# Real world example

A typical example is the usage of an already existing project database in GRASS. GRASS organizes all data in an internal file structure that is known as gisdbase folder, a mapset and one or more locations within this mapset. All raster and vector data is stored inside this structure and the organisation is performed by GRASS. So a typical task could be to work on data sets that are already stored in an existing GRASS structure

## Creating a GRASS project

First of all we need some real world data. In this this case the gridded 2011 micro zensus population data of Germany. It has some nice aspects:

• It is provided in a typical authority format
• It is big enough >35 Mio points
• It is pretty instructive for a lot of spatial analysis.

We also have to download a meta data description file (excel sheet) for informations about projection and data concepts and so on.

 # we need some additional packages
require(curl)

# first of all we create  a project folder structure
projFolders =  c("run/"),
path_prefix = "path_",
global = TRUE)

# set runtime directory
setwd(path_run)

# get some typical authority generated data
DemografischeGrunddaten/csv_Bevoelkerung_100m_Gitter.zip;
jsessionid=294313DDBB57914D6636DE373897A3F2.2_cid389?__blob=publicationFile&v=3"

# unzip it
unzip(res,files = grep(".csv", unzip(res,list = TRUE)$Name,value = TRUE), junkpaths = TRUE, overwrite = TRUE) fn <- list.files(pattern = "[.]csv$", path = getwd(), full.names = TRUE)

### Preprocessing of the data

After downloading the data we will use it for some demonstration stuff. If you have a look the data is nothing than x,y,z with assuming some projection information.

# get the filename

head(xyz)

We can easy rasterize this data as it is intentionally gridded data.that means we have in at a grid size of 100 by 100 meters a value.

 require(RColorBrewer)
require(raster)
require(mapview)

# clean dataframe
xyz <- xyz[,-1]

# rasterize it according to the projection
r <- raster::rasterFromXYZ(xyz,crs = sp::CRS("+init=epsg:3035"))

# map it
p <- colorRampPalette(brewer.pal(8, "Reds"))
# aet resolution to 1 sqkm
mapview::mapviewOptions(mapview.maxpixels = r@ncols*r@nrows/10)
mapview::mapview(r, col.regions = p,
at = c(-1,10,25,50,100,500,1000,2500),
legend = TRUE)

### Setup GRASS Project

So far nothing new. Now we create a new but permanent GRASS gisbase using the spatial parameters from the raster object. As you know the linkGRASS7 function performs a full search for one or more than one existing GRASS installations. If a valid GRASS installation exists all parameter are setup und the package rgrass7 is linked.

Due to the fact that the gisdbase_exist is by default set to FALSE it will create a new structure according to the R object.

require(link2GI)
# initialize GRASS and set up a permanent structure
location = "microzensus2011")   

Finally we can now import the data to the GRASS gisdbase using the rgass7 package functionality.

First we must convert the raster object to GeoTIFF file. Any GDAL format is possible but GeoTIFF is very common and stable.

require(link2GI)
require(raster)
require(rgrass7)

# write it to geotiff
raster::writeRaster(r, paste0(path_run,"/Zensus_Bevoelkerung_100m-Gitter.tif"),
overwrite = TRUE)

# import raster to GRASS
rgrass7::execGRASS('r.external',
flags=c('o',"overwrite","quiet"),
input=paste0(path_run,"/Zensus_Bevoelkerung_100m-Gitter.tif"),
output="Zensus_Bevoelkerung_100m_Gitter",
band=1)

# check imported data set
rgrass7::execGRASS('r.info',
map = "Zensus_Bevoelkerung_100m_Gitter") 

Let’s do now the same import as a vector data set. First we create a sf object. Please note this will take quite a while.

 xyz_sf = st_as_sf(xyz,
coords = c("x_mp_100m", "y_mp_100m"),
crs = 3035,
agr = "constant")

#map points
sf::plot_sf(xyz_sf)

The GRASS gisdbase already exists. So we pass linkGRASS7 the argument gisdbase_exist=TRUE and import the xyz data as generic GRASS vector points.

 require(sf)
require(sp)
rgrass7::execGRASS('v.info', map = "Zensus_Bevoelkerung_100m_")