In ToonVanDaele/metapop:

knitr::opts_chunk$set(echo = TRUE)
library(MetaLandSim)
library(plyr)
library(dplyr)
library(ggplot2)
library(pander)

# Loading all population data
# Output from IBM model

df.pop <- read.csv2("c:/temp/output.txt", sep = ",")
str(df.pop)
summary(df.pop)

Properties and parameters of the population

Number of patches: r dplyr::n_distinct(df.pop$patch)

Number of timesteps: r dplyr::n_distinct(df.pop$timestep)

Number of unique individuals: r dplyr::n_distinct(df.pop$ID)

Number of individuals per patch by time (first 20 years)

#Number of individuals per patch by time (first 20 years)
df.pop %>%
  dplyr::filter(timestep <= 20) %>%
  dplyr::group_by(patch, timestep) %>%
  dplyr::count() %>%
  ggplot(aes(x = timestep, y = n, colour = as.factor(patch))) +
  geom_line()

Total number of births per patch

df.pop %>%
  dplyr::filter(age == 1) %>%
  dplyr::group_by(patch) %>%
  dplyr::count() %>%
  pandoc.table()

Cumulative number of births per patch

df.pop %>%
  dplyr::filter(age == 1) %>%
  dplyr::group_by(patch, timestep) %>%
  dplyr::count() %>%
  dplyr::mutate(csum = cumsum(n)) %>%
  ggplot(aes(x = timestep, y = csum, colour = as.factor(patch))) +
           geom_line()

Maximum age (age at dead) distribution

df.maxAge <- df.pop %>%
  dplyr::group_by(ID) %>%
  dplyr::summarise(maxAge = max(age))

ggplot(df.maxAge, aes(x = maxAge)) + geom_bar()

Age distribution by time (first 20 years)

(not yet included)

Number of migrated individuals

(rows = patch at birth, columns = patch at dead)

df.temp2 <- df.maxAge %>%
  left_join(df.pop, by = c("ID", "maxAge" = "age"))

df.temp2$patchBirth <- as.integer(gsub("\\:.*","", df.temp2$ID))

myTable <- table(df.temp2$patchBirth, df.temp2$patch)
pandoc.table(myTable)

Calculating the vital parameters for the full population

Survival rate adults

S_ad <- vector(length = 90)
for (i in 1:90) {
  ad1 <- as.vector(df.pop[(df.pop$age > 2) & (df.pop$timestep == i), "ID"])
  ad2 <- as.vector(df.pop[(df.pop$age > 2) & (df.pop$timestep == i + 1), "ID"])
  S_ad[i] <- length(ad1[ad1 %in% ad2]) /  length(ad1)
}

mean(S_ad)
plot(S_ad)

Survival rate juveniles (survival first year)

S_juv <- vector(length = 90)
for (i in 1:90) {
  juv1 <- as.vector(df.pop[(df.pop$age == 1) & (df.pop$timestep == i), "ID"])
  juv2 <- as.vector(df.pop[(df.pop$age > 1) & (df.pop$timestep == i + 1), "ID"])
  S_juv[i] <- length(juv1[juv1 %in% juv2]) /  length(juv1)
}

mean(S_juv)
plot(S_juv)