This lesson is still being designed and assembled (Pre-Alpha version)

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Working with Geospatial Data

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Convert from .csv to a Shapefile in R

Overview

Teaching: 40 min
Exercises: 20 min
Questions

  • How can I import CSV files as shapefiles in R?

Objectives

  • Import .csv files containing x,y coordinate locations into R as a data frame.

  • Convert a data frame to a spatial object.

  • Export a spatial object to a text file.

Things You’ll Need To Complete This Episode

See the lesson homepage for detailed information about the software, data, and other prerequisites you will need to work through the examples in this episode.

This episode will review how to import spatial points stored in .csv (Comma Separated Value) format into R as an sf spatial object. We will also reproject data imported from a shapefile format, export this data as a shapefile, and plot raster and vector data as layers in the same plot.

Spatial Data in Text Format

The fish_tracks.csv file contains x, y (point) locations from a fish tracking study in Lake Erie.

We would like to:

Spatial data are sometimes stored in a text file format (.txt or .csv). If the text file has an associated x and y location column, then we can convert it into an sf spatial object. The sf object allows us to store both the x,y values that represent the coordinate location of each point and the associated attribute data - or columns describing each feature in the spatial object.

We will continue using the sf package in this episode.

Import .csv

To begin let’s import a .csv file that contains fish tracking coordinate x, y locations in Lake Erie (fish_tracks.csv) and look at the structure of that new object:

fish_tracks <-
  read.csv("data/fish_tracks.csv")

str(fish_tracks)

'data.frame':	1366 obs. of  6 variables:
 $ animal_id    : int  16176001 16176001 16176001 16176001 16176001 16176001 16176001 16176001 16176001 16176001 ...
 $ bin_timestamp: Factor w/ 1226 levels "2014-05-06 04:59:17",..: 1 2 2 3 4 5 6 7 8 9 ...
 $ record_type  : Factor w/ 2 levels "detection","interpolated": 1 1 1 2 1 2 2 2 2 2 ...
 $ X            : num  335496 334227 335496 334107 335496 ...
 $ Y            : num  4614819 4609585 4614819 4611017 4614819 ...
 $ utmZone      : int  17 17 17 17 17 17 17 17 17 17 ...

We now have a data frame that contains 1366 location observations (rows) and 6 variables (attributes). Note that all of our character data was imported into R as factor (categorical) data. Next, let’s explore the dataframe to determine whether it contains columns with coordinate values. If we are lucky, our .csv will contain columns labeled:

Let’s check out the column names of our dataframe.

names(fish_tracks)

[1] "animal_id"     "bin_timestamp" "record_type"   "X"            
[5] "Y"             "utmZone"

Identify X,Y Location Columns

Our column names include several fields that might contain spatial information. The fish_tracks$X and fish_tracks$Y columns contain coordinate values. We can confirm this by looking at the first six rows of our data.

head(fish_tracks$X)

[1] 335496.0 334227.4 335496.0 334107.1 335496.0 335496.0

head(fish_tracks$Y)

[1] 4614819 4609585 4614819 4611017 4614819 4614819

We have coordinate values in our data frame. In order to convert our data frame to an sf object, we also need to know the CRS associated with those coordinate values.

There are several ways to figure out the CRS of spatial data in text format.

  1. We can check the file metadata in hopes that the CRS was recorded in the data.
  2. We can explore the file itself to see if CRS information is embedded in the file header or somewhere in the data columns.

It is not typical to store CRS information in a column. But this particular file contains CRS information this way. The utmZone column contain the information that helps us determine the CRS:

In When Vector Data Don’t Line Up - Handling Spatial Projection & CRS in R we learned about the components of a proj4 string. We have everything we need to assign a CRS to our data frame.

To create the proj4 associated with UTM Zone 17 WGS84 we can look up the projection on the Spatial Reference website, which contains a list of CRS formats for each projection. From here, we can extract the proj4 string for UTM Zone 17N WGS84.

However, if we have other data in the UTM Zone 17N projection, it’s much easier to use the st_crs() function to extract the CRS in proj4 format from that object and assign it to our new spatial object. We’ve seen this CRS before with our Lake Erie outline (erie_outline).

st_crs(erie_outline)

Coordinate Reference System:
  No EPSG code
  proj4string: "+proj=utm +zone=17 +ellps=GRS80 +units=m +no_defs"

The output above shows that the points shapefile is in UTM zone 17N. We can thus use the CRS from that spatial object to convert our non-spatial dataframe into an sf object.

Next, let’s create a crs object that we can use to define the CRS of our sf object when we create it.

utm17nCRS <- st_crs(erie_outline)
utm17nCRS

Coordinate Reference System:
  No EPSG code
  proj4string: "+proj=utm +zone=17 +ellps=GRS80 +units=m +no_defs"

class(utm17nCRS)

[1] "crs"

.csv to sf object

Next, let’s convert our dataframe into an sf object. To do this, we need to specify:

  1. The columns containing X (easting) and Y (northing) coordinate values
  2. The CRS that the column coordinate represent (units are included in the CRS) - stored in our utmCRS object.

We will use the st_as_sf() function to perform the conversion.

fish_locations <- st_as_sf(fish_tracks, coords = c("X", "Y"), 
                                   crs = utm17nCRS)

We should double check the CRS to make sure it is correct.

st_crs(fish_locations)

Coordinate Reference System:
  No EPSG code
  proj4string: "+proj=utm +zone=17 +ellps=GRS80 +units=m +no_defs"

Plot Spatial Object

We now have a spatial R object, we can plot our newly created spatial object.

ggplot() +
  geom_sf(data = fish_locations) +
  ggtitle("Map of Fish Locations")

plot of chunk plot-data-points

Plot Extent

In Open and Plot Shapefiles in R we learned about spatial object extent. When we plot several spatial layers in R using ggplot, all of the layers of the plot are considered in setting the boundaries of the plot. To show this, let’s plot our erie_outline object with fish tracking point layer.

ggplot() +
  geom_sf(data = erie_outline) +
  geom_sf(data = fish_locations) +
  ggtitle("Fishing Tracking Locations")

plot of chunk plot-data

When we plot the two layers together, ggplot sets the plot boundaries so that they are large enough to include all of the data included in all of the layers. That’s really handy!

Export a Shapefile

We can write an R spatial object to a shapefile using the st_write function in sf. To do this we need the following arguments:

We can now export the spatial object as a shapefile.

st_write(fish_locations,
         "data/fish_locations.shp", driver = "ESRI Shapefile")

Key Points

  • Know the projection (if any) of your point data prior to converting to a spatial object.

  • Convert a data frame to an sf object using the st_as_sf() function.

  • Export an sf object as text using the st_write() function.

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