R Tutorial: Getting to know your raster data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/spatial-analysis-with-sf-and-raster-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In this lesson, we will explore raster data. Once you read in your raster layers with either the raster() or brick() functions you'll notice that they will be stored as objects with the class RasterLayer or RasterBrick. These classes are very similar in terms of their structure. Let's start with a look at the RasterLayer object. If you type the name of your RasterLayer in the console, this is the single-band object created by reading in with the raster() function, you'll see a print out of some key pieces of metadata like information on the dimensions, resolution (meaning the grid cell size) and coordinate reference system (which we'll talk about later). With a RasterBrick object you can see that the metadata is very similar to a RasterLayer except that there are a few spots that indicate we have more than one layer. In particular, under dimensions we have 3 as the number of layers and we have three names, one for each layer in the brick. The print out on the console is a useful description of all the metadata, but in many cases you'll want to extract and use pieces of this information. The raster package comes with a number of handy functions to help you do this. So for example, you can use extent() to get the minimum and maximum X and Y coordinates, ncell() and nlayer() to get the number of cells and number of layers. And then crs() will grab the coordinate reference system and you'll see later that you can use this to reassign the CRS if needed. A very useful, but sometimes confusing, aspect of the raster package is that the functions that you're using to read rasters do not read in the actual raster values by default. Rasters can often be very big files and the raster package will help conserve memory by only reading raster valu