# 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$pilogl01
Assume that the x coordinate is the latitude and the y coordinate is the longitude.
plots <- make_eems_plots(mcmcpath, longlat = FALSE) plots$mrates01
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$mrates01
The default color scale is [-2.5, +2.5] for migration rates and [-0.1, 0.1] for diversity rates.
plots <- make_eems_plots(mcmcpath, longlat = TRUE, m_colscale = c(-3, 3), q_colscale = c(-0.3, +0.3)) plots$mrates01 plots$qrates01
plots <- make_eems_plots(mcmcpath, longlat = TRUE, add_grid = TRUE, add_demes = TRUE) plots$mrates01
# 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$mrates01 + geom_point(data = coord, aes(x = long, y = lat), shape = 1)
# "Tidy" the map so that each polygon is a "group" map <- rworldmap::getMap(resolution = "high") map <- broom::tidy(map) plots <- make_eems_plots(mcmcpath, longlat = TRUE) plots$mrates01 + geom_path(data = map, aes(x = long, y = lat, group = group), color = "#888888", size = 0.5) + coord_quickmap()
# 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)
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