Convert from .csv to a Vector Layer

Last updated on 2024-03-12 | Edit this page

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Overview

Questions

  • How can I import CSV files as vector layers 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.

You’ll need to load the following libraries

R 

library(ggplot2)
library(dplyr)
library(sf)

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 plot it and save the data as an ESRI shapefile.

Spatial Data in Text Format

In the Intro to R for Geospatial lessons, we worked with data from Maine DMR urchin surves and Steneck/Rasher lab surveys of kelp forests up and down the coast of Maine. This data contains x, y (point) locations for study sites in the form of the variables longitude and latitude.

We would like to:

  • Create a map of these site locations.
  • Create a map showing the coastline as a reference
  • Export the data in an ESRI shapefile format to share with our colleagues. This shapefile can be imported into most GIS software.

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.

Import .csv

To begin let’s import a .csv file that contains site coordinate locations from these subtidal locations and look at the structure of that new object:

R 

dmr <- 
  read.csv("data/maine_dmr/dmr_kelp_urchin.csv")

str(dmr)

OUTPUT 

'data.frame':	1478 obs. of  15 variables:
 $ year         : int  2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 ...
 $ region       : chr  "York" "York" "York" "York" ...
 $ exposure.code: int  4 4 4 4 4 2 2 3 2 2 ...
 $ coastal.code : int  3 3 3 4 3 2 2 3 2 2 ...
 $ latitude     : num  43.1 43.3 43.4 43.5 43.5 ...
 $ longitude    : num  -70.7 -70.6 -70.4 -70.3 -70.3 ...
 $ depth        : int  5 5 5 5 5 5 5 5 5 5 ...
 $ crust        : num  60 75.5 73.5 63.5 72.5 6.1 31.5 31.5 40.5 53 ...
 $ understory   : num  100 100 80 82 69 38.5 74 96.5 60 59.5 ...
 $ kelp         : num  1.9 0 18.5 0.6 63.5 92.5 59 7.7 52.5 29.2 ...
 $ urchin       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ month        : int  6 6 6 6 6 6 6 6 6 6 ...
 $ day          : int  13 13 14 14 14 15 15 15 8 8 ...
 $ survey       : chr  "dmr" "dmr" "dmr" "dmr" ...
 $ site         : int  42 47 56 61 62 66 71 70 23 22 ...

We now have a data frame that contains 1478 locations (rows) and 15 variables (attributes). Note that all of our character data was imported into R as character (text) 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:

  • “X” and “Y” OR
  • Latitude and Longitude OR
  • easting and northing (UTM coordinates)

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

R 

names(dmr)

OUTPUT 

 [1] "year"          "region"        "exposure.code" "coastal.code" 
 [5] "latitude"      "longitude"     "depth"         "crust"        
 [9] "understory"    "kelp"          "urchin"        "month"        
[13] "day"           "survey"        "site"

Identify X,Y Location Columns

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

R 

head(dmr$longitude)

OUTPUT 

[1] -70.66749 -70.55645 -70.38046 -70.32038 -70.30401 -70.10721

R 

head(dmr$latitude)

OUTPUT 

[1] 43.06709 43.28014 43.40849 43.50783 43.52791 43.72766

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.

In our case, as we have decimal degrees, this is likely a standard WGS 84 defined under EPSG code 4326. However, it always behoves you to check!

If we had had columns like easting and northing columns, then we are likely dealing with UTM or otherwise. Check if there is a geodeticDatum and a utmZone column. These appear to contain CRS information (datum and projection). Or, again, check the metadata for the data set.

In When Vector Data Don’t Line Up - Handling Spatial Projection & CRS in R we learned about the components of a proj4 string and EPSG. We have everything we need to assign a CRS to our data frame. If we wanted, we could use another loaded shapefile to extract a CRS and use it here. That is not needed, however, as sf lets us use EPSG codes.

.csv to sf object

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

  1. The columns containing X (longitude) and Y (latitude) coordinate values
  2. The CRS. Either as an object or an EPSG code.

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

R 

dmr_sf <- st_as_sf(dmr,
                   coords = c("longitude", "latitude"),
                   crs = 4326)

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

R 

st_crs(dmr_sf)

OUTPUT 

Coordinate Reference System:
  User input: EPSG:4326 
  wkt:
GEOGCRS["WGS 84",
    ENSEMBLE["World Geodetic System 1984 ensemble",
        MEMBER["World Geodetic System 1984 (Transit)"],
        MEMBER["World Geodetic System 1984 (G730)"],
        MEMBER["World Geodetic System 1984 (G873)"],
        MEMBER["World Geodetic System 1984 (G1150)"],
        MEMBER["World Geodetic System 1984 (G1674)"],
        MEMBER["World Geodetic System 1984 (G1762)"],
        MEMBER["World Geodetic System 1984 (G2139)"],
        ELLIPSOID["WGS 84",6378137,298.257223563,
            LENGTHUNIT["metre",1]],
        ENSEMBLEACCURACY[2.0]],
    PRIMEM["Greenwich",0,
        ANGLEUNIT["degree",0.0174532925199433]],
    CS[ellipsoidal,2],
        AXIS["geodetic latitude (Lat)",north,
            ORDER[1],
            ANGLEUNIT["degree",0.0174532925199433]],
        AXIS["geodetic longitude (Lon)",east,
            ORDER[2],
            ANGLEUNIT["degree",0.0174532925199433]],
    USAGE[
        SCOPE["Horizontal component of 3D system."],
        AREA["World."],
        BBOX[-90,-180,90,180]],
    ID["EPSG",4326]]

Plot Spatial Object

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

R 

ggplot() +
  geom_sf(data = dmr_sf,
          mapping = aes(color = region)) +
  ggtitle("Map of Site Locations")

Looks good! If we really want to check, we can either load up our state of Maine shapefile or plot it against a tiles with leaflet.

R 

library(leaflet)

pal <- colorFactor("Set1",
                   domain = dmr_sf$region)

leaflet() |>
  addTiles() |>
  addCircles(data = dmr_sf,
             color = ~pal(region))

Challenge - Import & Plot Additional Points

Load just Casco Bay and plot it along with the Maine coastal shapefile. Use the Casco data to crop the Maine shapefile and plot them together.

R 

# Load Casco
casco_dmr <- read.csv(
  "data/maine_dmr/casco_kelp_urchin.csv"
)


# Turn it into an sf object
casco_dmr_sf <- st_as_sf(casco_dmr,
                         coords = c("longitude", "latitude"),
                         crs = 4326)

# Load Maine
maine <- st_read(
  "data/maine_gov_maps/Maine_State_Boundary_Polygon_Feature/Maine_State_Boundary_Polygon_Feature.shp",
  quiet = TRUE)

# Crop to Casco
casco <- st_crop(maine |> st_make_valid(), 
                 casco_dmr_sf)

# Plot!
ggplot() +
  geom_sf(data = casco) +
  geom_sf(data = casco_dmr_sf, color = "red")

Key Points

Sometimes, we want to crop to a larger area than just the data set. For that, we can create a box from the extent of the new vector object using st_bbox(). This, though, is really just a vector, so we need to turn it into a polygon using st_sfc() (sfc objects are just a raw shape, while sf contains data).

To make this box bigger, we can use st_buffer() which will create a buffer area using a distance specified in meters. So, 1e4 would be 10km.

This technique can be a nice way to put a new vector file in context, as follows.

R 

#Make a bounding box of the Casco Bay area from the data
casco_bbox <- st_bbox(casco_dmr_sf) |>
  st_as_sfc()

# Enlarge it by 10 km
casco_bbox_big <- st_buffer(casco_bbox, 
                            dist = 1e4)

# Crop to the new area
casco <- st_crop(maine |> st_make_valid(), 
                 casco_bbox_big)

# Plot!
ggplot() +
  geom_sf(data = casco, fill = "darkgrey") +
  geom_sf(data = casco_dmr_sf, color = "red")

Export to an ESRI shapefile

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

  • the name of the spatial object (dmr_sf)
  • the directory where we want to save our ESRI shapefile (to use current = getwd() or you can specify a different path). You can also use dir.create() no make a new directory.
  • the name of the new ESRI shapefile (dmr_kelp_urchins)
  • the driver which specifies the file format (ESRI Shapefile)

We can now export the spatial object as an ESRI shapefile. Note - this will make a few files.

R 

dir.create("data/dmr_kelp_urchins")
st_write(dmr_sf,
         "data/dmr_kelp_urchins/dmr_kelp_urchins.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.