![]() R # CREATE DSM MAPS # import DSM data DSM_SJER <- rast ( "data/NEON-DS-Airborne-Remote-Sensing/SJER/DSM/SJER_dsmCrop.tif" ) # convert to a df for plotting DSM_SJER_df <- as.ame ( DSM_SJER, xy = TRUE ) # import DSM hillshade DSM_hill_SJER <- rast ( "data/NEON-DS-Airborne-Remote-Sensing/SJER/DSM/SJER_dsmHill.tif" ) # convert to a df for plotting DSM_hill_SJER_df <- as.ame ( DSM_hill_SJER, xy = TRUE ) # Build Plot ggplot ( ) + geom_raster (data = DSM_SJER_df, aes (x = x, y = y, fill = SJER_dsmCrop, alpha = 0.8 ) ) + geom_raster (data = DSM_hill_SJER_df, aes (x = x, y = y, alpha = SJER_dsmHill ) ) + scale_fill_viridis_c ( ) + guides (fill = guide_colorbar ( ) ) + scale_alpha (range = c ( 0.4, 0.7 ), guide = "none" ) + # remove grey background and grid lines theme_bw ( ) + theme ( = element_blank ( ), id. With cut() to split the data into 3 bins. Will use dplyr’s mutate() function combined This stat can be critical in long battles. The Speed stat decides which Pokémon will make the first move in battle. If your Pokémons HP hits zero, it faints and is no longer usable in battle (it can still use Hidden Machines, though). HP (Hit Points) is a Pokémons life force. To make these decisions, it is useful to firstĮxplore the distribution of the data using a bar plot. Every Pokémon creature has an array of stats. Ggplot how many groups to break our data into, and where For clarity and visibility of the plot, we may prefer to view theĭata “symbolized” or colored according to ranges of values. The output can be pasted into a spreadsheet or a text editor for further analysis/manipulation. There are various options to show forms, types and base stats, or filter by generation/type. It's a simple tool to grab a plain text list of Pokmon. In the previous episode, we viewed our data using a continuous color The second new page is a Pokmon text list generator. (DSM) raster for the NEON Harvard Forest Field Site. We will continue working with the Digital Surface Model ![]() It alsoĬovers how to layer a raster on top of a hillshade to produce anĮloquent map. Ggplot2 package with customized coloring schemes. VIEWS OF REST STOP FEATURES AS THEY ARE MENTIONED SMART PHONE SCREEN WITH POKEMON GRAPHICS They of course have restrooms, a bunch of vending machines. This episode covers how to plot a raster in R using the See the lesson homepage for detailed informationĪbout the software, data, and other prerequisites you will need to work
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