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# Geospatial Data Carpentry Capstone: Glossary

## Key Points

 Introduction First key point. Brief Answer to questions. (FIXME) Day 1: Introduction to Geospatial Concepts Raster data is pixelated data where each pixel is associated with a specific location. Raster data always has an extent and a resolution. The extent is the geographical area covered by a raster. The resolution is the area covered by each pixel of a raster. Vector data structures represent specific features on the Earth’s surface along with attributes of those features. Vector objects are either points, lines, or polygons. Day 2: Introduction to Raster Data Always explore your raster first using the summary() and crs() functions. To plot your raster with ggplot(), first convert your raster to a data frame using the as.data.frame() function and specify the argument xy=TRUE. You may need to reproject your raster, for which you can use the projectRaster() function. There are many plotting and aesthetics options in the ggplot2 package for you to explore. Day 3: Introduction to Raster Data Always explore your data and note initial geospatial insights. The overlay() function can be used to calculate different summary statistics for your raster. With the overlay() function, you can begin to develop your skills in writing functions. Day 4: Exploring Vector Data Options for exploring vector files include displaying metadata, and visually examining plots R offers design options for the visual display of vector data, including choosing a color palette, and reversing the order of colors assigned to categories Day 5: Manipulating Raster Data Convert a data frame to an `sf` object using the `st_as_sf()` function. Use the `crop()` and `extract()` functions to get raster data of spatial subsets. Remember to check if your data align spatially by looking at the projection, extent, and units Wrap-up First key point. Brief Answer to questions. (FIXME)

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