Overlaying a Shapefile on a Filled Contour Plot

In this article, we will explore how to overlay the outline of a polygon from a shapefile onto a filled contour plot created using the raster package in R. The filledContour function is used to create filled contour plots, but unfortunately, it doesn’t support adding lines or polygons directly.

We’ll take a step-by-step approach to achieve this by leveraging other functions and features within the raster package, as well as some additional packages like rgdal. We will also cover how to handle common issues that may arise during the process.

Background

The raster package is used for geospatial data analysis in R. It provides a wide range of tools for working with raster data, including conversion, filtering, and plotting. The filledContour function creates filled contour plots, which are useful for visualizing the distribution of values within a raster dataset.

On the other hand, the rgdal package is used for geospatial data manipulation in R. It provides an interface to various geodatabases and formats, including shapefiles.

The filledContour function doesn’t support adding lines or polygons directly because it’s primarily designed to work with single-band raster data. However, we can use other functions within the raster package and the rgdal package to achieve our goal.

Method 1: Using Plot.Axes

The first method involves using the plot.axes argument in the filledContour function to draw lines or polygons on top of the filled contour plot. Unfortunately, this approach doesn’t seem to work as expected according to the original question.

Here’s an example code block that attempts to use the plot.axes argument:

library(raster)
library(rgdal)

test_raster <- raster("test_raster.grd")
test_Shapefile <- readOGR("test_shapefile.shp")

test_filled_contour <- filledContour(test, maxpixels = 100000,
                                    plot.axes = {
                                      axis(1) # x-axis
                                      axis(2)
                                      map(test_Shapefile, add=T) # Add the shapefile map to the plot
                                    })

This code block attempts to use the plot.axes argument to draw lines or polygons on top of the filled contour plot. However, it doesn’t seem to work as expected according to the original question.

Method 2: Using the Plot Function

The second method involves using the plot function directly to create a filled contour plot and then add the shapefile map using the map function from the rgdal package. This approach provides more control over the appearance of the plot.

Here’s an example code block that uses this approach:

library(raster)
library(rgdal)

test_raster <- raster("test_raster.grd")
test_Shapefile <- readOGR("test_shapefile.shp")

# Create a new axes for plotting
plot.axes = function(xlim, ylim) {
  axis(1) # x-axis
  axis(2)
  
  # Draw lines or polygons on top of the plot using the map function from rgdal
  map(test_Shapefile, add=T)
}

# Plot the filled contour and draw lines/polygons on top
plot(test_raster,
     plot.axes = plot.axes,
     main="Filled Contour Plot with Shapefile",
     xlab="X-axis", ylab="Y-axis")

This code block uses the plot function directly to create a filled contour plot. It then defines a new plot.axes function that draws lines or polygons on top of the plot using the map function from the rgdal package.

Method 3: Using the Sp Plot Function

The third method involves using the sp.plot function directly to create a filled contour plot and then add the shapefile map. This approach is similar to the previous method but provides more control over the appearance of the plot.

Here’s an example code block that uses this approach:

library(raster)
library(rgdal)

test_raster <- raster("test_raster.grd")
test_Shapefile <- readOGR("test_shapefile.shp")

# Plot the filled contour and draw lines/polygons on top using sp.plot
sp.plot(
  test_raster,
  map(test_Shapefile, add=T),
  main="Filled Contour Plot with Shapefile",
  xlab="X-axis", ylab="Y-axis"
)

This code block uses the sp.plot function directly to create a filled contour plot. It then adds the shapefile map using the map function from the rgdal package.

Conclusion

In this article, we explored three different methods for overlaying a shapefile on top of a filled contour plot created using the raster package in R. The first method attempted to use the plot.axes argument, which didn’t seem to work as expected. The second and third methods used the plot function and sp.plot function directly, respectively, to create a filled contour plot and add the shapefile map.

Each of these methods provides more control over the appearance of the plot and can be customized according to specific needs. By using these methods, users can achieve their desired results and gain a deeper understanding of the capabilities of the raster package in R.

Common Issues

One common issue that may arise during this process is when the shapefile and raster datasets do not use the same coordinate system. In such cases, it’s essential to ensure that both datasets are projected onto the same coordinate system using the proj4string function from the sp package.

Here’s an example code block that demonstrates how to project a shapefile dataset onto the same coordinate system as the raster dataset:

library(raster)
library(rgdal)
library(sp)

test_raster <- raster("test_raster.grd")
test_Shapefile <- readOGR("test_shapefile.shp")

# Project the shapefile dataset onto the same coordinate system as the raster dataset
proj4string(test_Shapefile) <- proj4string(test_raster)

plot(test_raster,
     main="Filled Contour Plot with Shapefile",
     xlab="X-axis", ylab="Y-axis")

This code block projects the shapefile dataset onto the same coordinate system as the raster dataset using the proj4string function from the sp package. This ensures that both datasets are projected onto the same coordinate system, which is essential for achieving accurate results.

Conclusion

In conclusion, overlaying a shapefile on top of a filled contour plot created using the raster package in R can be achieved by leveraging various functions and features within the package. By using these methods, users can create high-quality plots that provide valuable insights into their data.

We hope this article has provided you with a deeper understanding of how to achieve your desired results using the raster package in R.

Last modified on 2023-10-09