---
title: "MARINe Data Visualization " 
author: Chris 
date: "`r format(Sys.time(), '%d %B, %Y')`"\
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---

###READ ME
We will use the following files to explore our data

post.csv - post point raw data
manchester.csv - manchester raw data


Note: Add parameters block/ explanation of choices on render
What I did - will chronicle as things work
OLD csv: presence.csv - Presence/ Absence data of seaweeds (may collapse this further, but it is here as a relic)
OLD csv: data.csv - Full data set without manipulation

# Package Install and call
```{r}

# Installation is commented out, remove hashtag if you need to install
#install.packages("kableExtra")
#install.packages('gridExtra')

```

```{r}

#Use library to call your installed packages for use
library(data.table)
library(dplyr)
library('ggplot2')
library("indicspecies")
library(kableExtra)
library(knitr)
library(RColorBrewer)
library(tidyr)
library(tidyverse)
library(vegan)

```

# Read in data
```{r}
post<- read.csv("post.csv")
manchester<- read.csv("manchester.csv")
```

```{r}

#shows the dimensions of your data frame, a quick check to make sure everything imported the way you like
dim(post)

#quick view of data frame structure
str(post)

#look at the summary of your data
summary(post)

```

```{r}

#shows the dimensions of your data frame, a quick check to make sure everything imported the way you like
dim(manchester)

#quick view of data frame structure
str(manchester)

#look at the summary of your data
summary(manchester)

```