Update intro comments, mention sf

intro-spatial.Rmd

@@ -56,7 +56,9 @@ experience with R will help.
 If you have not used R before, it may be worth following an
 introductory tutorial, such as
 *Efficient R Programming*
-([Gillespie and Lovelace, 2016](http://cran.r-project.org/doc/contrib/Torfs+Brauer-Short-R-Intro.pdf)), the official [Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.html) or tutorials suggested on [rstudio.com](https://www.rstudio.com/online-learning/) and [cran.r-project.org](https://cran.r-project.org/other-docs.html).
+([Gillespie and Lovelace, 2016](https://csgillespie.github.io/efficientR/)), *R for Data Science* ([Grolemund and Wickham, 2016](http://r4ds.had.co.nz/)) or tutorials suggested on [rstudio.com](https://www.rstudio.com/online-learning/) and [cran.r-project.org](https://cran.r-project.org/other-docs.html).
+
+...
 
 Now you know some R, it's time to turn your attention towards spatial data with R. To that end, this tutorial is organised as follows:
 
@@ -127,7 +129,7 @@ on and download some packages and data.
 R has a huge and growing number of spatial data packages. We recommend taking a quick browse on R's main website to see the spatial packages available:
 [http://cran.r-project.org/web/views/Spatial.html](http://cran.r-project.org/web/views/Spatial.html).
 
-In this tutorial we will use the following packages:
+In this tutorial we will use the packages from the '**sp**verse', that use the **sp** package:
 
 - **ggmap**: extends the plotting package **ggplot2** for maps
 - **rgdal**: R's interface to the popular C/C++ spatial data processing library [gdal](http://www.gdal.org/)
@@ -136,6 +138,7 @@ In this tutorial we will use the following packages:
 - [**dplyr**](http://cran.r-project.org/web/packages/dplyr/index.html) and [**tidyr**](http://blog.rstudio.org/2014/07/22/introducing-tidyr/): fast and concise data manipulation packages
 - **tmap**: a new packages for rapidly creating beautiful maps
 
+For a tutorial based on the recent [**sf**](https://github.com/edzer/sfr) package you will have to look elsewhere, like the [geocompr](https://bookdown.org/robinlovelace/geocompr/) website, the online home of the forthcoming book *Geocomputation with R*.
 
 Some packages may already be installed on your computer. To test if a package is installed, try to load it using the `library` function; for example, to test if **ggplot2** is installed, type `library(ggplot2)` into the console window. 
 If there is no output from R, this is good news: it means that the library
@@ -208,7 +211,7 @@ R to handle a broader range of spatial data formats. If you've not already
 
 ```{r, message=FALSE, results='hide'}
 library(rgdal)
-lnd <- readOGR(dsn = "data/LondonBoroughs.shp")
+lnd <- readOGR(dsn = "data/london_sport.shp")
 # lnd <- readOGR(dsn = "data", layer = "london_sport")
 ```
 
@@ -264,6 +267,12 @@ grid.raster(readPNG("figure/tab-complete.png"))
 
 To explore `lnd` object further, try typing `nrow(lnd)` (display number of rows) and record how many zones the dataset contains. You can also try `ncol(lnd)`.
 
+One issue with the data that we have loaded is that it has no coordinate reference system (CRS):
+
+```{r}
+lnd@proj4string
+```
+
 ## Basic plotting
 
 Now we have seen something of the structure of spatial objects in R,
@@ -282,8 +291,8 @@ an entirely different type of plot. Thus R is intelligent at guessing what you w
 R has powerful subsetting capabilities that can be accessed very concisely using square brackets,as shown in the following example:
 
 ```{r}
-# select rows of lnd@data where sports participation is less than 15
-lnd@data[lnd$Partic_Per < 15, ]
+# select rows of lnd@data where sports participation is less than 13
+lnd@data[lnd$Partic_Per < 13, 1:3]
 ```
 
 The above line of code asked R to select only the rows from the `lnd` object, where sports participation is lower than 15,
@@ -324,7 +333,7 @@ we will cover this in more detail in subsequent sections.
 
 As a bonus stage, select and plot
 only zones that are
-close to the centre of London (see Fig. 6). Programming encourages rigorous
+close to the centre of London (see Figure 5). Programming encourages rigorous
 thinking and it helps to define the problem
 more specifically:
 
@@ -383,75 +392,24 @@ lnd$quadrant <- "unknown" # prevent NAs in result
 lnd$quadrant[east & north] <- "northeast"
 ```
 
-> **Challenge**: Based on the the above code as refrence try and find the remaining 3 quadrants and colour them as per Figure 6 below.
+> **Challenge**: Based on the the above code as refrence try and find the remaining 3 quadrants and colour them as per Figure 6.
 Hint - you can use the **llgridlines** function in order to overlay the long-lat lines.
 For bonus points try to desolve the quadrants so the map is left with only 4 polygons.  
 
-```{r, echo=FALSE, eval=FALSE}
+```{r, echo=FALSE, fig.cap="The 4 quadrants of London and dissolved borders. Challenge: recreate a plot that looks like this.", fig.show='hold', out.height="5cm"}
 lnd$quadrant[!east & north] <- "northwest"
 lnd$quadrant[east & !north] <- "southeast"
 lnd$quadrant[!east & !north] <- "southwest"
-library(tmap)
-qtm(lnd, fill = "quadrant")
-lnd_disolved = rgeos::gUnaryUnion(spgeom = lnd, id = lnd$quadrant)
-plot(lnd_disolved)
-```
-
-
-```{r, echo=FALSE, fig.cap="The 4 quadrants of London"}
-grid.raster(readPNG("figure/lnd-quads.png"))
-par(mfrow = c(1,2))
-#plot the results
-# plot(london)
-# plot(lnd[east & north,],add = TRUE, col = "red" )
-
-# place a grid over the object
-# llgridlines(lnd, lty= 3, side ="EN", offset = -0.5)
+plot(lnd)
+plot(lnd[east & north,], add = TRUE, col = "red" )
+llgridlines(lnd, lty= 3, side ="EN", offset = -0.5)
 
-london = gUnaryUnion(lnd, lnd$dummy)
-london = SpatialPolygonsDataFrame(london, data.frame(dummy = c("london")), match.ID = FALSE)
-
-centrelondon = gCentroid(london, byid = TRUE)
-
-centreLEP = gCentroid(lnd, byid = TRUE)
-coords <- cbind(centrelondon$y)
-
-c1 = c(centrelondon$x, centrelondon$x)
-c2 = c(90, -90)
-c3 = c(90, -90)
-c4 = c(centrelondon$y,centrelondon$y)
-
-# simple line strings
-L1 = Line(cbind(c1, c2))
-L2 = Line(cbind(c3, c4))
-
-Ls1 = Lines(list(L1), ID = "a")
-Ls2 = Lines(list(L2), ID = "b")
-
-Ls1 <- SpatialLines(LinesList = list(Ls1))
-Ls2 <- SpatialLines(LinesList = list(Ls2))
-
-Longitude = SpatialLinesDataFrame(Ls1, data.frame(Z = c("1", "2"), row.names = c("a","b")))
-Latitude = SpatialLinesDataFrame(Ls2, data.frame(Z = c("1", "2"), row.names = c("a","b")))
-
-east <- coordinates(lnd)[,1] > Longitude@lines[[1]]@Lines[[1]]@coords[,1][1]
-north <- coordinates(lnd)[,2] > Latitude@lines[[1]]@Lines[[1]]@coords[,2][1]
-west <- coordinates(lnd)[,1] < Longitude@lines[[1]]@Lines[[1]]@coords[,1][1]
-south <-coordinates(lnd)[,2] < Latitude@lines[[1]]@Lines[[1]]@coords[,2][1]
-
-lnd@data$quadrant[east & north] <- "northeast"
-lnd@data$quadrant[west & north] <- "northwest"
-lnd@data$quadrant[east & south] <- "southeast"
-lnd@data$quadrant[west & south] <- "southwest"
-
-# plot(lnd)
-# plot(lnd[east & north,],add = TRUE, col = "red" )
-# plot(lnd[west & north,],add = TRUE, col = "blue" )
-# plot(lnd[east & south,],add = TRUE, col = "green" )
-# plot(lnd[west & south,],add = TRUE, col = "yellow" )
+lnd_disolved = rgeos::gUnaryUnion(spgeom = lnd, id = lnd$quadrant)
 
-# llgridlines(lnd, lty= 3, side ="EN", offset = -0.5)
-par(mfrow=c(1,1)) # return to default par
+library(tmap)
+qtm(lnd, fill = "quadrant") +
+  tm_shape(lnd_disolved) +
+  tm_borders(lwd = 9)
 ```
 
 <!-- ## Attribute data -->
@@ -975,7 +933,7 @@ plot(stations) # test the clip succeeded
 <!-- ``` -->
 
 <!-- This results in a simple choropleth map and a new vector containing the area of each -->
-<!-- borough (the basis for Fig. 11). As an additional step, try comparing the mean -->
+<!-- borough (the basis for Figure 11). As an additional step, try comparing the mean -->
 <!-- area of each borough with the -->
 <!-- mean value of `stations` points within it: `plot(lnd_n$NUMBER, areas)`. -->