SWMPrats_pres.Rmd

---
title: "Open Science tools for the National Estuarine Research Reserve System"
author: "[Dr. Marcus Beck](mailto:marcusb@sccwrp.org) $\\bullet$ [\\@fawda123](https://twitter.com/fawda123)	$\\bullet$ [#TampaBayOpensci](https://twitter.com/hashtag/TampaBayOpenSci?src=hashtag_click)"
institute: "Southern California Coastal Water Research Project"
date: "9/5/2019"
output:
  xaringan::moon_reader:
    lib_dir: libs
    nature:
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
---

```{r, message = F, echo = F, warning = F}
library(knitr)

# global knitr options
opts_chunk$set(message = FALSE, dev.args = list(family = 'serif'), echo = F, warning = F)

# chunk hook for par
knit_hooks$set(par = function(before, options, envir){
  if (before && options$fig.show!='none') par(mar=c(4,4,.1,.1))
})

library(SWMPr)
library(ggplot2)

theme_mine <- function (base_size = 12, base_family = "") {
  theme_bw(base_size = base_size, base_family = base_family) %+replace% 
  theme(
    plot.background = element_rect(fill='transparent', 
      colour = NA),
    panel.background = element_rect(fill='transparent', 
      colour = NA),
    legend.background = element_rect(fill='transparent', 
      colour = NA),
    # strip.background = element_rect(fill = 
        # alpha(pal(5)[5],0.5)),
    legend.key = element_rect(fill = 'transparent', 
      colour = NA)
    )   
}

# set as default
theme_set(theme_mine())
```

class: center, middle

# The NERRS network

```{r, echo = F, out.width = '100%'}
knitr::include_graphics("figure/NERRS_locations.png")
```

---

# The need for open science at NERRS

#### NERRS researchers, managers, technicians, and stakeholders need more quantitative tools for handling these data: 

* Understand regional and national trends while retaining the ability to determine local trends

* Train users

* Maintain a versatile and evolving data analysis approach

* Create a community of practice

---

# Genesis of SWMPrats

```{r, echo = F}
knitr::include_graphics('figure/bg_main.jpg')
```

#### One-day training workshop at 2014 annual meeting

* Attended by over 70 NERRS staff, representing 19 of 28 reserves

* General focus on time series analysis, simple application with SWMP data

* Pre/post workshop materials including An R package for SWMP

---

# Genesis of SWMPrats

```{r, echo = F}
knitr::include_graphics('figure/swmprats_logo.png')
```

#### A working group was formed from this meeting

## __S__ystem- __W__ide __M__onitoring __P__rogram __R__esources for the __A__nalysis of __T__ime __S__eries

#### [SWMPrats.net](SWMPrats.net) is our base of operations...

---

class: center, top

# SWMPrats.net

A website with information and tools for SWMP data analysis

```{r, echo = F, out.width = '80%'}
knitr::include_graphics('figure/swmprats_home.png')
```

---

# SWMPrats.net: The SWMPr package

```{r, echo = F}
knitr::include_graphics('figure/swmpr_logo.png')
```

SWMPr is an open-source R package for working with SWMP data

```{r eval = F}
# install/load from R
install.packages('SWMPr')
library(SWMPr)
```


* Dealing with "bad" data

* Subsetting by date ranges, parameters

* Combining data from different sites

* Standardizing time steps

* ...not to mention the analysis

---

# SWMPrats.net: Interactive web apps

.pull-left[
The most common question - what's happened at my site over time? 

Three Shiny applications allow users to visualize trends in SWMP data, these apps allow *reactive* use of SWMPr functions

<br></br>
```{r, out.width = "70%"}
knitr::include_graphics('figure/swmp_summary.png')
```
]

.pull-right[
```{r, out.width = "80%"}
knitr::include_graphics('figure/swmp_comp.png')
```
<br></br>
```{r, out.width = "80%"}
knitr::include_graphics('figure/swmp_agg.png')
```
]

---

# Why should you care?

* These tools will be used! To date, SWMPr has had over __13k__ downloads!

--

* Increased mileage of data - more users looking at more questions = more answers!

--

* Develop a community of practice - shared workflows promote collaboration and encourage group learning!

--

* More group learning means collective benefits for all - more efficient use of resources!