Scripps_HAB.Rmd

---
title: "Harmful Algal Bloom (HAB) Event Information from 2011 to 2020 for the Scripps Pier in San Diego California"
author: "Felicia Cruz, Joe Decesaro, Steven Cognac"
date: "10/16/2021"
output: 
  html_document:
    theme: flatly
    code_folding: show
    toc: true
    toc_float: true
---

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

library(here)
library(tidyverse)
library(metajam)
library(lubridate)

```
We are using `metajam` to download HAB data for the CA coast from DataOne. We wil specifically filter for locations at the Scripps Pier in La Jolla.

"California Harmful Algal bloom Monitoring and ALert Program Data"

https://search.dataone.org/view/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fedi%2F988%2F4#https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fdata%2Feml%2Fedi%2F988%2F4%2F4119639092e62c55ea8be348e4d9260d


```{r}
# set file path
path <- here()

```


# Data Download

## Event, Occurrence, and Extended Measurement Data.

```{r, eval=FALSE}

# Only run if data not already downloaded locally
# Or alternatively, delete three "data pasta" folders and run all.

# Event Data Download
event_data_url <- "https://cn.dataone.org/cn/v2/resolve/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fdata%2Feml%2Fedi%2F988%2F4%2F4119639092e62c55ea8be348e4d9260d"
event_data <- download_d1_data(event_data_url, path)


# Occurrence Data Download
occ_data_url <- "https://cn.dataone.org/cn/v2/resolve/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fdata%2Feml%2Fedi%2F988%2F4%2F7ad56af78ac5f73d81f5e80293f1d6c9"
occ_data <- download_d1_data(occ_data_url, path)


# Extended Measurement Download
ext_data_url <- "https://cn.dataone.org/cn/v2/resolve/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fdata%2Feml%2Fedi%2F988%2F4%2Ff616903aa9a2592cb3082cb3b9525270"
ext_data <- download_d1_data(ext_data_url, path)
```

```{r}

# returns a list of all of the data in the folder 
all_event_data <- metajam::read_d1_files(here("https_pasta.lternet.edu_package_metadata_eml_edi_988_4__event__csv"))

all_occ_data <- read_d1_files(here("https_pasta.lternet.edu_package_metadata_eml_edi_988_4__occurrence__csv"))

all_ext_data <- read_d1_files(here("https_pasta.lternet.edu_package_metadata_eml_edi_988_4__extendedmeasurementorfact__csv"))


```
## View metadata

### Occurrence Data
- From looking at the data and metadata, this includes organism information 
```{r, error = TRUE}

View(all_occ_data$attribute_metadata)
view(all_occ_data$data)
```

### Extended Measurement Data 
- From the data and metadata, this contains information about measurement type, value, etc.  
```{r, error = TRUE}

View(all_ext_data$attribute_metadata)
View(all_ext_data$data)
```


# Data Analysis

## Event data filtering for Scripps Pier only
- By looking at the metadata, this includes location and geometric info 
- Filter by single location; Scripps Pier in La Jolla, CA.

```{r, error=TRUE}

# view all event data
View(all_event_data$data)

# filter for ScrippsPier
scripps <- all_event_data$data %>% 
  filter(locationID == "HABs-ScrippsPier") %>% 
  data.frame()

# View used to double check creation of dataframe. Not necessary for workflow
# view(scripps)

occ <- all_occ_data$data
ext <- all_ext_data$data

```

## Join data sets together.  
```{r}
# joins Scripps event data and occurrence data
scripps_1 <- left_join(scripps, occ, by = "id")

# joins Scripps event data/occurrence data with extended measurement data
scripps_combined <- left_join(scripps_1, ext, by = "id")
```


```{r}

# Remove columns unrelated to our anticipated analysis on HABs
scripps_combined <- scripps_combined %>% 
  
  # select columns of interest
  select(-c(eventID, locationID, eventRemarks, basisOfRecord, decimalLatitude, decimalLongitude,minimumDepthInMeters, maximumDepthInMeters,coordinateUncertaintyInMeters, measurementID, measurementTypeID, measurementUnitID)) %>% 
  
  # remove NA values
  drop_na() %>% 
  
  # create year column
  mutate(year = lubridate::year(eventDate))
  
```


## Export new dataset as .csv
```{r}
scripps_data <- write.csv(scripps_combined, here("data", "scripps_pier_HAB_data.csv"), row.names = TRUE)
```


# HAB Analysis

## Create subset of Scripps data of algae totals by year and scientific names.
```{r}
# create yearly organism total based on scientific name.
scripps_quantity <- scripps_combined %>% 
  group_by(year, scientificName) %>% 
  summarize(Organism_sum = sum(organismQuantity)) %>% 
  mutate(year = as.factor(year))
scripps_quantity

# create subset of unique events grouped by year and id.
scripps_events <- scripps_combined %>% 
  group_by(year) %>% 
  summarize(event = unique(id)) %>% 
  mutate(year = as.factor(year))
scripps_events
  
```

### Stacked Bar Graph of algae count totals per year.
```{r}
plot1 <- ggplot(data = scripps_quantity, aes(fill = scientificName, y = Organism_sum, x = year)) +
  geom_bar(position = "stack", stat = "identity") +
  labs(title = "Scripps Pier Algae Counts from 2011 to 2020",
       x = "Year",
       y = "Organism Counts (cells/L)")
plot1

plot1 <- ggsave(here("figs", "Scripps_bar_graph.png"))
```



### Line plot of algae counts per year based on species.
```{r}
plot2 <- ggplot(data = scripps_combined, aes(x = eventDate, y = organismQuantity)) +
  geom_line(aes(color = scientificName)) +
  ylim(0,25000) +
  labs(x = "Date",
       y = "Organism Counts",
       title = "Scripps Pier Algae Counts from 2011 to 2020")
plot2
ggsave(here("figs", "Scripps_line_graph.png"))
```