Making Maps: Salamander Species in US. Read in the Data

Transcription

1 Anything Arc can do, R can do better OR How to lose Arc in 10 days (part 1/n) UA Summer R Workshop: Week 3 Nicholas M. Caruso Christina L. Staudhammer 14 June 2016 Making Maps: Salamander Species in US To demonstrate making maps, we will use IUCN range maps to map out the number of salamander species in the United States. These range map shapefiles, as well as many others, can be found on IUCN s website here. As with other plotting we will use the ggplot2 package, which requires the data to be in a data frame, so we must convert it before plotting. We will also be using the maptools, raster, rgeos, and rgdal, sp, and maps packages, which will likely need to be installed first. # install.packages(c('maptools','raster','rgeos','rgdal','sp','maps')) library(maptools) library(raster) library(rgeos) library(rgdal) library(sp) library(tidyr) library(dplyr) library(maps) Read in the Data We will use the readshapepoly() function and call the path to our range map shapefile. Next, we will use map() to get a reference map of the United States counties (not including Alaska or Hawaii). Because we are dealing with spatial data, we need to give our maps a projection, we will define a coordinate reference system using CRS() set the projection for the caudate shapefile. We could plot these range maps, but in their current format, they aren t very informative. caudates <- readshapepoly('e:/idrive-sync/documents/r workshop Summer 2016/CAUDATA.shp') usa <- map("county", fill=true) 1

2 albers.proj <- CRS("+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +datum=nad83 +units=m +no_defs") projection(caudates) <- albers.proj plot(caudates) 2

3 Convert Map to SpatialPolygon Before we work with these two maps, we need to convert our county USA map to a SpatialPolygon. We want to preserve the identity of the county and states polygons (for plotting), so we ll use the preserve the ID variable and give the usa map the same projection as our range map. IDs <- usa$names usa <- map2spatialpolygons(usa, IDs=IDs, proj4string=albers.proj) Number of Salamanders in Each County For this map, we want to know how many polygons falls within the boundaries of each county. This can be accomplished using sapply(), over(), geometry(). We will retrieve the geometry from our composite range map shapefile, and overlay our county map with our range map. This overlay function will return a list for every county with a vector of all of the species from our range map that are within that county (each entry of the the vector corresponds to a species). We then use sapply() to find the length of each of those vectors, convert to a dataframe and use the rownames to create a new column of county ids. counts <- sapply(over(usa, geometry(caudates), returnlist=true), length) counts.df <- as.data.frame(counts) counts.df$id <- rownames(counts.df) rownames(counts.df) <- NULL 3

4 Combine Datasets Now we can combine our dataframe of counts of number of species per county, we can join these data back into our county map data so that we can plot the county polygons. First we will convert our SpatialPolygon-class US county map into a dataframe for joining and plotting. We will then use left_join to join our count dataframe into our county maps dataframe, matching by the unique county id. states.df <- fortify(usa, region = "ID") sallys.counties <- left_join(states.df, counts.df, by='id') head(sallys.counties) ## long lat order hole piece id group ## FALSE 1 alabama,autauga alabama,autauga.1 ## FALSE 1 alabama,autauga alabama,autauga.1 ## FALSE 1 alabama,autauga alabama,autauga.1 ## FALSE 1 alabama,autauga alabama,autauga.1 ## FALSE 1 alabama,autauga alabama,autauga.1 ## FALSE 1 alabama,autauga alabama,autauga.1 ## counts ## 1 22 ## 2 22 ## 3 22 ## 4 22 ## 5 22 ##

5 Plot the map We will use the ggmap, scales, and RColorBrewer packages along with ggplot2 to map our data. First, we ll plot the new counties dataframe with counts of salamanders using geom_polygon(); our aesthetics are defined as the longitude (x), latitude (y), the county ids (group), the number of salamander species within each county (fill). The remaining changes it the map are superficial and include, adjusting the map projection, changing the fill gradient, changing the theme, legend position, appearence of the x-axis, and the labels for both axes. # install.packages(c('ggmap','scales','rcolorbrewer')) library(ggplot2) library(ggmap) library(scales) library(rcolorbrewer) ggplot() + geom_polygon(data=sallys.counties, aes(x=long, y=lat, group=id, fill=counts), color="black", size=0.25) + coord_equal() + scale_fill_gradient2(name="number of\nspecies", low="white", high='navyblue') + theme_bw() + theme(legend.position=c(0.92, 0.27), legend.background=element_rect('transparent')) + scale_x_continuous(breaks=c(-120,-100,-80), labels=c(120,100,80), expression(paste('longitude (', degree, 'W)'))) + ylab(expression(paste('latitude (', degree, 'N)'))) 5

6 50 45 Latitude ( N) Number of Species Longitude ( W) 6

7 Inset Map: Alabama Salamander Richness To demonstrate an inset map, we will visualize Alabama species richness compared to the rest of the US. First we ll filter our county map by only the state of Alabama using grepl which searches for a pattern (alabama) in our id column. Filter Data bama.rich <- sallys.counties[grepl('alabama,', sallys.counties$id),] Define Map Similarly to the US map, we will create a map of the number of salamander species in Alabama and store it. Because The scale is much smaller, we will adjust the fill gradient. Just to make it look fancy, we ll add an off-centered gray polygon to make it look like the plot has a shadow. To show a different x and y axes labels, we adjusted the x and y scale pasting the N/W directly after the number. bama.map <- ggplot() + geom_polygon(data=bama.rich, aes(x=long+0.05, y=lat-0.05, group=id), fill='gray80') + geom_polygon(data=bama.rich, aes(x=long, y=lat, group=id, fill=counts), color="black", size=0.25) + coord_equal() + scale_fill_gradient2(name="number of\nspecies", low="white", high='darkblue', midpoint=22, mid='steelblue1') + theme_bw() + scale_x_continuous(breaks=c(-88,-87,-86,-85), labels=c(paste(c(88:85), ' W', sep=''))) + scale_y_continuous(limits=c(29.8,35.25), breaks=c(seq(30, 35, 1)), labels=c(paste(seq(30, 35, 1), ' N', sep=''))) + labs(x='', y='') Define Inset We will use the states.df to plot our inset map. To define our zoomed-in map of Alabama, by plotting the state of Alabama with darker red lines. states <- map("state", fill=true, plot=false) states.df2 <- fortify(states, region='id') inset.map <- ggplot() + geom_polygon(data=states.df2, aes(long, lat, group=group), color='black', fill='white', size=0.25) + geom_polygon(data=states.df2[states.df2$region=='alabama',], aes(long, lat, group=group), color='darkred', fill='white', size=1) + coord_equal() + theme_bw() + labs(x=null, y=null) + 7

8 theme(axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks=element_blank(), axis.title.x=element_blank()) Plot Map and Inset We will need the gridextra package to plot these two maps together. Here we can define the areas and placement for each plot. We will place the zoomed-in map in the middle of the plot, while we will place our inset map in the lower right hand corner. # install.packages('gridextra') library(gridextra) library(grid) library(gistools) grid.newpage() map.viewport <- viewport(width=1, height=1, x=0.5, y=0.5) inset.viewport <- viewport(width=0.45, height=0.55, x=0.57, y=0.3) print(bama.map, vp=map.viewport) print(inset.map, vp=inset.viewport) 8

9 35 N 34 N 33 N 32 N Number of Species N 30 N 88 W 87 W 86 W 85 W 9

10 Add Points Let s say that we randomly sampled the state of Alabama, and we want to visualize the proportion of species that we found at each site. We will use the dismo package to create the random sampling locations throughout the state. To create a random sampling of points, we need to use a raster object as the mask. We will also create a proportion species found column. Thus, when we add these data points to our previous plot we can scale the symbol size to be proportional to the proportion of species we found at that site. Random Sampling Sites library(dismo) r <- raster(ncol=200, nrow=200) extent(r) <- c(-88,-85.5, 31, 35) set.seed( ) sampling.sites <- as.data.frame(randompoints(r, 20)) sampling.sites$prop.spp.found <- runif(nrow(sampling.sites), 0.5, 1) head(sampling.sites) ## x y prop.spp.found ## ## ## ## ## ##

11 Plot Points with Proportional Symbol Size Now we can create a new map with our points, and plot it similarly as above. bama.map2 <- ggplot() + geom_polygon(data=bama.rich, aes(x=long+0.05, y=lat-0.05, group=id), fill='gray80') + geom_polygon(data=bama.rich, aes(x=long, y=lat, group=id, fill=counts), color="black", size=0.25) + geom_point(data=sampling.sites, aes(x=x, y=y, size=prop.spp.found)) + coord_equal() + scale_fill_gradient2(name="number of\nspecies", low="white", high='darkblue', midpoint=22, mid='steelblue1') + theme_bw() + scale_x_continuous(breaks=c(-88,-87,-86,-85), labels=c(paste(c(88:85), ' W', sep=''))) + scale_y_continuous(limits=c(29.8,35.25), breaks=c(seq(30, 35, 1)), labels=c(paste(seq(30, 35, 1), ' N', sep=''))) + labs(x='', y='') + scale_size(name='proportion of\nspecies Found', range=c(0.5,7)) library(grid) grid.newpage() map.viewport <- viewport(width=1, height=1, x=0.5, y=0.5) inset.viewport <- viewport(width=0.45, height=0.65, x=0.56, y=0.22) print(bama.map2, vp=map.viewport) print(inset.map, vp=inset.viewport) 11

12 35 N 34 N 33 N 32 N Number of Species Proportion of Species Found N 30 N 88 W 87 W 86 W 85 W 12