In dipetkov/reemsplots2: Generate plots to inspect and visualize the results of EEMS

# Change the eval option to TRUE to show the plots
knitr::opts_chunk$set(warning = TRUE, message = FALSE, collapse = TRUE, 
                      comment = "#>", out.width = "75%", dpi = 600,
                      fig.asp = 1 / 1.6, fig.width = 5, fig.retina = NULL,
                      echo = TRUE, eval = FALSE)

Produce a set of figures to visualize the results of analyzing geo-referenced genetic data with EEMS as well as to evaluate the EEMS model fit.

library("reemsplots2")
library("ggplot2")      # Modify the default plots
library("rworldmap")    # Add a geographic map
library("broom")        # Required for the map
library("mapproj")      # Change the projection
library("RColorBrewer") # Change the color scheme

mcmcpath <- system.file("extdata", "EEMS-example", package = "reemsplots2")
plots <- make_eems_plots(mcmcpath, longlat = TRUE)
names(plots)

plots$mrates01

plots$mrates02

plots$qrates01

plots$qrates02

plots$rdist01

plots$rdist02

plots$rdist03

plots$pilogl01

Flip the x and y axis

Assume that the x coordinate is the latitude and the y coordinate is the longitude.

plots <- make_eems_plots(mcmcpath, longlat = FALSE)
plots$mrates01

Change the color palette

Use a divergent Red to Blue color scheme from the RColorBrewer package instead of the default DarkOrange to Blue color scheme.

plots <- make_eems_plots(mcmcpath, longlat = TRUE,
                         eems_colors = brewer.pal(11, "RdBu"))
plots$mrates01

Change the color scale

The default color scale is [-2.5, +2.5] for migration rates and [-0.1, 0.1] for diversity rates.

plots <- make_eems_plots(mcmcpath, longlat = TRUE,
                         m_colscale = c(-3, 3),
                         q_colscale = c(-0.3, +0.3))
plots$mrates01
plots$qrates01

Add the population grid and sampled demes

plots <- make_eems_plots(mcmcpath, longlat = TRUE,
                         add_grid = TRUE, add_demes = TRUE)
plots$mrates01

Add the original sampling locations to the contour plot

# In this case the datapath is the same as the mcmcpath
# because all package data is in the "extdata" folder
datapath <- system.file("extdata", "EEMS-example", package = "reemsplots2")

# Load the sampling coordinates
coord <- read.table(paste0(datapath, ".coord"), header = FALSE)
# Name the columns appropriately
colnames(coord) <- c("long", "lat")

plots <- make_eems_plots(mcmcpath, longlat = TRUE)
plots$mrates01 + 
  geom_point(data = coord, aes(x = long, y = lat), shape = 1)

Add a high-resolution geographic map

# "Tidy" the map so that each polygon is a "group"
map <- rworldmap::getMap(resolution = "high")
map <- broom::tidy(map)

plots <- make_eems_plots(mcmcpath, longlat = TRUE)
plots$mrates01 + 
  geom_path(data = map, aes(x = long, y = lat, group = group),
            color = "#888888", size = 0.5) +
  coord_quickmap()

Use a different projection

# Transform to Albers equal-area conic projection, choosing lat0 and lat1 appropriate for Africa
plots$mrates01 + 
  geom_path(data = map, aes(x = long, y = lat, group = group),
            color = "#888888", size = 0.5) +
  coord_map(projection = "albers", lat0 = 0, lat1 = 20)

dipetkov/reemsplots2 documentation built on May 15, 2019, 8:47 a.m.