Let’s query some values from near the middle of the dataset to make sure we actually have data. NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA We can see only NA values at these locations. Now we’ll print dat to get an idea of what the values look like. #check the datasets dimensionsĭat contains 1405 rows, 621 columns, and 1 layer, so we can confirm the dimesions match. Let’s use dim() to check the dimensions of the resulting variable to confirm they match those of nc. #get a variable's dataĭat <- ncvar_get(nc, attributes(nc$var)$names) We will read the data into the dat variable. The netCDF object we created by opening a file (if you’re following along from the previous tutorial we named this nc), and the name of variable for which we want to retrieve data. Let’s get the data from the ‘data’ varialbe. Now that we know what the data variables are, we can get the data they contain. This gives the same result as the other methods, but would be necessary if the dataset contained multiple variables. We can get the name of specific data attribute by specifying its index. $namesĬalling the names property directly after the attributes() call gives the same result. Which is contained within the names property of var. The output shows us this dataset has one data attribute with the name ‘data’. Accessing the var property of our netCDF varialbe will list the data attributes for the netCDF file.
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