Tm_shape(honey0912) + tm_polygons( col = "priceperlb", style = "cont", pal = viridis( 10, direction = -1), title = "Price in US$ per lb" ) + tm_facets( by = "year", ncol = 2) + tm_layout( = 0.2) # select the years starting in 2009 honey0912 2008, ] # bind missing data to the original data honey0912 <- rbind( honey0912, # create an empty matrix for all the states that are missing matrix( NA, ncol = ncol(honey0912), nrow = 4 * length( unique(abbrev $state) ), dimnames = list( c( seq_len( 4 * length( unique(abbrev $state)) ) ), colnames(honey0912) ) ) ) # add the missing state names honey0912 $state <- sort( rep( unique(abbrev $state), 4) ) # add the year for the missing states honey0912 $year <- rep( 2009 : 2012, 10) # merge the honey dataset with the state abbreviations honey0912 <- (honey0912, abbrev, by = "state") # change the colnames of our two datasets for merging colnames(honey0912) <- c( "short", "State") # use the us_states data from spData colnames(us_states) <- "State" # remove the district of columbia from the us_states dataset from spData us_states <- us_states # merge the data together honey0912 <- merge(honey0912, us_states, by = "State", all.y = TRUE) # turn it into an sf object honey0912 <- st_as_sf(honey0912)
With grid.lines, lines are added to better separate the maps from each other. After that the three maps are inserted, for Hawaii the “height” again depends on the ratio of the “height” of Alaska to the 48 states. The height of each row depends on the ratio of the “height” of Alaska and the 48 states. With grid.layout, a window with two rows and one column is defined. In order for the widest possible range of people to be able to interpret a map, care should also be taken, if possible, to ensure that color-blind people can identify the differences between classes. In addition, when using a palette, it should be clear which color stands for lower or higher values. Green is associated with forests and blue with bodies of water. This example can also be extended to geographical features. For example, red is associated with negative things, while green is associated with positive things. You should pay attention to the selection of your colors: Colors transfer feelings. Categorical palettes: Easily distinguishable colors, ideal for categorical dataĪlternatively, a custom palette can be passed, for this purpose HEX codes may be used. Should be used for continuous variables with a natural midpoint ( midpoint).
Diverging pallets: Follow a gradient from dark to light, to dark. Sequential pallets: Follow a gradient from light to dark. The palettes from these packages can be divided into three types of palettes: # load shapefile for bavaria bavaria = 1000000, ] # turn it into an sf object cities % st_as_sf( coords = c( "long", "lat"), crs = 4326) %>% st_cast( "POINT") # keep only the cities that are in europe cities <- st_intersection(cities, st_union(europe_shape)) # turn the europe object into a MULTILINESTRING europe_shape <- st_cast(europe_shape, "MULTILINESTRING") communities <- read_sf( "gmd_ex.shp") # keep only the ones in rosenheim rosenheim <- communities # load the csv file for honey production in the us honey_csv <- read_csv( "honeyproduction.csv") # load the xlsx file for abbreviations of the us states abbrev <- read_xlsx( "abbrev.xlsx") # load honey shapefile honey_sf <- read_sf( "honey.shp")
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