---
title: "cdmetapopR Vignette"
subtitle: "Reading, summarizing, customizing, and plotting CDMetaPOP outputs"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Reading, summarizing, customizing, and plotting CDMetaPOP outputs}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  echo = TRUE,
  message = FALSE,
  warning = FALSE,
  fig.width = 8,
  fig.height = 5,
  out.width = "100%"
)

library(cdmetapopR)
library(tidyverse)
```

# Overview

This vignette shows how to use `cdmetapopR` to read, reshape, summarize,
and plot CDMetaPOP outputs. The goal is to show both quick plotting
functions and the lower-level data-frame outputs that can be used for
custom `ggplot2` figures.

The examples use the package example output files in
`inst/extdata/Example_dat`.

The example data include:

-   2 batches
-   2 Monte Carlo replicates per batch
-   `summary_popAllTime.csv`
-   `summary_classAllTime.csv`
-   `summary_popAllTime_DiseaseStates.csv`
-   `ind0.csv` through `ind9.csv`

```{r example-paths}
ex_dir <- system.file("extdata", "Example_dat", package = "cdmetapopR")

pop_file <- file.path(ex_dir, "run0batch0mc0species0", "summary_popAllTime.csv")
class_file <- file.path(ex_dir, "run0batch0mc0species0", "summary_classAllTime.csv")
ind_file <- file.path(ex_dir, "run0batch0mc0species0", "ind9.csv")

ex_dir
list.files(ex_dir)
```

## Choosing Runs, Batches, Monte Carlo Replicates, and Species

Most summary functions use the same filtering arguments:

-   `run = 0`
-   `batch = 0`
-   `mc = 0`
-   `species = 0`

These defaults intentionally select one output folder. Use `"all"` or a
range of values `c(0,1)` for any of these arguments to combine multiple
folders. The example directory includes two batches with two MCs each.
Each of these files contains ten ind#.csvs, containing information from
each year of the simulation. Additionally, each file has a
summary_popAllTime.csv, summary_classAllTime.csv, and a
summary_popAllTime_DiseaseStates.csv that summarizes information across
the years of the simulation.

```{r filter-examples}
one_mc <- summary_dataframe(ex_dir, type = "ind", years = 9, mc = 0)
both_mcs <- summary_dataframe(ex_dir, type = "ind", years = 9, mc = "all")
all_batches_mcs <- summary_dataframe(ex_dir, type = "pop", batch = "all", mc = "all")

c(
  one_mc_rows = nrow(one_mc),
  both_mcs_rows = nrow(both_mcs),
  all_batches_mcs_rows = nrow(all_batches_mcs)
)
```

For individual files, `years` narrows the selected `ind##.csv` files.
For summary files, years are filtered after reading by using ordinary
data-frame operations.

# Conversion Helpers

## `create_cdmat()`

Use `create_cdmat()` to create a cost distance matrix from patch
coordinates. The simplest options are Euclidean distance and equal
distance.

```{r create-cdmat}
coords <- data.frame(
  x = c(0, 1, 3, 6),
  y = c(0, 2, 2, 5)
)

create_cdmat(coords, method = "euclidean")
create_cdmat(coords, method = "equal")
```

## `cdmetapop_to_gene()`

Use `cdmetapop_to_gene()` to convert an `ind##.csv` file to GENEPOP or
GENALEX format. The function writes the converted file to the current
working directory, so this example uses a temporary directory.

```{r cdmetapop-to-gene}
old_wd <- getwd()
setwd(tempdir())

cdmetapop_to_gene(ind_file, format = "genepop")
list.files(pattern = "^my_genepop")

setwd(old_wd)
```

# Output Data Frames

`summary_dataframe()` returns CDMetaPOP output files as data frames for
custom plotting or analysis. Use `type` to choose the file family and
`run`, `batch`, `mc`, and `species` to choose which output folders to
include. For `type = "pop"` and `type = "class"`, pipe-delimited summary
columns are split into one row per patch or class (depending on type) by
default while keeping metrics in separate columns.

```{r summary-dataframe-pop}
pop_df <- summary_dataframe(ex_dir, type = "pop", batch = 0, mc = 0)
head(pop_df[, c("Year", ".batch", ".mc", "PatchID", "GrowthRate", "K", "N_Initial")])
```

In the default long format, metrics remain in separate columns, but each
pipe-delimited value is split into its own row. `PatchID` gives the
position of the value inside the original `|`-delimited cell. For many
`summary_popAllTime` columns, `PatchID == 0` is the total across patches
(such as N_initial) and later Patch IDs are actual patch-level values.

```{r summary-dataframe-pop-total-plot}
pop_totals <- pop_df %>%
  filter(PatchID == 1)

ggplot(pop_totals, aes(x = Year, y = N_Initial)) +
  geom_line(linewidth = 0.8, color = "steelblue") +
  labs(
    title = "Initial Population Size from summary_dataframe()",
    x = "Year",
    y = "N initial"
  ) +
  theme_minimal()
```

```{r summary-dataframe-pop-patch-plot}
patch_subset <- pop_df %>%
  filter(PatchID %in% 2:9)

ggplot(patch_subset, aes(x = Year, y = K, color = factor(PatchID))) +
  geom_line(linewidth = 0.7) +
  labs(
    title = "Patch Carrying Capacity for Selected Patches",
    x = "Year",
    y = "K",
    color = "PatchID"
  ) +
  theme_minimal()
```

The same workflow can compare Monte Carlo replicates or batches.

```{r summary-dataframe-pop-multi-mc}
pop_mc <- summary_dataframe(ex_dir, type = "pop", batch = 0, mc = "all") %>%
  filter(PatchID == 1)

ggplot(pop_mc, aes(x = Year, y = Births, color = factor(.mc), group = .mc)) +
  geom_line(linewidth = 0.8) +
  labs(
    title = "Births by Monte Carlo Replicate",
    x = "Year",
    y = "Births",
    color = "MC"
  ) +
  theme_minimal()
```

Use `summary_format = "wide"` if you need the original summary columns
instead.

```{r summary-dataframe-pop-wide}
pop_wide_df <- summary_dataframe(ex_dir, type = "pop", batch = 0, mc = 0, summary_format = "wide")
head(pop_wide_df[, c("Year", "GrowthRate", "N_Initial")])
```

```{r summary-dataframe-class}
class_df <- summary_dataframe(ex_dir, type = "class", batch = 0, mc = 0)
head(class_df[, c("Year", ".batch", ".mc", "ClassID", "Ages", "N_Initial_Age")])
```

Class summaries are useful when the class ID positions correspond to age
or size classes.

```{r summary-dataframe-class-age-plot}
class_selected_years <- class_df %>%
  filter(Year %in% c(0, 5, 9))

ggplot(class_selected_years, aes(x = Ages, y = N_Initial_Age, fill = factor(Year))) +
  geom_col(position = "dodge") +
  labs(
    title = "Age Structure Across Selected Years",
    x = "Age",
    y = "Individuals",
    fill = "Year"
  ) +
  theme_minimal()
```

Disease-state files are returned in tidy long format by default, with
one row per year, source, and disease state.

```{r summary-dataframe-disease}
disease_df <- summary_dataframe(
  ex_dir,
  type = "disease",
  state_names = c("Susceptible", "Infected", "Recovered")
)
head(disease_df)
```

```{r summary-dataframe-disease-plot}
disease_mc <- summary_dataframe(
  ex_dir,
  type = "disease",
  state_names = c("Susceptible", "Infected", "Recovered"),
  mc = "all"
)

ggplot(disease_mc, aes(x = Year, y = Count, color = factor(.mc), group = .mc)) +
  geom_line(linewidth = 0.8) +
  facet_wrap(~ State, scales = "free_y") +
  labs(
    title = "Disease State Counts by Monte Carlo Replicate",
    x = "Year",
    y = "Count",
    color = "MC"
  ) +
  theme_minimal()
```

Individual files can be filtered by year and patch.

```{r summary-dataframe-ind}
ind_df <- summary_dataframe(ex_dir, type = "ind", years = 9, patches = c(1, 3))
head(ind_df)
```

You may want to view the number of individuals in each disease state:

```{r summary-dataframe-ind-custom-age}
ind_year9 <- summary_dataframe(ex_dir, type = "ind", years = 9, mc = "all")

ggplot(ind_year9, aes(x = age, fill = factor(.mc))) +
  geom_histogram(binwidth = 1, position = "identity", alpha = 0.45) +
  labs(
    title = "Individual Age Distribution by Monte Carlo Replicate",
    x = "Age",
    y = "Individuals",
    fill = "MC"
  ) +
  theme_minimal()
```

You may also examine sizes of individuals, however this does not vary in
our example files:

```{r summary-dataframe-ind-custom-patch-boxplot}
top_patches <- ind_year9 %>%
  count(PatchID, sort = TRUE) %>%
  slice_head(n = 8) %>%
  pull(PatchID)

ind_top_patches <- ind_year9 %>%
  filter(PatchID %in% top_patches)

ggplot(ind_top_patches, aes(x = factor(PatchID), y = size, fill = factor(.mc))) +
  geom_boxplot(outlier.alpha = 0.3) +
  labs(
    title = "Individual Size in the Most Populated Patches",
    x = "Patch",
    y = "Size",
    fill = "MC"
  ) +
  theme_minimal()

```

Perhaps view the distribution of individuals spatially:

```{r summary-dataframe-ind-custom-spatial}
patch_counts <- ind_year9 %>%
  group_by(.mc, PatchID, XCOORD, YCOORD) %>%
  summarise(n = n(), .groups = "drop")

ggplot(patch_counts, aes(x = XCOORD, y = YCOORD, size = n, color = factor(.mc))) +
  geom_point(alpha = 0.7) +
  coord_equal() +
  labs(
    title = "Patch Abundance",
    x = "X coordinate",
    y = "Y coordinate",
    size = "Individuals",
    color = "MC"
  ) +
  theme_minimal()
```

# Population Summary Plots

If users prefer to view figures that can be made directly from CDMetaPOP
outputs, they can use the following summary_pop() functions.

`summary_pop()` works with `summary_popAllTime.csv` files. The input can
be a single file, a vector of files, a data frame, or a directory
containing CDMetaPOP output folders.

When a directory is supplied, `summary_pop()` discovers the matching
output folders. By default it uses `run = 0`, `batch = 0`, `mc = 0`, and
`species = 0`; use `"all"` for any of those arguments to include
multiple runs, batches, Monte Carlo replicates, or species.

## Initial Population Size

```{r summary-pop-n-initial}
summary_pop(ex_dir, type = "N_initial")
```

Include multiple Monte Carlo replicates by setting `mc = "all"`. With
multiple source files, the default behavior can show individual MC
trajectories and a summary band.

```{r summary-pop-n-initial-all-mc}
summary_pop(ex_dir, type = "N_initial", mc = "all")
```

To show only the summarized pattern, set `show_mc = FALSE`.

```{r summary-pop-n-initial-ci-only}
summary_pop(ex_dir, type = "N_initial", mc = "all", show_mc = FALSE)
```

## Sex Counts

By default, `type = "sex"` shows males and females only.

```{r summary-pop-sex}
summary_pop(ex_dir, type = "sex")
```

Use `include_yys = TRUE` to include YY males and YY females. This may be
relevant for fisheries research.

```{r summary-pop-sex-yys}
summary_pop(ex_dir, type = "sex", include_yys = TRUE)
```

The same YY option works when comparing multiple batches or MCs.

```{r summary-pop-sex-all-batches}
summary_pop(
  ex_dir,
  type = "sex",
  batch = "all",
  mc = "all",
  include_yys = TRUE,
  show_mc = FALSE
)
```

## Mature Counts

By default, `type = "mature"` shows mature males and mature females
only.

```{r summary-pop-mature}
summary_pop(ex_dir, type = "mature")
```

Use `include_yys = TRUE` to include mature YY males and mature YY
females.

```{r summary-pop-mature-yys}
summary_pop(ex_dir, type = "mature", include_yys = TRUE)
```

## Births

```{r summary-pop-births}
summary_pop(ex_dir, type = "births")
```

```{r summary-pop-births-all-mc}
summary_pop(ex_dir, type = "births", mc = "all")
```

## Myy Ratio

```{r summary-pop-myy-ratio}
summary_pop(ex_dir, type = "myy_ratio")
```

## Patch Abundance from `summary_popAllTime.csv`

```{r summary-pop-patch}
summary_pop(ex_dir, type = "patch", years = c(0, 5, 9))
```

## Allelic Richness and Heterozygosity from `summary_popAllTime.csv`

These options summarize genetic metrics stored in the population summary
file.

```{r summary-pop-allelic-richness}
summary_pop(ex_dir, type = "allelic_richness", mc = "all")
```

```{r summary-pop-het}
summary_pop(ex_dir, type = "het", mc = "all")
```

# Class Summary Plots

`summary_class()` works with `summary_classAllTime.csv` files.

## Age Class Counts

```{r summary-class-age-class}
summary_class(ex_dir, type = "age_class", n = 10)
```

Use a smaller `n` to facet more years. This can be useful for short
example runs or simulations with relatively few generations.

```{r summary-class-age-class-n5}
summary_class(ex_dir, type = "age_class", n = 5)
```

## Age Plus One

```{r summary-class-age-plus-one}
summary_class(ex_dir, type = "age_plus_one")
```

```{r summary-class-age-plus-one-all-mc}
summary_class(ex_dir, type = "age_plus_one", mc = "all")
```

# Disease State Summaries

`summary_disease()` works with `summary_popAllTime_DiseaseStates.csv`
files. It summarizes disease-state counts across Monte Carlo replicates
and compares batches.

## Default State Names

```{r summarize-states-default}
summary_disease(ex_dir)
```

## Custom State and Scenario Names

```{r summarize-states-custom}
summary_disease(
  ex_dir,
  state_names = c("State 1", "State 2", "State 3"),
  scenario_names = c("Batch 0", "Batch 1")
)
```

## Cumulative State

Use `cumulative_states` for a state that should be plotted as a running
total within each Monte Carlo replicate.

```{r summarize-states-cumulative}
summary_disease(
  ex_dir,
  state_names = c("State 1", "State 2", "State 3"),
  scenario_names = c("Batch 0", "Batch 1"),
  cumulative_states = "State 3"
)
```

# Individual File Summaries

`summary_ind()` works with `ind##.csv` files. The input can be one file,
multiple files, a run folder, a top-level output directory, or a data
frame.

For one-year plots, specify `year`. For movement over time, specify
`years`.

## Age Histogram

```{r summary-ind-age}
summary_ind(ex_dir, type = "age", year = 9, batch = 0, mc = 0)
```

```{r summary-ind-age-all-mc}
summary_ind(ex_dir, type = "age", year = 9, batch = 0, mc = "all")
```

## Size Histogram

```{r summary-ind-size}
summary_ind(ex_dir, type = "size", year = 9, batch = 0, mc = 0)
```

## Size by Age

```{r summary-ind-age-size}
summary_ind(ex_dir, type = "age_size", year = 9, batch = 0, mc = 0)
```

Filter to a subset of patches with `patches`.

```{r summary-ind-age-size-patches}
summary_ind(ex_dir, type = "age_size", year = 9, batch = 0, mc = 0, patches = c(1, 3, 4, 5))
```

## Hindex Histogram

```{r summary-ind-hindex}
summary_ind(ex_dir, type = "hindex", year = 9, batch = 0, mc = 0)
```

## Movement Distance Histogram

`CDist = -9999` is treated as no movement and is excluded from the
histogram.

```{r summary-ind-cdist}
summary_ind(ex_dir, type = "cdist", year = 9, batch = 0, mc = 0)
```

## Movement Over Time

```{r summary-ind-movement}
summary_ind(ex_dir, type = "movement", years = 0:9, batch = 0, mc = 0)
```

```{r summary-ind-movement-all-mc}
summary_ind(ex_dir, type = "movement", years = 0:9, batch = 0, mc = "all")
```

If CDMetaPOP output includes sampled individual files, use
`file_type = "ind_Sample"` to read `ind##_Sample.csv` files instead of
`ind##.csv` files. The bundled example data use `ind##.csv`, so this
example is shown but not evaluated.

```{r summary-ind-sample-example, eval=FALSE}
summary_ind(ex_dir, type = "age", year = 9, file_type = "ind_Sample")
```

# Individual-Level Genetics

The individual-level genetics functions use genotype columns named like
`L0A0`, `L0A1`, `L1A0`, and so on.

## Allele Frequencies by Patch

```{r allele-frequencies-by-patch}
allele_frequencies_ind(ex_dir, year = 9, batch = 0, mc = 0)
```

Focus on one locus with `loci`.

```{r allele-frequencies-locus}
allele_frequencies_ind(ex_dir, year = 9, batch = 0, mc = 0, loci = "L0")
```

Filter to selected patches when a figure would otherwise be too dense.

```{r allele-frequencies-patches}
allele_frequencies_ind(
  ex_dir,
  year = 9,
  batch = 0,
  mc = 0,
  loci = "L0",
  patches = c(1, 3, 4, 5, 7, 9)#, 
  #jitter = FALSE
)
```

Jittering of points on top of the boxplots can be turned off by adding
the argument jitter = FALSE.

## Heterozygosity by Patch

```{r heterozygosity-by-patch}
heterozygosity_ind(ex_dir, year = 9, batch = 0, mc = 0)
```

```{r heterozygosity-locus-patches}
heterozygosity_ind(
  ex_dir,
  year = 9,
  batch = 0,
  mc = 0,
  loci = "L0",
  patches = c(1, 3, 4, 5, 7, 9)
)
```

## Pairwise FST

```{r pairwise-fst}
pairwise_fst_ind(ex_dir, year = 9, batch = 0, mc = 0)
```

```{r pairwise-fst-locus}
pairwise_fst_ind(ex_dir, year = 9, batch = 0, mc = 0, loci = "L0")
```

# Patch Maps from Individual Files

`summary_patch_map()` maps patch locations from the individual files.
Point size reflects the number of individuals in each patch.

## All Individuals

```{r summary-patch-map-all}
summary_patch_map(
  ex_dir,
  years = c(0, 5, 9),
  batch = 0,
  mc = 0,
  crs = 5070
)
```

If patch counts vary widely, use `log_scale = TRUE` for abundance point
sizes.

```{r summary-patch-map-log}
summary_patch_map(
  ex_dir,
  years = c(0, 5, 9),
  batch = 0,
  mc = 0,
  log_scale = TRUE,
  crs = 5070
)
```

## One Disease State

Use `states` to count only individuals in selected disease states.

```{r summary-patch-map-state}
summary_patch_map(
  ex_dir,
  years = c(0, 5, 9),
  batch = 0,
  mc = 0,
  states = 1,
  crs = 5070
)
```

## Faceted by Disease State

```{r summary-patch-map-state-facet, fig.height=7}
summary_patch_map(
  ex_dir,
  years = c(0, 9),
  batch = 0,
  mc = 0,
  states = c(0, 1),
  facet_by_state = TRUE,
  crs = 5070
)
```

## Allele Frequency Map

Patch maps can also show genetic summaries from individual files. For
allele frequency maps, specify one `locus` and one `allele`.

```{r summary-patch-map-allele-frequency}
summary_patch_map(
  ex_dir,
  type = "allele_frequency",
  years = 9,
  batch = 0,
  mc = 0,
  locus = "L0",
  allele = "A1",
  crs = 5070
)
```

## Heterozygosity Map

```{r summary-patch-map-heterozygosity}
summary_patch_map(
  ex_dir,
  type = "heterozygosity",
  years = 9,
  batch = 0,
  mc = 0,
  locus = "L0",
  metric = "Ho",
  crs = 5070
)
```

# Older Summary Helpers

## `age_structure_proportions()`

```{r age-structure-proportions}
age_structure_proportions(
  path = paste0(ex_dir, "/"),
  runs = 1,
  gen = 9,
  species = 0
)
```

# Interactive and External-Run Functions

The following functions are exported, but they launch external software
or interactive Shiny apps. They are shown here as code examples but are
not run while knitting this document.

## `launch_cdmetapop()`

```{r launch-cdmetapop, eval=FALSE}
launch_cdmetapop(
  pythonFilepath = "C:/path/to/python.exe",
  CDMetaPOPFilepath = "C:/path/to/CDMetaPOP.py",
  runvarsDirectory = "C:/path/to/example_files/",
  runvarsFilename = "RunVars.csv",
  outputDirectory = "test_output"
)
```

## Template Editors

```{r write-template-functions, eval=FALSE}
write_runvars(output_file = "my_new_runvars.csv")
write_popvars(output_file = "my_new_popvars.csv")
write_patchvars(output_file = "my_new_patchvars.csv")
write_classvars(output_file = "my_new_classvars.csv")
```
