![]() The process isn’t bad once you have the steps down! Let’s check it out.ĭata Tip: If your data attribute values are not read in as factors, you can convert the categorical attribute values using as.factor().įirst, let’s import all of the needed libraries. However, you will have to convert your data from spatial ( sp) objects to ames to use ggplot. Compared to base plot, you will find creating custom legends to be simpler and cleaner, and creating nicely formatted themed maps to be simpler as well. ggplot is a powerful tool for making custom maps. In this lesson you will create the same maps, however instead you will use ggplot(). In the previous lesson, you used base plot() to create a map of vector data - your roads data - in R. You will need a computer with internet access to complete this lesson and the data for week 4 of the course.ĭownload Week 4 Data (~500 MB) Making Maps with GGPLOT Plot a vector dataset by attributes in R using ggplot().SECTION 15 LAST CLASS: FINAL PROJECT PRESENTATIONSĪfter completing this tutorial, you will be able to:.SECTION 14 FINAL PROJECTS & COURSE FEEDBACK DISCUSSION.SECTION 10 MIDTERM REVIEW / PRESENTATION BEST PRACTICES.SECTION 9 STUDY FIRE USING REMOTE SENSING DATA.8.1 Fire / spectral remote sensing data - in R. ![]() SECTION 8 QUANTIFY FIRE IMPACTS - REMOTE SENSING.SECTION 7 MULTISPECTRAL IMAGERY R - NAIP, LANDSAT, FIRE & REMOTE SENSING.Uncertainty in Scientific Data & Metadata SECTION 5 LIDAR DATA IN R - REMOTE SENSING UNCERTAINTY.
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