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| 0.13.0 | ||
| 0.16.2 |
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| --- | ||
| title: "Open and Plot Vector Layers" | ||
| teaching: 25 | ||
| exercises: 5 | ||
| --- | ||
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| :::::::::::::::::::::::::::::::::::::: questions | ||
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| - How can I read, examine and visualize point, line and polygon vector data in R? | ||
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| :::::::::::::::::::::::::::::::::::::::::::::::: | ||
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| ::::::::::::::::::::::::::::::::::::: objectives | ||
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| - Know the difference between point, line, and polygon vector data. | ||
| - Load vector data into R. | ||
| - Access the attributes of a vector object in R. | ||
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| :::::::::::::::::::::::::::::::::::::::::::::::: | ||
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| :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor | ||
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| Make sure that the `sf` package and its dependencies are installed before the | ||
| workshop. The installation can take quite some time, so allocate enough extra | ||
| time before the workshop for solving installation problems. We recommend one | ||
| or two installation 'walk-in' hours on a day before the workshop and 15-30 | ||
| minutes at the beginning of the first workshop day should be enough to tackle | ||
| installation issues. | ||
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| :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: | ||
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| ::: prereq | ||
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| If you have not installed the `sf` package yet, run `install.packages("sf")` first. Note that the `sf` package has some external dependencies, namely GEOS, PROJ.4, GDAL and UDUNITS, which need to be installed beforehand. Follow the workshop [setup instructions]() for the installation of `sf` and its dependencies. | ||
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| ::: | ||
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| First we need to load the packages we will use in this lesson. We will use the packages `tidyverse` and `here` with which you are already familiar from the previous lesson. In addition, we need to load the [`sf`](https://r-spatial.github.io/sf/) package for working with spatial vector data. | ||
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| ```{r lib, message=FALSE} | ||
| library(tidyverse) # tools for wrangling, reshaping and visualizing data | ||
| library(here) # managing paths | ||
| library(sf) # work with spatial vector data | ||
| ``` | ||
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| ::: callout | ||
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| # The `sf` package | ||
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| `sf` stands for Simple Features which is a standard defined by the Open Geospatial Consortium for storing and accessing geospatial vector data. PostGIS uses the same standard; so those of you who used PostGIS, might find some resemblances with the functions used by the `sf` package. | ||
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| ::: | ||
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| ## Import shapefiles | ||
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| Let's start by opening a shapefile. Shapefiles a common file format to store spatial vector data used in GIS software. We will read a shapefile with the administrative boundary of Delft with the function `st_read()` from the `sf` package. | ||
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| ```{r results='hide'} | ||
| boundary_Delft <- st_read("data/delft-boundary.shp") | ||
| ``` | ||
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| ::: callout | ||
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| # All `sf` functions start with `st_` | ||
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| Note that all functions from the `sf` package start with the standard prefix `st_` which stands for Spatial Type. This is helpful in at least two ways: (1) it makes the interaction with or translation to/from software using the simple features standard like PostGIS easy, and (2) it allows for easy autocompletion of function names in RStudio. | ||
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| ::: | ||
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| ## Spatial Metadata | ||
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| The `st_read()` function gave us a message with a summary of metadata about the file that was read in. To examine the metadata in more detail, we can use other, more specialised, functions from the `sf` package. The `st_geometry_type()` function, for instance, gives us information about the geometry type, which in this case is `POLYGON`. | ||
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| ```{r} | ||
| st_geometry_type(boundary_Delft) | ||
| ``` | ||
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| The `st_crs()` function returns the coordinate reference system (CRS) used by the shapefile, which in this case is `WGS84` and has the unique reference code `EPSG: 4326`. | ||
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| ```{r} | ||
| st_crs(boundary_Delft) | ||
| ``` | ||
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| ::: callout | ||
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| # Examining the output of `st_crs()` | ||
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| As the output of `st_crs()` can be long, you can use `$Name` and `$epsg` after the `crs()` call to extract the projection name and EPSG code respectively. | ||
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| ::: | ||
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| The `st_bbox()` function shows the extent of the layer. As `WGS84` is a **geographic CRS**, the extent of the shapefile is displayed in degrees. | ||
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| ```{r} | ||
| st_bbox(boundary_Delft) | ||
| ``` | ||
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| We need a **projected CRS**, which in the case of the Netherlands is typically the Amersfort / RD New projection. To reproject our shapefile, we will use the `st_transform()` function. For the `crs` argument we can use the EPSG code of the CRS we want to use, which is `28992` for the `Amersfort / RD New` projection. | ||
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| ```{r} | ||
| boundary_Delft <- st_transform(boundary_Delft, 28992) | ||
| st_crs(boundary_Delft) | ||
| ``` | ||
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| Notice that the bounding box is measured in meters after the transformation. | ||
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| ```{r} | ||
| st_bbox(boundary_Delft) | ||
| ``` | ||
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| We confirm the transformation by examining the reprojected shapefile. | ||
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| ```{r} | ||
| boundary_Delft | ||
| ``` | ||
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| ::: callout | ||
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| More about CRS in [Handling Spatial Projection & CRS](). | ||
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| ::: | ||
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| ## Plot a vector layer | ||
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| Now, let's plot this shapefile. You are already familiar with the `ggplot2` package from [Introduction to Visualisation](). `ggplot2` has special `geom_` functions for spatial data. We will use the `geom_sf()` function for `sf` data. | ||
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| ```{r} | ||
| ggplot(data = boundary_Delft) + | ||
| geom_sf(size = 3, color = "black", fill = "cyan1") + | ||
| labs(title = "Delft Administrative Boundary") + | ||
| coord_sf(datum = st_crs(28992)) # this is needed to display the axes in meters | ||
| ``` | ||
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| ::::::::::::::::::::::::::::::::::::: challenge | ||
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| ### Challenge 1: Import line and point vector layers | ||
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| Read in `delft-streets.shp` and `delft-leisure.shp` and assign them to `lines_Delft` and `point_Delft` respectively. Answer the following questions: | ||
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| 1. What type of R spatial object is created when you import each layer? | ||
| 2. What is the CRS and extent for each object? | ||
| 3. Do the files contain points, lines, or polygons? | ||
| 4. How many features are in each file? | ||
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| :::::::::::::::::::::::: solution | ||
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| ```{r results='hide'} | ||
| lines_Delft <- st_read("data/delft-streets.shp") | ||
| point_Delft <- st_read("data/delft-leisure.shp") | ||
| ``` | ||
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| We can check the type of data with the `class()` function from base R. Both `lines_Delft` and `point_Delft` are objects of class `"sf"`, which extends the `"data.frame"` class. | ||
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| ```{r} | ||
| class(lines_Delft) | ||
| class(point_Delft) | ||
| ``` | ||
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| `lines_Delft` and `point_Delft` are in the correct CRS. | ||
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| ```{r} | ||
| st_crs(lines_Delft)$epsg | ||
| st_crs(point_Delft)$epsg | ||
| ``` | ||
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| When looking at the bounding boxes with the `st_bbox()` function, we see the spatial extent of the two objects in a projected CRS using meters as units. `lines_Delft()` and `point_Delft` have similar extents. | ||
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| ```{r} | ||
| st_bbox(lines_Delft) | ||
| st_bbox(point_Delft) | ||
| ``` | ||
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| ::::::::::::::::::::::::::::::::: | ||
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| :::::::::::::::::::::::::::::::::::::::::::::::: | ||
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| ::::::::::::::::::::::::::::::::::::: keypoints | ||
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| - Metadata for vector layers include geometry type, CRS, and extent. | ||
| - Load spatial objects into R with the `st_read()` function. | ||
| - Spatial objects can be plotted directly with `ggplot` using the `geom_sf()` function. No need to convert to a data frame. | ||
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| :::::::::::::::::::::::::::::::::::::::::::::::: |
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