The pgrid package allows conveniently creating raster data sets (more specifically: VeloxRaster objects) from the PRIO GRID raw data as distributed at http://grid.prio.org.
Install pgrid straight from github:
require(devtools)
devtools::install_github('hunzikp/pgrid')
Before using the pgrid package you need to download the PRIO GRID data in csv format from the PRIO GRID download portal. You don't need to download all the available variables, but obviously, when using pgrid, you'll only have access to those variables you downloaded.
Once you've downloaded the static and/or yearly PRIO GRID data you're
interested in, you need to let pgrid know where those files are located.
To do so, use the setDataPath function, e.g. as follows:
library(pgrid)
## Set paths
setDataPath(staticPath = '/home/hunzikp/Data/priogrid/PRIO-GRID Static Variables - 2017-12-08.csv',
yearlyPath = '/home/hunzikp/Data/priogrid/PRIO-GRID Yearly Variables for 1946-2014 - 2017-12-08.csv')
To see what PRIO GRID variables the pgrid package has access to, use the
getVarNames function. If the details option is turned on, the
function returns additional information about variable sources, etc.
Here we inquire what static PRIO GRID variables pgrid has access to:
## Get info about static variables
static.df <- getVarNames(type = 'static', details = TRUE)
names(static.df)
## [1] "name" "id" "displayName"
## [4] "description" "category" "type"
## [7] "algorithm" "default" "startYear"
## [10] "endYear" "sourceName" "sourceUrl"
## [13] "sourceReference" "version" "unit"
head(static.df[,c('name', 'displayName', 'unit')])
## name displayName unit
## 6 agri_gc Agriculture land (Globcover) %
## 7 aquaveg_gc Aquatic vegetation (Globcover) %
## 8 barren_gc Barren land (Globcover) %
## 9 cmr_max Child malnutrition (%), max %
## 10 cmr_mean Child malnutrition (%), average %
## 11 cmr_min Child malnutrition (%), min %
And equivalently, what yearly variables pgrid has access to:
## Get info about yearly variables
yearly.df <- getVarNames(type = 'yearly', details = TRUE)
head(static.df[,c('name', 'displayName', 'unit')])
## name displayName unit
## 6 agri_gc Agriculture land (Globcover) %
## 7 aquaveg_gc Aquatic vegetation (Globcover) %
## 8 barren_gc Barren land (Globcover) %
## 9 cmr_max Child malnutrition (%), max %
## 10 cmr_mean Child malnutrition (%), average %
## 11 cmr_min Child malnutrition (%), min %
The getPrioRaster function returns PRIO GRID data as rasters. In the
following, we construct a raster containing distances to the nearest
urban center in one band (a static variable), and nightlight emission
data for all available years between 2005 and 2010 in the other bands.
Note that getPrioRaster returns a list with two elements: (1) the
actual raster data, and (2) a data frame containing meta data.
## Get raster data
prio.ls <- getPrioRaster(names = c('ttime_mean', 'nlights_mean'), years = 2005:2010)
prio.vx <- prio.ls$raster
meta.df <- prio.ls$meta
The meta data frame links the requested variables to the raster bands:
## Show meta data
head(meta.df)
## name year band
## 1 nlights_mean 2005 1
## 2 nlights_mean 2006 2
## 3 nlights_mean 2007 3
## 4 nlights_mean 2008 4
## 5 nlights_mean 2009 5
## 6 nlights_mean 2010 6
The raster data is returned as a VeloxRaster
object. If we want to plot it, we can
cast it as a RasterStack. In the following, we use the excellent
rasterVis package for
plotting.
## Cast raster data as RasterStack
prio.stk <- prio.vx$as.RasterStack()
## Plot nightlights in 2005/2010
library(raster)
library(rasterVis)
nightPalette <- colorRampPalette(c("black",
rgb(218,165,32,maxColorValue=255),
rgb(255,215,0,maxColorValue=255),
"white"), bias = 0.5)
nightTheme <- rasterTheme(region = nightPalette(20))
levelplot(prio.stk[[meta.df$band[meta.df$name == 'nlights_mean' & meta.df$year %in% c(2005,2010)]]],
maxpixels = 720*360,
par.settings = nightTheme)
