add description how this can be added to the .Renviron / .Rprofile

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add description how this can be added to the .Renviron / .Rprofile

book/01_Making_Maps/Static_Maps.Rmd

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/ todo: add description how this can be added to the .Renviron / .Rprofile

For this chapter, you will need the following packages and datasets:

```{r}
library(arc2r) 
library(sf)
library(raster)

data("gemeinde_zh")
data("swissAlti3D")

As we saw in chapter @ref(importing-vector-sf), we can quickly visualize an sf object by simply calling plot. This will return a small multiple of your geodata, where each facet is colour coded based on a column.

plot(gemeinde_zh)

You will get one facet for each column, where the maximum number of facets is set to 9 by default. I find this behaviour pretty annoying, since I usually just want get a quick look at the data in space, and these small multiples take ages to render. There are several workarounds to prevent small multiples:

1. Extract the geometry from the sf object and just plot that
2. Specify the column which should colourcode your data
3. Specify max.plot = 1 to just plot 1 facet (using the first column in your dataset for colour coding)
4. Set the option sf_max.plot to 1 with Option(sf_max.plot = 1) (this will persist throughout your R Session)

plot(st_geometry(gemeinde_zh))  # 1
plot(gemeinde_zh["bezirksnam"]) # 2
plot(gemeinde_zh, max.plot = 1) # 3

options(sf_max.plot = 1)        # 4
plot(gemeinde_zh)

We dont have the "problem" of facets with raster data, so we can simply call plot() on a raster object to visualize it.

plot(swissAlti3D)

The plot() method is fine for quick view of the data, but as you want to add more layers, legend etc. plot() becomes complicated. There are many alternative methods to visualizing spatial data, we will showcase our favourite methods here.

Package ggplot2

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