In camposfa/paceR: Tools for working with the PACE Database

knitr::opts_chunk$set(echo = TRUE)

Vector averaging

Some exploration of vector averaging results

library(paceR)
library(tidyverse)
library(circular)
library(dplyr)

Create datasets with varying degrees of seasonality

very_sync_12 = data_frame(MonthRadian = rep(seq(from = pi / 6, to = 2 * pi, length.out = 12), 5),
                          sync = "high_12",
                          TreesInFr = rep(c(0, 0, 0, 0, 0, 12, 0, 0, 0, 0, 0, 0), 5))

bimodal_6_12 = data_frame(MonthRadian = rep(seq(from = pi / 6, to = 2 * pi, length.out = 12), 5),
                          sync = "bimodal",
                          TreesInFr = rep(c(0, 0, 0, 0, 0, 14, 0, 0, 0, 0, 0, 2), 5))

very_sync_6 = data_frame(MonthRadian = rep(seq(from = pi / 6, to = 2 * pi, length.out = 12), 5),
                         sync = "high_6",
                         TreesInFr = rep(c(0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0), 5))

no_sync= data_frame(MonthRadian = rep(seq(from = pi / 6, to = 2 * pi, length.out = 12), 5),
                    sync = "no_sync",
                    TreesInFr = rep(rep(1, 12), 5))


# Made some changes to your previous "random" data set
set.seed(123)
random_sync = data_frame(MonthRadian = rep(seq(from = pi / 6, to = 2 * pi, length.out = 12), 5),
                         sync = "random_sync",
                         TreesInFr = rep(sample(1:12, 12, replace = TRUE), 5))

# Combine datasets
model_data <-  bind_rows(very_sync_12, bimodal_6_12, very_sync_6, no_sync, random_sync)%>% 
  mutate(TreesInFr = as.integer(TreesInFr)) %>% 
  group_by(sync) %>% 
  mutate(SampleSize = n(), SumOfTreeInFr = sum(TreesInFr)) %>% 
  ungroup()


# Greate a grouped data frame
model_data <- model_data %>% 
  group_by(sync) %>% 
  nest(.key = count_data)

# See what this looks like
model_data

model_data$count_data[[1]]


# This function takes your summarized count data and creates one row per non-zero tree 
make_tree_based <- function(df) {

  fruiting <- df %>% 
    select(MonthRadian, TreesInFr) %>% 
    filter(TreesInFr != 0)

  fruiting_unique <- fruiting %>% 
    distinct(MonthRadian, TreesInFr)

  n_times <- nrow(fruiting) / nrow(fruiting_unique)

  res <- data_frame(MonthRadian = rep(rep(fruiting_unique$MonthRadian, 
                                          times = fruiting_unique$TreesInFr), 
                                      times = n_times),
                    HasFruit = 1)
}

# Apply the function to make individual-based data
model_data <- model_data %>% 
  mutate(individual_data = purrr::map(count_data, make_tree_based))

# See what this looks like
model_data$individual_data[[1]]


# This function runs the Rayleigh test of uniformity and returns the test statistic
rayleigh_test_stat <- function(df) {
  rt <- suppressWarnings(rayleigh.test(df$MonthRadian))
  return(rt$statistic)
}

model_data <- model_data %>%
  mutate(ray_results = round(purrr::map_dbl(individual_data, rayleigh_test_stat), 2))


# View results
model_data