For the matter at hand, here's a second "out take" from the talk, where I explain how to create a map which labels cities across the world with dots corresponding in size to their annual solar radiation.
By the time you're done, you should have a map which looks like this one here.
Have fun!
#To get the longitude, latitude for the cities in question, you will need to install nominatim for Open Street Maps #devtools::install_github("hrbrmstr/nominatim") # You will ned an OSM API key which you can get for free from Map Quest # Then you just populate the data frame like so calgary.rug.cities = c("Calgary", "Vancouver", "Edmonton", "Yellowknife", "Winnipeg", "Utrecht", "Buenos Aires", "Ramallah", "Doha", "Berlin") calgary.rug.countries = c("CA", "CA", "CA", "CA", "CA", "NL", "AR", "PS", "QA", "DE") #You need to know the ISO country codes for the countries in question, I suppose calgary.rug.solar.df = data.frame(matrix(nrow = length(calgary.rug.cities), ncol = 2)) for(i in 1:length(calgary.rug.cities)) { place_holder = nominatim::osm_geocode(query = calgary.rug.cities[i], country_codes = calgary.rug.countries[i], key = myosmapikey) calgary.rug.solar.df[i,1] = place_holder$lon calgary.rug.solar.df[i,2] = place_holder$lat print("One more city") } #We will need 13 columns of insolation data, one for each month and one for the annual values calgary.rug.solar.monthly = data.frame(matrix(nrow = nrow(calgary.rug.solar.df), ncol = 13)) for(i in 1:nrow(calgary.rug.solar.monthly)) { request_solar_ghi = nasapower::get_power(community = "SSE", pars = "ALLSKY_SFC_SW_DWN", temporal_average = "CLIMATOLOGY", lonlat = c(calgary.rug.solar.df[i,1], calgary.rug.solar.df[i,2])) #To see how this next line works, you really have to know what the structure of the returned response from NASAPOWER calgary.rug.solar.monthly[i,] = data.frame(request_solar_ghi[which(request_solar_ghi$PARAMETER == "ALLSKY_SFC_SW_DWN"),][,4:16]) } colnames(calgary.rug.solar.monthly) = colnames(data.frame(request_solar_ghi[,4:16])) #Check this to see what it is calgary.rug.solar.monthly rownames(calgary.rug.solar.monthly) = calgary.rug.cities calgary.rug.solar.df = cbind(calgary.rug.solar.df, calgary.rug.solar.monthly$ANN) calgary.rug.solar.df row.names(calgary.rug.solar.df) = calgary.rug.cities colnames(calgary.rug.solar.df) = c("LON", "LAT", "Annual GHI") calgary.rug.solar.df #The annual value is given as a "average day," so we need to fix it calgary.rug.solar.df$`Annual GHI` = calgary.rug.solar.df$`Annual GHI`*365 calgary.rug.solar.df calgary.rug.cities.diffuse = vector(length = length(calgary.rug.cities)) for(i in 1:length(calgary.rug.cities.diffuse)) { diffuse_request = nasapower::get_power(community = "SSE", pars = "DIFF", temporal_average = "CLIMATOLOGY", lonlat = c(calgary.rug.cities.for.presenting$LON[i], calgary.rug.cities.for.presenting$LAT[i])) annual_diffuse = data.frame(diffuse_request)$ANN calgary.rug.cities.diffuse[i] = annual_diffuse } calgary.rug.cities.dnr = vector(length = length(calgary.rug.cities)) for(i in 1:length(calgary.rug.cities)) { dnr_request = nasapower::get_power(community = "SSE", pars = "DNR", temporal_average = "CLIMATOLOGY", lonlat = c(calgary.rug.cities.for.presenting$LON[i], calgary.rug.cities.for.presenting$LAT[i])) annual_dnr = data.frame(dnr_request)$ANN calgary.rug.cities.dnr[i] = annual_dnr } calgary.rug.cities.diffuse[is.na(calgary.rug.cities.diffuse)] <- 0 calgary.cities.diff_to_dnr = calgary.rug.cities.diffuse/calgary.rug.cities.dnr #Yelloknife is a special case it seems; just not a lot of sunlight in some parts of the year calgary.rug.yellowknife_diffuse1 = data.frame(nasapower::get_power(community = "SSE", pars = "DIFF", temporal_average = "CLIMATOLOGY", lonlat = c(calgary.rug.cities.for.presenting$LON[4], calgary.rug.cities.for.presenting$LAT[4]))) calgary.rug.yellowknife_diffuse = sum((calgary.rug.yellowknife_diffuse1)[,4:16], na.rm = TRUE) calgary.rug.yellowknife_dnr1 = data.frame(nasapower::get_power(community = "SSE", pars = "DNR", temporal_average = "CLIMATOLOGY", lonlat = c(calgary.rug.cities.for.presenting$LON[4], calgary.rug.cities.for.presenting$LAT[4]))) calgary.rug.yellowknife_dnr = sum(calgary.rug.yellowknife_dnr1[,4:16], na.rm = TRUE) calgary.rug.yellowknife_diff_to_dnr = calgary.rug.yellowknife_diffuse/calgary.rug.yellowknife_dnr calgary.cities.diff_to_dnr[4] = calgary.rug.yellowknife_diff_to_dnr calgary.rug.cities.for.presenting$DiffDNR = calgary.cities.diff_to_dnr #Before going any further, please have a quick check to see that the different data frames are reasonable before trying to plot them myPaletter = colorRampPalette(rev(brewer.pal(11, "RdBu"))) scaled_map = scale_color_gradientn(colors = myPaletter(25), limits = c(min(calgary.rug.solar.df$DiffDNR2), max(calgary.rug.solar.df$DiffDNR2)), name = "Diffuse-to-DNR \n %-age") #The map for diffuse to DNI ggplot2::ggplot(data = world_map) + geom_sf() + geom_point(data = calgary.rug.solar.df[1:10,], mapping = aes(x = LON, y = LAT, color = (DiffDNR2)), size = 5) + coord_sf(xlim = c(min(calgary.rug.cities.for.presenting$LON)-10, max(calgary.rug.cities.for.presenting$LON) + 10), ylim = c(min(calgary.rug.cities.for.presenting$LAT)-10, max(calgary.rug.cities.for.presenting$LAT)+10), expand = F) + scaled_map + ggtitle("Diffuse to DNR annually \n selected cities") +theme(panel.background = element_rect(fill = "lightblue", color = "blue", size = 0.5))
