archy-208-module-7-bmarwick.qmd

---
title: "Untitled"
format: html
editor: visual
---

## Introduction

The aim of this report ...

Q1.

Q2.

Q3.


```{r}
library(tidyverse)
library(here)
library(sf)
library(terra) # was raster
library(spatstat)
library(maptools)

unzip("data.zip", exdir = "data")

# read in the spatial data files
survey_area <- 
  st_read(here("data/survey_area/survey_area.shp"))

ahu <- 
  st_read(here("data/ahu/ahu.shp"))

water <- 
  st_read(here("data/water/water.shp"))

rock_mulch <-
  rast(here("data/mulch/mulch.tif"))

# prepare inline code
how_many_ahu <- nrow(ahu)

```


... `r how_many_ahu`

The data come from DiNapoli et al. (2019)

## Exploratory data visualation


```{r}
#| label: fig-overview-map
#| fig-cap: "change me!"

# convert raster to df to use with ggplot
rock_mulch_df <- 
  as.data.frame(rock_mulch, xy = TRUE)


ggplot() +
  geom_sf(data = survey_area) +
  geom_raster(data = rock_mulch_df,
              aes(x = x,
                  y = y,
                  fill = mulch)) +
  geom_sf(data = ahu,
          colour = "red",
          shape = 15) +
  geom_sf(data = water,
          colour = "grey") +
  theme_minimal()

```

In @fig-overview-map we can see...

```{r}
# convert data to use with spatstat functions

survey_win <- 
  as.owin(survey_area)

ahu_pp <- ppp(ahu$POINT_X,
              ahu$POINT_Y,
              window = survey_win)

water_pp <- ppp(water$POINT_X,
                water$POINT_Y,
                window = survey_win)

water_pp <- unique(water_pp)

# create distances map for water
water_dist <- distmap(water_pp)

# create distance map for lithic mulch
mulch_dist <- as.im(rock_mulch_df)

```


## Hypothesis testing

```{r}
#| label: fig-hyp-testing 
#| fig-cap: "change me!"
#| fig-subcap: 
#|   - "Water"
#|   - "Mulch"
#| layout-ncol: 2


# freshwater
ahu_water_cdf <- cdf.test(ahu_pp, 
                          water_dist, 
                          alternative = "greater")

ahu_water_cdf_pval <- 
  round(ahu_water_cdf$p.value, 4)

plot(ahu_water_cdf,
     style = "cdf",
     ylab = "Probability",
     xlab = "Distance to freshwater")

# lithic mulch gardens
ahu_mulch_cdf <- 
  cdf.test(ahu_pp,
           mulch_dist,
           alternative = "greater")

ahu_mulch_cdf_pval <- 
  round(ahu_mulch_cdf$p.value, 4)

plot(ahu_mulch_cdf,
     style = "cdf",
     ylab = "Probability",
     xlab = "Distance to Rock Mulch")
  
```

In @fig-hyp-testing  we can see ...