1  A First Look at Vector Data in R

The sf package offers a lot of different functionality for handling vector data, and interfaces with many different packages that extend our analytical and visualization capabilities. But before we get into these aspects, it’s probably useful to see how the Simple Features geometry works with some of the things we already know. In this section, we’ll work through importing data, making changes to it using some of the tidyverse functions, and visualizing it using ggplot2.

Reading vector data from shapefiles

By and large, the ESRI shapefile is the standard for storing and sharing vector data. Shapefiles are actually collections of multiple files that store different aspects of the data (e.g., the .prj file contains projection information), but the main file in this structure is the .shp file. To read this kind of file, we’ll use the st_read function:

Reading layer `SACharCores' from data source 
  `C:\Users\bdav_\Dropbox\Teaching\ENV 170\Lab Materials\Week8\Week8Lab\data\SACharCores.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 27 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 18.11663 ymin: -34.66926 xmax: 32.3673 ymax: -22.85796
Geodetic CRS:  WGS 84

These are locations of sedimentary cores containing microcharcoal that have been collected from sites around South Africa used to reconstruct fire history. The message above gives us some information about the dataset:

  • Where the data is being read in from and its file format (ESRI Shapefile)

  • It is being stored as a Simple Features collection with 27 features (cores) and 3 fields (variables)

  • The data has a POINT type geometry, meaning that it is a 0-dimensional geometry (e.g., it has no length, width, etc.)

  • Somewhat confusingly, it then gives a value called “Dimension”, but this refers to the number of dimensions in the coordinate space (here it is XY data) rather than the dimensionality of the objects

  • Bounding box: This is the spatial extent covered by the data, represented by minimum and maximum x and y values

  • The coordinate reference system (or CRS) refers to the system that is being used to In this case, it is longitude/latitudes based on the WGS 84 datum

We can access these attributes individualls

#Get the bounding box values
     xmin      ymin      xmax      ymax 
 18.11663 -34.66925  32.36730 -22.85796 
#Get the dimension
 [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

When we just enter the object name, we get a similar summary of information about the dataset, but, similarly to looking at a tibble, we also get a preview of the data itself:

Simple feature collection with 27 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 18.11663 ymin: -34.66926 xmax: 32.3673 ymax: -22.85796
Geodetic CRS:  WGS 84
First 10 features:
   SiteNo                SiteName SiteCode                  geometry
1     280             Craigrossie      CRA      POINT (28.46 -28.54)
2     322            Rietvlei Dam       RD      POINT (28.27 -25.88)
3     335               Lake Teza      TEZ  POINT (32.3007 -28.5117)
4     378                    Elim      ELI      POINT (28.41 -28.48)
5     455 Wonderkrater borehole 3      WON      POINT (28.75 -24.43)
6     598             Lake Sibaya      SIB  POINT (32.3673 -27.2065)
7     600                 Funduzi      FUN     POINT (30.3 -22.8583)
8     771               Braamhoek      BRA POINT (29.58333 -28.2333)
9     774         Moreletta River      MOR       POINT (28.3 -25.73)
10   1476          Tswaing Crater       TC POINT (28.0832 -25.40865)

These are the variables associated with our data (sometimes called attributes in GIS lingo). This is just a table of data with an extra column, geometry, that stores the spatial information. In fact, if we wanted to remove the spatial information and just use the associated data, the package offers a handy st_drop_geometry function, which will just leave you with a tibble of the variables:

   SiteNo                SiteName SiteCode
1     280             Craigrossie      CRA
2     322            Rietvlei Dam       RD
3     335               Lake Teza      TEZ
4     378                    Elim      ELI
5     455 Wonderkrater borehole 3      WON
6     598             Lake Sibaya      SIB
7     600                 Funduzi      FUN
8     771               Braamhoek      BRA
9     774         Moreletta River      MOR
10   1476          Tswaing Crater       TC
11   1479  Scot's Farm Borehole 1      SFB
12   1480              Tate Vondo       TV
13   1481                 Mahwaqa      MHQ
14    775     Cederberg Pakhuisch      PAK
15   1458            Verlorenvlei      VER
16   1459            Pearly Beach       PB
17   1460        Rietvlei Wetland       RW
18   1461         Eilandvlei lake      EIL
19   1462                De Rif-1      DR*
20   1463                De Rif-2     <NA>
21   1472           Princess Vlei      PRI
22   1473         Katbakkies Pass      KBP
23   1474              Groenkloof      GKF
24   1475               Platbos 1      PB1
25   1477              Pella 1_4a     <NA>
26   1478          Vankervelsvlei      VAN
27   1482               Pella 1_1     PEL*

Fields = Variables = Columns

Previous packages used for spatial work in R stored the data in complex ways, where each kind of data structure had its own object type, and accessing the variables might The sf package simplifies this process by treating all spatial information as a single variable, geometry, in an otherwise normal table of data.

Let’s look at another shapefile: biomes of southern Africa. This comes from the South Africa National Biodiversity Institute and is based on a 2006 classification scheme by Mucina and Rutherford.

Simple feature collection with 287 features and 7 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 16.45696 ymin: -34.8334 xmax: 32.89179 ymax: -22.12917
Geodetic CRS:  WGS 84
First 10 features:
1        8         Forests   Forests   31.367        FO         1   1062.17
2        8         Forests   Forests   74.086        FO         2   1062.17
3        6 Grassland Biome Grassland  606.697         G         3 360148.95
4        8         Forests   Forests   20.073        FO         4   1062.17
5        6 Grassland Biome Grassland   97.729         G         5 360148.95
6        6 Grassland Biome Grassland   55.420         G         6 360148.95
7        6 Grassland Biome Grassland   96.993         G         7 360148.95
8        6 Grassland Biome Grassland   35.279         G         8 360148.95
9        6 Grassland Biome Grassland   21.633         G         9 360148.95
10       6 Grassland Biome Grassland   27.021         G        10 360148.95
1  MULTIPOLYGON (((31.17252 -2...
2  MULTIPOLYGON (((30.01046 -2...
3  MULTIPOLYGON (((30.0337 -23...
4  MULTIPOLYGON (((30.02757 -2...
5  MULTIPOLYGON (((29.1999 -24...
6  MULTIPOLYGON (((28.84413 -2...
7  MULTIPOLYGON (((29.71844 -2...
8  MULTIPOLYGON (((29.0852 -24...
9  MULTIPOLYGON (((28.59739 -2...
10 MULTIPOLYGON (((28.5839 -24...

Here you can see a similar summary to the previous dataset, but a major distinction is that this is MULTIPOLYGON data, or 2-dimensional geometry (e.g., has length and width). The sf package distinguishes between POLYGON and MULTIPOLYGON data. In a POLYGON geometry, each individual polygon is a distinct feature, while in a MULTIPOLYGON geometry, multiple polygons might be included in a single feature.

You could think about it in terms of the main islands of Hawaii:

A POLYGON dataset might have separate features for Oahu, Maui, Kauai, and so on, while a MULTIPOLYGON dataset might have 8 polygons that are all a single object called “Hawaii”. For the biomes dataset, some biomes may include several disconnected components, but are here considered to be a single “multipolygon”.

Because the data are stored as a table, we can use pretty much any of the functions we would normally use to access and manipulate the data. Here, we’ll modify the data using filter and select to include only a subset of rows and columns:

savanna<-saBiomes |>
  filter(BIOMENAME=="Savanna") |>
Simple feature collection with 58 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 19.99944 ymin: -33.56281 xmax: 32.61765 ymax: -22.12917
Geodetic CRS:  WGS 84
First 10 features:
           BIOME POLYSQKM BIOMESQKM                       geometry
1  Savanna Biome   29.175  418564.4 MULTIPOLYGON (((28.57282 -2...
2  Savanna Biome   91.943  418564.4 MULTIPOLYGON (((27.74764 -2...
3  Savanna Biome   22.009  418564.4 MULTIPOLYGON (((28.11957 -2...
4  Savanna Biome   82.667  418564.4 MULTIPOLYGON (((28.11963 -2...
5  Savanna Biome  703.326  418564.4 MULTIPOLYGON (((27.89195 -2...
6  Savanna Biome  191.453  418564.4 MULTIPOLYGON (((28.38203 -2...
7  Savanna Biome  187.384  418564.4 MULTIPOLYGON (((28.64878 -2...
8  Savanna Biome  209.071  418564.4 MULTIPOLYGON (((26.68986 -2...
9  Savanna Biome  146.613  418564.4 MULTIPOLYGON (((28.5071 -26...
10 Savanna Biome   22.608  418564.4 MULTIPOLYGON (((27.48282 -2...

And because the data are in table format, we can also integrate them into a ggplot function to quickly view patterns within the data. For example, let’s say we wanted to assess the total square kilometers in the Fynbos and Savanna biomes:

biomeCompare<-filter(saBiomes,BIOMENAME %in% c("Fynbos","Savanna"))
ggplot(biomeCompare,aes(x=BIOMENAME,y=POLYSQKM)) +

Visualizing spatial data with geom_sf

Over the years, the developers of ggplot2 have recognized widespread interest in using the package to make maps, and have updated the software to include handling and plotting of spatial data At the same time, efforts like the promotion of the Simple Features data structure are a recognition that data interoperability is a priority for many data users.

These two processes meet in the middle with the geom_sf object. This is a way of translating Simple Features data (like that from the sf package) into a format that can be manipulated in the ggplot2 environment. For example

ggplot(data= saCores) + 

This probably looks very similar to what you might expect from geom_point, and this makes sense since we’re dealing with point data. However, geom_sf is designed to handle vector data in a range of formats. For example, we can use the same function call to plot our polygon biomes data:

ggplot(data= saBiomes) + 

What’s important to keep in mind here is that geom_sf is taking into account the information in the geometry column of the object. It recognizes that this is polygon data, so it plots it accordingly.

Now that the data are in a ggplot function, we can add some aesthetic mappings. For starters, let’s map the biomes on to the color variable:

ggplot(data= saBiomes,aes(fill=BIOME)) + 

That looks pretty good, but there are some things that aren’t really working for this map. For example, the grey background is pretty unnecessary in this case, so we can get rid of it using a theme like theme_classic or theme_minimal.

We also probably don’t need the dark grey borders around each polygon; in places where there’s a lot of lines close together it gets pretty messy. We can eliminate these by adding color=NA to the geom_sf function. This is saying to not plot the elements to which color would be mapped. Since it’s being applied over the whole plot, and not relative to some variable in the data, we don’t need the aes function.

Finally, we can also add a label to the legend to edit the capitalization on the variable name.

ggplot(data= saBiomes,aes(fill=BIOME)) + 
  geom_sf(color=NA) +
  theme_classic() +

Much better. Keep in mind right now we’re mapping a nominal (categorical) value to fill, but we could just as easily use a ratio/interval (continuous) value. For example, instead of using the name of the biome, we could color by the total area in square kilometers:

ggplot(data= saBiomes,aes(fill=BIOMESQKM)) + 
  geom_sf(color=NA) +
  theme_classic() +
  labs(fill=bquote("Biome Area"~(km^2)))

Notice the use of the bquote function to modify the label to include the superscripted 2, which we learned about in Week 7.

OK, back to our biomes map. The color scheme is a bit intense: when working with mapped data that covers large areas of graphic, it’s usually a good idea to use more muted tones. To do this, we’re going to draw on our friend ColorBrewer with the scale_fill_brewer function:

ggplot(data= saBiomes,aes(fill=BIOME)) + 
  geom_sf(color=NA) +
  theme_classic() +
  labs(fill="Biome") +

Now let’s say we want to add the cores as a layer to this map. We can do this by adding another geom_sf call, but this time using the cores object as the data argument:

ggplot(data= saBiomes,aes(fill=BIOME)) + 
  geom_sf(color=NA) +
  theme_classic() +
  labs(fill="Biome") +
  scale_fill_brewer(palette="Set3") +
Error in `geom_sf()`:
! Problem while computing aesthetics.
ℹ Error occurred in the 2nd layer.
Caused by error:
! object 'BIOME' not found

Oops. What happened? This error relates to the way we’re doing the aesthetic mapping. At the moment, the aes function is inside the opening ggplot function, so this mapping is being applied to every geom underneath it. But since we now have geoms coming from two different datasets (biomes and cores), the mapping won’t make sense because the cores data has different variables.

When you’re dealing with more than one dataset, it’s good practice to put the arguments in the geoms they are referencing. So here, we’ll move the arguments from the ggplot function into the first geom_sf function:

ggplot() + 
  geom_sf(data= saBiomes,aes(fill=BIOME),color=NA) +
  theme_classic() +
  labs(fill="Biome") +
  scale_fill_brewer(palette="Set3") +

Lovely. Keep in mind that the order in which you place the geoms will determine their drawing order of each layer. So, for example, if we moved the geom_sf that plots the cores up to the top of the function, our cores will be hidden behind the biomes data that gets plotted immediately afterwards:

ggplot() + 
  geom_sf(data=saCores) +
  geom_sf(data= saBiomes,aes(fill=BIOME),color=NA) +
  theme_classic() +
  labs(fill="Biome") +

We can keep modifying this image until we’re happy with how it looks, just as we have with other kinds of plots. But just by adding a few new function to our toolkit, like st_read and geom_sf, we’re able to incorporate spatial data into our work.

Try it yourself!

There’s an additional shapefile in here called south_africa_border, which gives the borders of South Africa, Lesotho, and Eswatini. Using what you’ve learned above, read this file in, plot it and then add it the plot with the biomes and the cores.