Cover type per ecological community

Creating definitions for cover types according to proportions of grass, brush and wood, and building ternary plot.

Re-using code from: http://www.ggtern.com/2014/01/15/usda-textural-soil-classification/

Load the required libraries

library(ggtern)

## Warning: package 'ggtern' was built under R version 3.6.3

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 3.6.3

## Registered S3 methods overwritten by 'ggtern':
##   method           from   
##   grid.draw.ggplot ggplot2
##   plot.ggplot      ggplot2
##   print.ggplot     ggplot2

## --
## Remember to cite, run citation(package = 'ggtern') for further info.
## --

## 
## Attaching package: 'ggtern'

## The following objects are masked from 'package:ggplot2':
## 
##     aes, annotate, ggplot, ggplot_build, ggplot_gtable, ggplotGrob,
##     ggsave, layer_data, theme_bw, theme_classic, theme_dark,
##     theme_gray, theme_light, theme_linedraw, theme_minimal, theme_void

library(plyr)

## Warning: package 'plyr' was built under R version 3.6.3

library(grid)

Build data frame defining cover types using proportions of grass, brush, and wood. The criteria used for separating cover types (to select runoff curve number) was inferred from footnotes in: >Table 2.2 in: Cronshey R G 1986 Urban Hydrology for Small Watersheds, Technical Release 55 (TR-55). United States Department of Agriculture, Soil Conservation Service, Engineering Division.

coverTypes <- data.frame(
  rbind(
    # grass | brush | wood
    c(0,   0,   1,   "woodland"),
    c(0,   .5,  .5,  "woodland"),
    c(.4,  0,   .6,  "woodland"),
    c(1,   0,   0,   "grassland"),
    c(.6,  .4,  0,   "grassland"),
    c(.6,  0,   .4,  "grassland"),
    c(.4,  .6,  0,   "wood-grass"),
    c(.6,  .4,  0,   "wood-grass"),
    c(.6,  0,   .4,  "wood-grass"),
    c(.4,  0,   .6,  "wood-grass"),
    c(0,   .5,  .5,  "wood-grass"),
    c(0,   1,   0,   "shrubland"),
    c(.4,  .6,  0,   "shrubland"),
    c(0,   .5,  .5,  "shrubland")
  )
)
names(coverTypes) <- c("Grass", "Brush", "Wood", "Label")
coverTypes$Grass <- as.numeric(as.character(coverTypes$Grass))
coverTypes$Brush <- as.numeric(as.character(coverTypes$Brush))
coverTypes$Wood <- as.numeric(as.character(coverTypes$Wood))

# Put tile labels at the midpoint of each tile.
coverTypes.LAB = ddply(coverTypes, 'Label', function(df) {
    apply(df[, 1:3], 2, mean)
})

png("coverTypePerEcologicalCommunity.png", width = 600, height = 500)

# Construct the plot.
ggplot(data = coverTypes, aes(y=Brush, x=Grass, z=Wood)) +
  coord_tern(L="x",T="y",R="z") +
  geom_polygon(aes(fill = Label), 
               alpha = 0.75, size = 0.5, color = 'black') +
  geom_text(data = coverTypes.LAB,
            aes(label = Label),
            color = 'black',
            size = 3.5) +
  theme_rgbw() +
  theme_showsecondary() +
  theme_showarrows() +
  custom_percent("Percent") +
  theme(legend.justification = c(0, 1),
        legend.position      = c(0, 1)) +
  labs(title = 'Cover type per ecological community',
       fill  = 'Cover type',
       color = 'Cover type')

## Coordinate system already present. Adding new coordinate system, which will replace the existing one.

dev.off()

## png 
##   2

Cover type per ecological community

Andreas Angourakis

23/05/2020