Reading in a raster file
Raster files are most easily read in to R with the raster() function from the raster package. You simply pass in the filename (including the extension) of the raster as the first argument, x.
The raster() function uses some native raster package functions for reading in certain file types (based on the extension in the file name) and otherwise hands the reading of the file on to readGDAL() from the rgdal package. The benefit of not using readGDAL() directly is simply that raster() returns a RasterLayer object.
A common kind of raster file is the GeoTIFF, with file extension .tif or .tiff. We've downloaded a median income raster from the US census and put it in your working directory.
Let's take a look and read it in.
This is a part of the course
“Visualizing Geospatial Data in R”
Exercise instructions
- Use
dir()to take a look in your working directory. - Use
dir()again to look inside the directorynyc_grid_data. - Use
raster()to read in the median income raster to the variableincome_gridby passing in the complete path to the.tiffile. - Use
summary()to verify the raster is stored in aRasterLayer. - Use
plot()to verify the raster's contents.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
library(raster)
# Call dir()
# Call dir() on the directory
# Use raster() with file path: income_grid
# Call summary() on income_grid
# Call plot() on income_grid
This exercise is part of the course
Visualizing Geospatial Data in R
Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.
In this chapter you'll follow the creation of a visualization from raw spatial data files to adding a credit to a map. Along the way, you'll learn how to read spatial data into R, more about projections and coordinate reference systems, how to add additional data to a spatial object, and some tips for polishing your maps.
Exercise 1: Reading in spatial dataExercise 2: Reading in a shapefileExercise 3: Reading in a raster fileExercise 4: Getting data using a packageExercise 5: Coordinate reference systemsExercise 6: Merging data from different CRS/projectionsExercise 7: Converting from one CRS/projection to anotherExercise 8: Adding data to spatial objectsExercise 9: The wrong wayExercise 10: Checking data will matchExercise 11: Merging data attributesExercise 12: A first plotExercise 13: Polishing a mapExercise 14: Subsetting the neighborhoodsExercise 15: Adding neighborhood labelsExercise 16: Tidying up the legend and some final tweaksExercise 17: Wrap upWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.