slides.Rmd

---
title: "Geocomputation with R"
subtitle: "pre-conference workshop"
author: "Robin Lovelace, University of Leeds, for the RGS, London"
date: "2019-04-06. Source code: [github.com/geocompr/geocompkg](https://github.com/geocompr/geocompkg/blob/master/slides/)<br> "
output:
  xaringan::moon_reader:
    nature:
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
bibliography: 
  - rg.bib
  - geocompr.bib
---

```{r get-refs, include=FALSE}
download.file("https://github.com/geocompr/geocompkg/raw/master/slides/refs-geostat.bib", "rg.bib")
download.file("https://github.com/Robinlovelace/geocompr/raw/master/refs.bib", "geocompr.bib")
refs_geostat = RefManageR::ReadBib("rg.bib")
refs_geocompr = RefManageR::ReadBib("geocompr.bib")
o = ls()
r = o[grepl(pattern = "refs_", x = o)]
refs = do.call(what = c, args = mget(r))
ref_df = as.data.frame(refs)
# View(ref_df)
# RefManageR::WriteBib(refs, "refs-all.bib")
```


class: large

## What is this workshop about?



27 August 2019
2.00pm-5.00pm Free - places must be booked in advance

Royal Geographical Society (with IBG), 1 Kensington Gore, London, SW7 2AR

Free

This event, sponsored by the Quantitative Methods Research Group and led by Dr. Robin Lovelace, will bring together data, maps and (soon-to-be) R enthusiasts to learn a bit more about Robin's latest book, “Geocomputation in R”. In the spirit of open-source hacking, the book is available online and the event will borrow those ideas to offer an introductory, hands-on, and fun session getting started with spatial data in R. The only things you need to bring are your laptop and a positive attitude to learn!


--

```{r, echo=FALSE}
kensington_geo_talk = list(
  introduction = "2-3 pm: introduction to R",
  ecosystem = "3-4 pm: spatial data in R",
  vizpkgs = "4-5 pm: visualisation, map making + off piste"
)
```

```{r}
str(kensington_geo_talk)
```


---

# intro

---

## 'Team geocompr'

```{r setup, include = FALSE}
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 = do.call(what = c, args = mget(r))
knitr::opts_chunk$set(eval = TRUE)
```

.pull-left[

- [Jakub Nowosad](https://nowosad.github.io/): developer of GeoPAT + more.

- [Jannes Muenchow](http://www.geographie.uni-jena.de/en/Muenchow.html), creator of **RQGIS**.

- [Robin Lovelace](https://www.robinlovelace.net/), creator of **stplanr**, co-author of Efficent R Programming.

]

.pull-right[

```{r}
library(leaflet)
popup = c("Robin", "Jakub", "Jannes")
m = leaflet() %>%
  addProviderTiles("NASAGIBS.ViirsEarthAtNight2012") %>%
  addMarkers(lng = c(-3, 23, 11),
             lat = c(52, 53, 49), 
             popup = popup)
```

]

--

```{r, out.height="200px", out.width="700px"}
m
```


---

.pull-left[

## Why use geographic data?

- Geographic data is everywhere
- underlies some of society's biggest issues
- Give generalised analyse local meaning

]

--

.pull-right[

```{r, echo=FALSE}
knitr::include_graphics("https://raw.githubusercontent.com/npct/pct-team/master/figures/rnet-newcastle-cost-road.png")
```

- Example: Propensity to Cycle Tool (PCT) in London: http://pct.bike/m/?r=london

]

---

## 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[
```{r, echo=FALSE, message=FALSE}
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 `r Citep(my_bib, "lovelace_geocomputation_2019", .opts = list(cite.style = "authoryear"))`

---

## Another definition

> about harnessing the power of modern computers to *do things* with geographic data `r Citep(my_bib, "lovelace_geocomputation_2019", .opts = list(cite.style = "authoryear"))`.

--

.pull-left[

```{r, echo=FALSE}
knitr::include_graphics("https://geocompr.robinlovelace.net/figures/08_metro_areas.png")
```

]

.pull-right[

Raster image of Germany (resolution: 20 km) with the identified metropolitan areas (golden polygons) and the corresponding names.

]

---


.pull-left[
## What's in the box?

- Chapter 1: History + 'philosophy' = important

Foundations

- Starting from nothing
- Class definitions
- Spatial/attribute operations
- Projections 
- Data IO

Extensions

- Advanced methods
- How to build your own functions

Applications

- A taster of what you can do
- Transport, location analysis, ecology

]

--

.pull-right[

## Win this book!

Enter the reproducible map comp:

- most creative map making people think in new ways about geographic phenomena
- most policy relevant map

- Submission: [GitHub](https://github.com/Robinlovelace/geocompr/issues/437) by 16:30

```{r, echo=FALSE, message=FALSE}
knitr::include_graphics("https://geocompr.robinlovelace.net/images/cover.png")
```
]

---

## Let's get started with R

--

But first some audience participation

<!-- Get people to stand up on one leg, hold their ear, rub their bellies, rub their bellies and heads. Point: it will be hard. Plus bonding -->

--

Who has used R before?

--

Who has never used R before?

--

Who installed R on their computer for this workshop?

--

Who hasn't got R installed?

--

Any questions?

--

5 minute break + set-up -> practical bit


---

---

## A brief history of geographic packages in R

- See a [video](https://www.youtube.com/watch?v=Luxllb_WAqY) of Roger Bivand's talk on the subject (live demo of [R 0.49](https://cran.r-project.org/src/base/R-0/), released 1997) + GitHub [repo](https://github.com/rsbivand/eRum18/blob/master/eRum_keynote_18.Rmd)

- Brian Ripley, on R's [core team](https://www.r-project.org/contributors.html), with strong interest in spatial stats

- Rs capabilities have evolved substantially since then!

--

- **rgdal**, released in 2003

--

- **sp**, released 2005
  - **sp**verse: **gstat**, **geosphere**, **adehabitat**

- **raster**, released 2010

- **sf**, released 2017

---

## Getting data

- Many portals and packages for getting data - see [Chapter 7](https://geocompr.robinlovelace.net/read-write.html)

```{r}
library(osmdata)
kensington = getbb("kensington, london")
osm_data = opq(bbox = kensington) %>% 
  add_osm_feature(key = "leisure", value = "park") %>% 
  osmdata_sf()
parks = osm_data$osm_polygons
```

--

```{r, echo=FALSE, warning=FALSE, message=FALSE, out.height="200px"}
library(tmap)
tmap_mode("view")
qtm(parks)
tmap_mode("plot")
```

---

## Getting transport data

- pct package developed to make transport data more accessible

--

- Get transport zones for kensington

```{r}
zones_all = read_sf("zones_near_rgs.geojson")
zones = zones_all[c("geo_code", "car_driver", "foot", "bicycle")]
plot(zones)
```

---

## sf

- **sf** is a recently developed package for spatial (vector) data
- Combines the functionality of three previous packages: **sp**, **rgeos** and **rgdal**
- Has many advantages, including:
    - Faster data I/O
    - More geometry types supported
    - Compatibility with the *tidyverse*

---

## Reading and writing spatial data

```{r, echo=FALSE}
library(sf)
crashes_near_rgs = read_sf("crashes_near_rgs.geojson")
```

- `write_sf()` writes data `st_write(zones, 'zones.gpkg')`.

- See supported formats with: `sf::st_drivers()`. Details: Chapter 6 of our book: [geocompr.robinlovelace.net/read-write.html](https://geocompr.robinlovelace.net/read-write.html)

---

## sf class 

```{r}
library(spData) # pre-packaged data
class(zones)
zones
```

```{r, eval=FALSE, echo=FALSE}
# ---
## Structure of the sf objects
# world$name_long
# ```
# 
# ```{r, echo=FALSE}
# world$name_long[1:3]
# ```
# 
# ```{r, eval=FALSE}
# world$geom
# ```
# 
# ```{r, echo=FALSE}
# print(world$geom, n = 3)
```

---

## Spatial operations

It's a big topic which includes:

- Spatial subsetting
- Spatial joining/aggregation
- Topological relations
- Distances
- Spatial geometry modification
- Raster operations (map algebra)

See [Chapter 4](http://geocompr.robinlovelace.net/spatial-operations.html) of *Geocomputation with R* 

---

## Spatial subsetting

```{r, warning=FALSE}
parks = read_sf("parks.geojson")
crashes_in_parks = crashes_near_rgs[parks, ]
```

--

```{r, echo=FALSE, message=FALSE}
tmap_mode("view")
tm_shape(crashes_near_rgs) +
  tm_dots(alpha = 0.2, size = 0.1) +
  tm_shape(parks) +
  tm_polygons(col = "green", alpha = 0.3) +
  tm_shape(crashes_in_parks, col = "red") +
  tm_dots() 
```

---

## Spatial subsetting II

```{r, warning = FALSE, message = FALSE, fig.height = 4}
lnd_buff = lnd[1, ] %>% 
  st_transform(crs = 27700) %>%  # uk CRS
  st_buffer(500000) %>%
  st_transform(crs = 4326)
near_lnd = world[lnd_buff, ]
plot(near_lnd$geom)
```

- What is going with the country miles away?

---

## Spatial aggregation

```{r}
points_per_zone = aggregate(crashes_near_rgs[1], zones, length)
plot(points_per_zone)
```

---

## Exercises

- Find Chapter 4 of Geocomputation with R online
- Complete Section 4.2, which will involve running (at a minimum) the following code:

```{r, message=FALSE}
library(sf)
library(spData)
library(dplyr)
canterbury = nz %>% filter(Name == "Canterbury")
canterbury_height = nz_height[canterbury, ]
```

- Attempt Exercises 1:3 at the end of the chapter
- Bonus: find out how to get the data for the RGS site and identify all crashes that happened within 200 m of the RGS building in 2017

---

background-image: url(https://user-images.githubusercontent.com/1825120/38543030-5794b6f0-3c9b-11e8-9da9-10ec1f3ea726.gif)
background-size: cover
class: center, middle

# Geographic data visualisation

--

Source: [Geocomputation with R](https://geocompr.robinlovelace.net/adv-map.html#animated-maps)

---

## sf graphics

```{r}
plot(zones[c("car_driver", "foot")])
```

---

## ggplot2

```{r}
library(ggplot2)
ggplot(zones) +
  geom_sf(aes(fill = bicycle)) +
  scale_fill_viridis_c()
```

---

## leaflet

```{r, eval=FALSE}
library(leaflet)
leaflet(zones) %>%
        addTiles() %>%
        addPolygons(color = "#444444", weight = 1, fillOpacity = 0.5,
                    fillColor = ~colorQuantile("YlOrRd", bicycle)(bicycle),
                    popup = paste(round(zones$bicycle, 2)))
```

```{r, echo=FALSE, message=FALSE}
library(widgetframe)
library('leaflet')
l = leaflet(zones) %>%
  addTiles() %>%
  addPolygons(color = "#444444", weight = 1, fillOpacity = 0.5, fillColor = ~colorQuantile("YlOrRd", foot)(foot), popup = paste(round(zones$foot, 2)))
frameWidget(l, height = '400')
```

---

## tmap

- A dedicated mapping R package

```{r}
library(tmap)
tm_shape(zones) +
  tm_polygons("foot", palette = "RdYlBu")
```

---

## tmap II

- A dedicated mapping R package

```{r, message=FALSE}
tmap_mode("view")
tm_shape(zones) +
  tm_polygons("foot", palette = "RdYlBu")
```

---

## Mapdeck

- See example at [geocompr.robinlovelace.net/adv-map.html](https://geocompr.robinlovelace.net/adv-map.html#other-mapping-packages)

```{r}
library(mapdeck)
set_token(Sys.getenv("MAPBOX"))
mapdeck(data = zones) %>% 
  mapdeck::add_sf() %>% 
  add_polygon(elevation = zones$foot^2)
```


---

## Shiny

Here's one I made earlier: www.pct.bike

```{r, echo=FALSE}
knitr::include_graphics("https://raw.githubusercontent.com/npct/pct-team/master/figures/front-page-leeds-pct-demo.png")
```

---

## Future: Advances in raster data with R

Raster data in R is evolving:

- **raster** package dominates, designed for `Spatial*` objects, e.g. with `as(my_vector, "Spatial")`

- Efforts to bring raster data closer to the **tidyverse**/**sf** approaches, such as [tabularaster](https://github.com/hypertidy/tabularaster), [sfraster](https://github.com/mdsumner/sfraster) + [fasterize](https://github.com/ecohealthalliance/fasterize)
- [stars](https://github.com/r-spatial/stars) focusses on multidimensional space-time (raster/vector) is evolving.
- **[terra](https://github.com/rspatial/terra)** - first release expected 2019

--

- See https://github.com/edzer/sdsr

```{r, out.width="200px"}
raster::plot(spData::elev)
```

---

## Spatial networks

- Lots of packages out there, including `stplanr`, `dodgr` and `tidygraph`

- Source: https://github.com/spnethack/spnethack

```{r, echo=FALSE, out.width="80%"}
knitr::include_graphics("https://raw.githubusercontent.com/spnethack/spnethack/master/README_files/figure-gfm/pway-1.png")
```

---

## Summary: do things with geographic data in R

Base R + **tidyverse** now (mostly) works with spatial data.

This is thanks to **sf**, a recent package (first release in 2016) that implements the open standard data model *simple features*. Get **sf** with:

```{r, eval=FALSE}
install.packages("sf")
```

Raster data is also supported, in the more mature package **raster**:

```{r, eval=FALSE}
install.packages("raster")
```


For datasets...:

```{r, eval=FALSE}
install.packages("spData")
install.packages("rnaturalearth")
```

For more on this see the book: [github.com/Robinlovelace/geocompr](https://github.com/Robinlovelace/geocompr)

--

- 2 day workshop: [lida.leeds.ac.uk/event](https://lida.leeds.ac.uk/event/geocomputation-data-analysis-r/)

---

## Final practical session

- Option 1: Continue to work through exercises in the book - pick a chapter!

- Option 2: Work on reading in and mapping data related to your research (and ask for help)

- Everyone: Get a GitHub account and submit something to the reproducible map competition here: [github.com/robinlovelace/geocompr/issues](https://github.com/robinlovelace/geocompr/issues)

---

class: center, middle

# Thanks, links, happy (R)GS  🚶, 🚲 +  🚀!

- Reproducible slides: [github](https://github.com/geocompr/RGSWorkshop)

--

- Open source book: [geocompr.robinlovelace.net](https://geocompr.robinlovelace.net/)

--

- Support the project + open source: install it, use it, communicate!

--

- Twitter #geocompr, [@robinlovelace](https://twitter.com/robinlovelace), [@jakub_nowosad](https://twitter.com/jakub_nowosad/)



Check out future R courses...

--

```{r, echo=FALSE}
knitr::include_graphics("https://user-images.githubusercontent.com/1825120/55670985-962c7700-5882-11e9-8bef-188854547011.png")
```