---
title: "Testing Method"
author: "Chris"
date: "2023-07-27"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
# Step 1: Create a data frame with the provided data
data <- read.csv("presence.csv")
```

```{r}
#different data pivot; using the full sheet
library(dplyr)
library(tidyr)

data_long <- data %>%
  gather(key = "Seaweed", value = "Presence", FL_ROCKWEED:FL_OTHER)

```

```{r}
seaweed_counts <- data_long %>%
  group_by(SECTION, Seaweed, Presence) %>%
  tally()

```


```{r}
library(ggplot2)

ggplot(seaweed_counts, aes(x = Seaweed, y = n, fill = Presence)) +
  geom_bar(stat = "identity") +
  labs(title = "Presence/Absence of Seaweeds",
       x = "Seaweed Species",
       y = "Count",
       fill = "Presence") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_wrap(~SECTION, ncol = 2)

```

```{r}
# Assuming your original data frame is named "data"
library(tidyr)

data_long <- data %>%
  pivot_longer(cols = c(BARNACLE, ROCK, ALGAE),
               names_to = "substrate",
               values_to = "percent")

# Printing the first few rows of the resulting data frame
print(data_long)

```

```{r}
# Step 2: Load the required packages and create the stacked bar plot using ggplot2
#library(ggplot2)

ggplot(data_long, aes(x = zonation, y = percent, fill = substrate)) +
  geom_bar(stat = "identity") +
  labs(title = "Section 1- Stacked Bar Plot of Substrate Type by Zonation",
       x = "Zonation",
       y = "Percent",
       fill = "Substrate") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))


```

# Species richness by section and plot
```{r}

# Calculate species richness and standard deviation by section without typing out all column names
species_richness <- data %>%
  rowwise(SECTION) %>%
  summarise(species_count = sum(c_across(FL_ROCKWEED:FL_TAR) == "P"),
            sd = sd(c_across(FL_ROCKWEED:FL_TAR) == "P"))

# Create a bar plot for species richness by section with error bars
ggplot(species_richness, aes(x = factor(SECTION), y = species_count)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(title = "Species Richness by Section",
       x = "Section",
       y = "Species Richness") +
  geom_errorbar(aes(ymin = species_count - sd, ymax = species_count + sd),
                width = 0.2, color = "black", size = 0.7) +
  theme_minimal()

```


```{r}

# Create a bar plot with error bars for species richness by section
ggplot(species_richness, aes(x = factor(SECTION), y = species_count)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  geom_errorbar(aes(ymin = species_count - sd, ymax = species_count + sd),
                width = 0.2, color = "black", size = 0.7) +
  labs(title = "Species Richness by Section",
       x = "Section",
       y = "Species Richness") +
  theme_minimal()


```

## Including Plots

You can also embed plots, for example:

```{r pressure, echo=FALSE}

```

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.