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Load, plot vector layers & do spatial joins in R

Vector data visualization

We have followed a tutorial to replicate and improve our case study. Here we work on vector layers, first download a compressed folder in linux environment and extract it and check what you have using bash functions within R.

 system("ogrinfo -al cornwall_ward_27700.shp | more ")
 # ... 
 # Layer SRS WKT:
 # (unknown)

We downloaded a vector shape file in EPSG 27700 but this info is not available in the metadata, so we can update the metadata with projection parameters in a new layer.

 system("ogr2ogr -s_srs EPSG:27700 -t_srs EPSG:27700 cornwall.shp cornwall_ward_27700.shp")

Using the raster library we can load shape files in R from linux OS folder.

 library(raster) <- shapefile("cornwall_ward_27700.shp")
 ward <- shapefile("cornwall.shp")
 # Tell the difference between the two layers
More readings on using rgdal library to load vector maps

The raster library has a plot function able to plot R spatial objects such as the SpatialPolygonsDataFrame which is the corresponding object to the vector shape file we just imported. raster plot is overwriting the basic plot fuction of R.


The ward administrative boudary in cornwall

Spatial Join in R

We can load a text table in csv format, do a join by identifier and plot maps with statistics.

 stat = read.csv("SYNT_2002.csv", header=T)

To join files we need a common identifier. Which is the table and vector file common ID? Find it and merge the files using merge function from raster library to then plot the map and save the join vector file in your working directory outside R.

  agri <- merge(ward, stat, by.x='PI', by.y='ID')
  writeOGR(agri, ".", "agristat", driver="ESRI Shapefile")

Now we can plot in the ward map some features about agriculture using the statistics we have joined to our spatial database using the ggplot2 library.

Plotting some features of our database (ex: ward surface vs numberof diary livestock per ward) in black and white and in colour.

 # simple visualization black and white
 p<-ggplot(agri@data, aes(SUM_HA,dairy_v))
 # Add some colours
 p+geom_point(colour="red", size=2)
 # Add a legend
 p+geom_point(aes(colour=SUM_HA, size=dairy_v))
 # Add labels  (these are too many for our cvisualization)

Play with this and plot other informations…
Now we map the number of Diary livestock in Cornwall per each ward. We use fortify ( check ?fortify in R).

 agri_geom<-fortify(agri, region="PI")
 agri_geom<-merge(agri_geom, agri@data, by.x="id", by.y="PI")
 ## The map function:
 ## Map<-ggplot(agri_geom, aes(long,lat, group=group, fill=dairy_v))
 ## + geom_polygon()+ coord_equal() + 
 ## labs(x="Easting (m)", y="Northing (m)",fill= "# of livestocks")
 ## + ggtitle ("Dairy cow in Cornwall")
 Map<-ggplot(agri_geom, aes(long,lat, group=group, fill=dairy_v))+ geom_polygon()+ coord_equal() + labs(x="Easting (m)", y="Northing (m)",fill= "# of livestocks")+ ggtitle ("Dairy cow in Cornwall")
 # Black and white map
 Map + scale_fill_gradient(low="white", high="black")

wiki/rstat/r_load_vect.txt · Last modified: 2017/12/05 22:53 (external edit)