---
title: "Poster Visualizations"
author: "Chris"
date: "2023-12-18"
output: html_document
output: 
  html_document: 
  theme: readable 
  highlight: zenburn 
    toc: true 
    toc_float: true 
    number_sections: true 
    code_folding: show 
    code_download: true 
editor: 
markdown: 
wrap: 72 
---

```{r}
#load packages
library(data.table)
library(dplyr)
library('ggplot2')
library("indicspecies")
library(kableExtra)
library(knitr)
library(RColorBrewer)
library(tidyr)
library(tidyverse)
library(vegan)
```

```{r}
#load data
algae<- read.csv("algaefull.csv")
head(algae)
```

```{r}
#pie charts for algae
#clean up the data first
```

```{r}
df <- algae[1:482, ] %>% select(-NUMBER, -ASSIGNED_NUMBER)

rockweed_section1_L <- df %>%
  filter(SECTION == 1, ZONATION == 'L') %>%
  summarise(rockweed_sum = sum(ROCKWEED, na.rm = TRUE)) 

rockweed_section1_L
```

```{r}
result <- algae %>%
  group_by(SECTION) %>%
  summarise(across(5:19, sum, na.rm = TRUE))








# remove number and assigned number columns & stop the data at line 482
df <- df[, !(names(df) %in% c("number", "assigned_number"))]
df <- df[1:482, ]

# can you sum the number of times "ulva" appears across each section and zonation?
df_ulva_sum <- df %>%
  group_by(section, zonation) %>%
  summarise(ulva_sum = sum(ulva, na.rm = TRUE), .groups = 'drop')


# Convert data types and handle missing values if any
algae <- algae %>%
  mutate(across(everything(), as.factor)) %>%
  replace(is.na(.), 0)  # Replace NA values with 0 (assuming NA means absence)

# Summarize data for each SECTION and ZONATION
summary_data <- ALGAE %>%
  group_by(SECTION, ZONATION) %>%
  summarize_all(sum) %>%
  gather(key = "ALGAE_TYPE", value = "COUNT", -SECTION, -NUMBER, -ASSIGNED_NUMBER, -ZONATION)


```

```{r}
#species richness and diversity
```