4)
- Spatial data aggregation (4.2.5)
Day 2 GIS, visualisation, modelling: recap
--
.pull-left[
Day 1: Foundations
09:30-11:00 Introduction to the course and software
11:15-12:30: R's spatial ecosystem
Lunch
13:30-15:00 The structure of vector geographic data in R and working with attribute data
15:15-16:30 Spatial data operations
]
--
.pull-right[
09:30-11:00 Reading and writing geographic data
11:15-12:30: Geometric operations
Lunch
13:30-15:00 Visualisation
15:15-16:30 Working on your own data
]
--
Updated challenges/examples: git.io/geocompr-2-day-course
Reading and writing geographic data
- See slides at: https://geocompr.github.io/presentations/read-write.html#1
Geometric operations
-
See Chapter 5 of Geocomputation with R: Geometry operations
-
Example:
library(osmdata)
cycleways = opq("leeds") %>%
add_osm_feature(key = "name", value = "Cycle Superhighway 1") %>%
osmdata_sf()
cycleway_line = cycleways$osm_lines
cycleway_buffer = cycleway_line %>%
st_transform(27700) %>%
st_buffer(500) %>%
st_transform(4326)
Geometric exercises
-
Attempt the questions in Section 6 in the hand-out
-
Work through some of the examples of Chapter 6 of Geocomputation with R
-
Identify serious road traffic collisions that happened within 500 m of the Cycle Superhighway in 2017 (see https://git.io/geocompr-2-day-course)
Visualisation
- See slides at https://geocompr.github.io/presentations/04_viz.html#1
Working on your own data
geocompr/geocompkg documentation built on July 5, 2025, 2:35 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# publish slides cp -Rv slides/2* ~/geocompr/geocompr.github.io/static/presentations cd ~/geocompr/geocompr.github.io git status git add -A git commit -am 'Add slides' git push # hugo # cd public # git status # git diff # git add -A # git commit -am 'Update site' # git push cd ~/geocompr/geocompkg
# setup options(htmltools.dir.version = FALSE) library(RefManageR) BibOptions(check.entries = FALSE, bib.style = "authoryear", cite.style = 'alphabetic', style = "markdown", first.inits = FALSE, hyperlink = FALSE, dashed = FALSE) my_bib = ReadBib("refs-geostat.bib", check = FALSE)
About the course
About us
Robin Lovelace
- Experience: 10 years using R, ~5 years programming + developing R packages
- Teaching: many workshops, co-author of Efficient R Programming and Geocomputation with R
- Application: energy and transport, e.g. cycling potential
--
Nikée Groot
- Biologist
- Researching sustainable futures
--
You?
--
Course home: https://git.io/geocompr-2-day-course
Housekeeping
- The venue + facilities
- The agenda + further info can be found at:
- https://git.io/geocompr-2-day-course
library(leaflet) library(osmdata) library(tmap) lida = opq("Leeds") %>% add_osm_feature(key = "name", value = "Worsley Building") %>% osmdata_sf() # m = mapview::mapview(lida$osm_polygons, zoom = 16) lida_centroid = lida$osm_polygons %>% sf::st_centroid() lon = sf::st_coordinates(lida_centroid)[1] lat = sf::st_coordinates(lida_centroid)[2] m = tm_shape(lida$osm_polygons) + tm_polygons(col = "red") + tm_view(set.view = c(lon + 0.005, lat - 0.005, 15)) + tm_basemap(server = leaflet::providers$CartoDB.Positron) + tm_minimap(server = leaflet::providers$OpenTopoMap, zoomLevelOffset = -10) + tm_scale_bar() tmap_mode("view")
m
Learning objectives
By the end of the course, you will be able to:
- Be able to use R and RStudio as a powerful Geographic Information System (GIS)
- Know how R's spatial capabilities fit within the landscape of open source GIS software
- Be confident with using R's command-line interface (CLI) and scripting capabilities for geographic data processing
- Understand how to import a range of data sources into R
- Be able to perform a range of attribute operations such as subsetting and joining
- Understand how to implement a range of spatial data operations including spatial subsetting and spatial aggregation
- Have the confidence to output the results of geographic research in the form of static and interactive maps
Agenda
.pull-left[
Day 1: Foundations
09:30-11:00 Introduction to the course and software
11:15-12:30: R's spatial ecosystem
Lunch
13:30-15:00 The structure of vector geographic data in R and working with attribute data
15:15-16:30 Spatial data operations
]
--
.pull-right[
Day 2 GIS, viz, modelling
09:30-11:00 Reading and writing geographic data
11:15-12:30: Geometric operations
Lunch
13:30-15:00 Visualisation
15:15-16:30 Working on your own data
]
--
Updated challenges/examples: git.io/geocompr-2-day-course
What is geocomputation?
.pull-left[
GeoComputation is about using the various different types of geodata and about developing relevant geo-tools within the overall context of a 'scientific' approach
r Citep(my_bib, "openshaw_geocomputation_2000", .opts = list(cite.style = "authoryear")).
]
.pull-right[
knitr::include_graphics("http://www.ccg.leeds.ac.uk/people/s.openshaw/s.openshaw.png")
]
--
- But we do differ from early definitions in one important way:
At the turn of the 21st Century it was unrealistic to expect readers to be able to reproduce code examples, due to barriers preventing access to the necessary hardware, software and data
Other aspects of the definition
--
What distinguishes geocomputation from the older quantitative geography, is its emphasis on "creative and experimental" GIS applications
r Citep(my_bib, "longley_geocomputation_1998", .opts = list(cite.style = "authoryear")).
--
It's about doing "practical work that is beneficial or useful" r Citep(my_bib, "openshaw_geocomputation_2000", .opts = list(cite.style = "authoryear")).
--
about harnessing the power of modern computers to do things with geographic data.
Introduction to the course and software
- Source: rstudio.com
--
- Key elements: Source, Console, Environment, Plot panes
--
- Projects and short-cuts (e.g. try
Alt-Shift-J)
An introduction to R
See slides at https://geocompr.github.io/presentations/intro-to-R.html
- Work through Exercises in Section 1 of handouts
R's spatial ecosystem
-
See slides at https://geocompr.github.io/presentations/satRday-newcastle.html#19
-
Rs capabilities have evolved substantially:
--
- rgdal, released in 2003
--
- sp, released 2005
-
spverse: gstat, geosphere, adehabitat
-
raster, released 2010
Spatial ecosystem: demo and practical
- Live demo: using R as a GIS
- Spatial data in R
- R's spatial ecosystem (see section 1.4 of Geocomputation with R - package ecosystem)
- Work through Section 2 exercises in hand-out
- Follow-on practical: Work through the exercise 1:3 in Chapter 2
- Advanced: complete exercises in Chapter 2
- Bonus: reproduce the results in the
sfvignettesf1
Attribute operations
-
See slides at https://geocompr.github.io/presentations/attr.html#1
-
Work through the following sections in Geocomputation with R:
-
Vector attribute subsetting (3.2.1)
- Vector attribute aggregation (3.2.2)
- Vector attribute joining (3.2.3)
-
Creating attributes and removing spatial information (3.2.4)
-
Answer questions in Section 3 in the handouts
-
Bonus: try remaining exercises in Chapter 3
Spatial data operations
- Live demo using stats19 data in Leeds
Work through the following sections in Geocomputation with R
- Spatial subsetting (4.2.1)
- Topological relations (4.2.2)
-
Spatial joining (4.2.3)
-
Work through section 4 in handouts
-
Bonus: Non-overlapping joins (4.2.4)
- Spatial data aggregation (4.2.5)
Day 2 GIS, visualisation, modelling: recap
--
.pull-left[
Day 1: Foundations
09:30-11:00 Introduction to the course and software
11:15-12:30: R's spatial ecosystem
Lunch
13:30-15:00 The structure of vector geographic data in R and working with attribute data
15:15-16:30 Spatial data operations
]
--
.pull-right[
09:30-11:00 Reading and writing geographic data
11:15-12:30: Geometric operations
Lunch
13:30-15:00 Visualisation
15:15-16:30 Working on your own data
]
--
Updated challenges/examples: git.io/geocompr-2-day-course
Reading and writing geographic data
- See slides at: https://geocompr.github.io/presentations/read-write.html#1
Geometric operations
-
See Chapter 5 of Geocomputation with R: Geometry operations
-
Example:
library(osmdata) cycleways = opq("leeds") %>% add_osm_feature(key = "name", value = "Cycle Superhighway 1") %>% osmdata_sf() cycleway_line = cycleways$osm_lines cycleway_buffer = cycleway_line %>% st_transform(27700) %>% st_buffer(500) %>% st_transform(4326)
Geometric exercises
-
Attempt the questions in Section 6 in the hand-out
-
Work through some of the examples of Chapter 6 of Geocomputation with R
-
Identify serious road traffic collisions that happened within 500 m of the Cycle Superhighway in 2017 (see https://git.io/geocompr-2-day-course)
Visualisation
- See slides at https://geocompr.github.io/presentations/04_viz.html#1
Working on your own data
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