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stat_MST

Euclidean Minimum Spanning Tree Stat Function

Description

A Stat extension for 'ggplot2' to plot a 2D MST by making a scatter plot with segments.

stat_MST uses the information returned by ComputeMST for producing a 2D Minimum Spanning Tree plot with 'ggplot2' and should be combined with geom_point().

Usage

stat_MST(
  mapping = NULL,
  data = NULL,
  geom = "segment",
  position = "identity",
  na.rm = FALSE,
  linetype = "dotted",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping

The aesthetic mapping, usually constructed with aes or aes_. The required aesthetics are x, y, from, and to. Those are columns of the mst object returned by ComputeMST.
data

a mst class object returned by the ComputeMST function.
geom

The geometric object to display the data. The default value is "segment" in order to produce the edges between the vertices.
position

The position adjustment to use for overlapping points on this layer
na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.
linetype

an integer or name: 0 = "blank", 1 = "solid", 2 = "dashed", 3 = "dotted", 4 = "dotdash", 5 = "longdash", 6 = "twodash". The default for 'MST' objects is "dotted".
show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.
inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders.
...

other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details.

Computed variables

x

x coordinates of the MST start points

y

y coordinates of the MST start points

xend

x coordinates of the MST end points

yend

y coordinates of the MST end points

Examples

## 2D artificial data:
set.seed(1984)
n <- 15
c1 <- data.frame(x = rnorm(n, -0.2, sd = 0.2), y = rnorm(n, -2, sd = 0.2))
c2 <- data.frame(x = rnorm(n, -1.1, sd = 0.15), y = rnorm(n, -2, sd = 0.3)) 
d <- rbind(c1, c2)
d <- as.data.frame(d)

## MST:
out <- ComputeMST(d)

#1) simple plot
library(ggplot2)
ggplot(data = out, 
    aes(x = x, y = y, 
    from = from, to = to))+ 
    geom_point()+
    stat_MST(colour = "red", linetype = 2)
    
#2) curved edges
library(ggplot2)
ggplot(data = out, 
    aes(x = x, y = y, 
    from = from, to = to))+ 
    geom_point()+
    stat_MST(geom = "curve", colour = "red", linetype = 2)

## Not run: 
## plotting MST on maps:
library(ggmap)

#3) honeymoon cruise example
# define ports
df.port_locations <- data.frame(location = c("Civitavecchia, Italy",
                                             "Genova, Italy",
                                             "Marseille, France",
                                             "Barcelona, Spain",
                                             "Tunis, Tunisia",
                                             "Palermo, Italy"), 
                                stringsAsFactors = FALSE)

# get latitude and longitude
geo.port_locations <- geocode(df.port_locations$location, source = "dsk")

# combine data
df.port_locations <- cbind(df.port_locations, geo.port_locations)

# MST
out <- ComputeMST(df.port_locations[,2:3])
plot(out) #just to check

# Plot
#' map <- c(left = -8, bottom = 32, right = 20, top = 47)

get_stamenmap(map, zoom = 5) %>% ggmap()+
  stat_MST(data = out, 
           aes(x = lon, y = lat, from = from, to = to), 
           colour = "red", linetype = 2)+
  geom_point(data = out, aes(x = lon, y = lat), size = 3)


#4) World Map travels:
library(ggplot2)
library(ggmaps)

country_coords_txt <- "
   1     3.00000  28.00000       Algeria
   2    54.00000  24.00000           UAE
   3   139.75309  35.68536         Japan
   4    45.00000  25.00000 'Saudi Arabia'
   5     9.00000  34.00000       Tunisia
   6     5.75000  52.50000   Netherlands
   7   103.80000   1.36667     Singapore
   8   124.10000  -8.36667         Korea
   9    -2.69531  54.75844            UK
   10    34.91155  39.05901        Turkey
   11  -113.64258  60.10867        Canada
   12    77.00000  20.00000         India
   13    25.00000  46.00000       Romania
   14   135.00000 -25.00000     Australia
   15    10.00000  62.00000        Norway"
   
   
 d <- read.delim(text = country_coords_txt, header = FALSE, 
   quote = "'", sep = "", col.names = c('id', 'lon', 'lat', 'name'))
   
 out <- ComputeMST(d[,2:3])
 
 country_shapes <- geom_polygon(aes(x = long, y = lat, group = group),
   data = map_data('world'), fill = "#CECECE", color = "#515151", 
   size = 0.15)
   
 ggplot()+ country_shapes+
   stat_MST(geomdata = out, aes(x = lon, y = lat, from = from, to = to), 
     colour = "red", linetype = 2)+
   geom_point(data = out, aes(x = lon, y = lat), size=2)

## End(Not run)
emstreeR

Tools for Fast Computing and Plotting Euclidean Minimum Spanning Trees

v2.2.2
BSD_3_clause + file LICENSE
Authors
Allan Quadros [aut, cre]
Initial release
2020-11-28

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