Auto-grouping and charting"

In DivInsight: Diversity Index Calculation & Visualisation for Taxa and Location

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(DivInsight)
library(dplyr)
data("Colombia")

To gain a more detailed insight of the diversity in a large area the group_radius argument can be used in the clusterise_sites() function to automatically group sites together by specifying a radius in metres.

A label is assigned to each site denoting which group it belongs to. This label can be seen in the site_group column in the cluster dataframe.

# subset the dataframe by province name 
Colombia_Meta <- subset(Colombia, stateProvince == "Meta")

# cluster occurrence data by date and generate centred coordinates for each site
clusterised_Meta_20km_groups <- clusterise_sites(
  dataframe = Colombia_Meta,
  cluster_min_length = 30,

  group_radius = 20000
)

# view the information for each site/cluster
print(clusterised_Meta_20km_groups[[2]])

Base R commands can be used to see how many sites there are in each group and help us decide which group(s) to examine further.

# use table() and sort() to see how many sites there are in each group
clusterised_Meta_20km_groups[[2]]$site_group %>% table %>% sort %>% print

Here we choose to examine sites 1, 5, 6, and 8 further by using filter_groups_by_number() to store each group's data then plot using plot_sites_scatter_H().

# store the data for each group
Colombia_Meta_1 <- filter_groups_by_number(clusterised_Meta_20km_groups, 1)
Colombia_Meta_5 <- filter_groups_by_number(clusterised_Meta_20km_groups, 5)
Colombia_Meta_6 <- filter_groups_by_number(clusterised_Meta_20km_groups, 6)
Colombia_Meta_8 <- filter_groups_by_number(clusterised_Meta_20km_groups, 8)

# plot the data in a scatter plot
plot_sites_scatter_H(Colombia_Meta_1, main = "Shannons H at Meta #1") 
plot_sites_scatter_H(Colombia_Meta_5, main = "Shannons H at Meta #5") 
plot_sites_scatter_H(Colombia_Meta_6, main = "Shannons H at Meta #6") 
plot_sites_scatter_H(Colombia_Meta_8, main = "Shannons H at Meta #8") 

The charts show varying patterns of diversity over available time frames but it is important to know where these changes are taking place.

map_start() can be used to create a new map object and map_add() can be used to add coordinate information.

# create a new map using group 1's coordinates
Colombia_Meta_map <- 

  map_start(

    clusterised_object = Colombia_Meta_1,
    site_name = "Meta #1",
    colour = "green"
  )

# add group 5's coordinates to the map object 
Colombia_Meta_map <-

  map_add(

    existing_map = Colombia_Meta_map,

    clusterised_object = Colombia_Meta_5,
    site_name = "Meta #5",
    colour = "purple"
  )

# add group 6's coordinates to the map object
Colombia_Meta_map <-

  map_add(

    existing_map = Colombia_Meta_map,

    clusterised_object = Colombia_Meta_6,
    site_name = "Meta #6",
    colour = "blue"
  )

# add group 8's coordinates to the map object
Colombia_Meta_map <-

  map_add(

    existing_map = Colombia_Meta_map,

    clusterised_object = Colombia_Meta_8,
    site_name = "Meta #8",
    colour = "red"
  )

Once the map has been created it can be viewed.

# view the map
Colombia_Meta_map

DivInsight documentation built on Aug. 12, 2023, 9:06 a.m.