Updates and new vignettes

.gitignore

@@ -25,3 +25,9 @@ shef*
 *shinyapp*
 intro-spatial.R
 test.Rmd
+test.geojson
+lines.geojson
+AFG_adm1.RData
+FRA_adm1.RData
+arast.*
+bikeWY.geojson

R-spatial-pres.Rmd

@@ -1,7 +1,7 @@
 ---
 title: "An introduction to R for Spatial data"
 author: "Robin Lovelace"
-date: "Sheffield, May 15th 2015"
+date: "Newcastle, June 2nd 2015"
 output:
   slidy_presentation:
     fig_height: 4
@@ -23,19 +23,37 @@ This course is brought to you the Consumer Data Research Centre
 project based at the University of Leeds and UCL. It is 
 funded by the ESRC's ([Big Data Network](http://www.esrc.ac.uk/research/major-investments/Big-Data/BDN-Phase2.aspx))
 
+Packages
+=================
+
+
+
+```{r, eval=FALSE}
+pkgs <- c("rgdal", # (can be tricky)
+  "rgeos",
+  "ggmap",
+  "tmap",)
+install.packages(pkgs)
+```
+
+
 Course agenda
 =======================================================
 
 
 During this course we will cover these topics
 
+- Introducing R as a GIS: Introduction and example with London (Part I handout), 09:00 - 11:00 am
+- The structure of spatial objects in R (Part II) 11:15 - lunch
+- Loading and interrogating spatial data (Part III) Day 1 14:00 - 15:15
+- Visualising spatial datasets (Part III) Day 1 15:30 - 11:00 Day 2
+
+<!--
 - An introduction to R (14:00 - 15:00)
 - Loading, interrogating and manipulating spatial data (15:00 - 16:00)
 - Making maps with ggplot2 (16:00 - 16:30)
 - Questions, answers and experimenting with R
 
-<!--
-
 LUNCH
 
 - Making maps in ggplot2 (13:30 - 14:30)
@@ -44,6 +62,17 @@ LUNCH
 - Taking it further + Q & A (16:00 onwards)
 -->
 
+Day 2
+==========
+
+- Acquiring external data with R Day 2 11:15 - lunch
+
+After that: you choose!
+
+- Point pattern analysis and spatial interpolation 
+- Geographical models in R
+- Webmaps
+
 ```{r, echo=FALSE}
 # During this course we will cover these topics
 # 

R/middle-earth.R

@@ -0,0 +1,12 @@
+library(rgeos)
+plot(lnd, col = "grey")
+cent_lnd <- gCentroid(lnd)
+points(cent_lnd, cex = 3)
+lnd_buffer <- gBuffer(spgeom = cent_lnd, width = 10000) # set 10 km buffer
+lnd_central <- lnd[lnd_buffer,]
+lnd_cents <- SpatialPoints(coordinates(lnd),  proj4string = CRS(proj4string(lnd)))
+sel <- lnd_cents[lnd_buffer,]
+lnd_central <- lnd[sel,]
+plot(lnd_central, add = T, col = "red")
+plot(lnd_buffer, add = T, col = "white")
+text(coordinates(cent_lnd), "Middle Earth")

data/.gitignore

@@ -0,0 +1,3 @@
+CTYUA*
+cuas.zip
+Boundary_Dataset*

vignettes/dl-las-stats19.Rmd

@@ -0,0 +1,68 @@
+---
+title: "Download LAs and point-pattern analysis"
+author: "Robin Lovelace"
+date: "June 3, 2015"
+output: html_document
+---
+
+```{r, eval=FALSE}
+downloader::download("https://geoportal.statistics.gov.uk/Docs/Boundaries/County_and_unitary_authorities_(E+W)_2012_Boundaries_(Generalised_Clipped).zip", "data/cuas.zip")
+unzip("data/cuas.zip", exdir = "data")
+cuas <- raster::shapefile("data/CTYUA_DEC_2012_EW_BGC.shp")
+plot(cuas)
+cuas@data$CTYUA12NM
+WY <- cuas[cuas$CTYUA12NM == "Surrey",]
+plot(WY)
+```
+
+Then follow this link to find out how to get data for road traffic
+'accidents': https://github.com/Robinlovelace/bikeR/blob/master/video-routes/load-stats19.R
+
+After running the code in this file, the
+challenge is to create a dataset of traffic
+casualties like this:
+
+```{r}
+library(spatstat)
+library(maptools)
+library(rgeos)
+library(raster)
+# downloader::download("https://github.com/Robinlovelace/bikeR/raw/master/geodata/bikeWY.geojson", "bikeWY.geojson")
+ac <- rgdal::readOGR("../bikeWY.geojson", "OGRGeoJSON")
+ac <- spTransform(ac, CRS("+init=epsg:27700"))
+
+# estimate 2d density
+acp <- as.ppp(ac)
+adens <- density.ppp(x = acp, sigma = 50, eps = 50)
+plot(adens)
+arast <- raster(adens)
+plot(arast)
+
+writeRaster(x = arast, filename = "arast.tif", overwrite = T)
+dsg <- as(arast, "SpatialGridDataFrame")
+dsg <- as.image.SpatialGridDataFrame(dsg)
+dcl <- contourLines(dsg, nlevels = 10)
+sldf <- ContourLines2SLDF(dcl)
+plot(sldf[8,]) # the most intense accident hotspot
+h1 <- gPolygonize(sldf[8,])
+spChFIDs(h1) <- 1
+h2 <- gPolygonize(sldf[7,])
+spChFIDs(h1) <- 2
+plot(h2)
+h3 <- gPolygonize(sldf[6,])
+spChFIDs(h3) <- seq(101, 100 + length(h3))
+hspots <- spRbind(h3, h2)
+h4 <- gPolygonize(sldf[5,])
+
+h5 <- gPolygonize(sldf[3,]) # the right contour to save
+length(h5)
+proj4string(h5) <- proj4string(ac)
+nacs <- aggregate(ac, h5, length)
+nacs <- spTransform(nacs, CRS("+init=epsg:27700"))
+nacs$area <- gArea(nacs, byid = T)
+plot(nacs)
+plot(adens, add = T)
+plot(nacs, add = T)
+```
+
+

vignettes/get-data.Rmd

@@ -0,0 +1,49 @@
+---
+title: "Geographical data out of R"
+author: "Robin Lovelace"
+date: "June 3, 2015"
+output: html_document
+---
+
+## Raster package
+
+```{r}
+lnd_raster <- raster::shapefile("data/")
+```
+
+
+```{r}
+library(raster)
+fr <- getData(name = "GADM", country = "FRA", level = 1)
+allcountries <- ccodes()
+allcountries[i,2]
+plot(fr)
+names(fr@data)
+fr$NAME_1
+
+world <- getData(name = "GADM")
+
+afg <- getData(country = "AFG", level = 1)
+?getData
+plot(cc)
+
+
+object.size(afg) / 1000000
+afgs <- rgeos::gSimplify(afg, tol = 0.01)
+afg <- SpatialPolygonsDataFrame(afgs, afg@data)
+object.size(afg) / 1000000
+
+# library(devtools)
+# install_github("rstudio/leaflet")
+library(leaflet)
+leaflet() %>% 
+  addTiles() %>% 
+  addPolygons(data = afg, popup = afg$NAME_1)
+```
+
+> **Challenge:** download data for 2 different countries and 2 different
+levels. 
+
+> **Challenge:** Try to merge some data you've found from the internet with this geographical data and plot the result using your prefered method out of tmap, base graphics, leaflet or ggmap.
+
+

vignettes/multipart-geojson.Rmd

@@ -0,0 +1,45 @@
+---
+title: "Multipart polygons R"
+author: "Robin Lovelace"
+date: "June 2, 2015"
+output: html_document
+---
+
+Based on this:
+https://stackoverflow.com/questions/30583048/convert-features-of-a-multifeature-geojson-into-r-spatial-objects/30593077#30593077
+
+Normally you can read geojson files into R with trusty `readOGR`, as illustrated [here][1].
+
+However, this fails for multifeature geojsons.
+
+Reproducible example:
+
+```{r}
+downloader::download("https://github.com/Robinlovelace/Creating-maps-in-R/raw/master/data/test-multifeature.geojson", "test.geojson")
+try(test <- rgdal::readOGR("test.geojson", "OGRGeoJSON")) # fails with:
+```
+
+The error message is clear-enough and indicates a solution: split the features.
+Aside from doing this with regex, I don't know how, however.
+
+Any ideas very much welcome.
+
+The amazing thing: [GitHub displays the data natively on the browser][2], whereas R cannot even (seemingly) read it in!
+
+Alternative way to a solution:
+
+```{r}
+test <- geojsonio::geojson_read("test.geojson")
+```
+
+## Solution from Spacedman
+
+```{r}
+mess <- 'ogr2ogr -where "OGR_GEOMETRY=\'LINESTRING\'" -f "GeoJSON" lines.geojson  test.geojson'
+system(mess)
+testl <- rgdal::readOGR("lines.geojson")
+```
+
+
+  [1]: https://github.com/Robinlovelace/Creating-maps-in-R/blob/master/vignettes/geoJSON.Rmd
+  [2]: https://github.com/Robinlovelace/Creating-maps-in-R/blob/master/data/test-multifeature.geojson