index.Rmd

---
title: "Index"
author: "Patricia DeRepentigny"
date: "1/17/2019"
output: html_document
---

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

```{r, echo=F}
library(dplyr)
library(tidyr)
library(ggplot2)
library(DT)
library(leaflet)
```

# Data Tidying

Read in escapement data sourced from: Alaska Department of Fish and Game, Division of Commercial Fisheries. 2017. Daily salmon escapement counts from the OceanAK database, Alaska, 1921-2017. Knowledge Network for Biocomplexity. doi:10.5063/F1S46Q6M.

```{r, results=F}
esc <- read.csv("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3Af119a05b-bbe7-4aea-93c6-85434dcb1c5e",
                stringsAsFactors = FALSE)

head(esc)
```

Calculate annual, regional total escapement by species:  
* `separate` the date into year - month - day  
* `group_by` and `summarize` to calculate the sum over unique region-year-species combinations  
* `filter` for salmon species

```{r}
annual_esc <- esc %>% 
  separate(sampleDate, into = c("year", "month", "day"), sep = "-") %>% 
  mutate(year = as.numeric(year)) %>% 
  group_by(SASAP.Region, Species, year) %>% 
  summarise(escapement = sum(DailyCount)) %>% 
  filter(Species %in% c("Chinook", "Sockeye", "Pink", "Chum", "Coho"))
```

```{r}
datatable(annual_esc)
```
# Static Plots

```{r}
ggplot(data = annual_esc, mapping = aes(x = Species, y = escapement, fill = SASAP.Region)) +
  geom_col()
```

Timeseries of Kodiak escapement by species.

```{r}
my_theme <- theme_bw() + 
  theme(plot.title = element_text(hjust = 0.5))

ggplot(data = filter(annual_esc, SASAP.Region == "Kodiak"), 
       aes(x = year, y = escapement, color = Species)) + 
  geom_line() + 
  geom_point() +
  ylab("Escapement (number of fish)") +
  ggtitle("Kodiak Salmon Escapement") +
  my_theme
  
```

Plot timeseries of escapement by species for all regions

```{r, fig.height = 10, fig.width = 8}
p <- ggplot(annual_esc, aes(x = year, y = escapement, color = Species)) +
  geom_line() +
  geom_point() + 
  facet_wrap(~SASAP.Region, scales = "free", ncol = 2) +
  my_theme

p

ggsave("Figures/region_escapement.eps", plot = p, height = 10, width = 8, units = "in")
```

Write derived data to a csv file

```{r}
write.csv(annual_esc, "Derived_data/annual_escapement.csv", row.names = F)
```

# Interactive Map

Make a map using leaflet:  
* Find unique location with lat/lon values

```{r}
locations <- esc %>% 
  distinct(Location, Latitude, Longitude) %>% 
  drop_na()

datatable(locations)
```
Create our interactive map

```{r}
leaflet(locations) %>% 
  addTiles() %>% 
  addMarkers(~Longitude, ~Latitude, popup = ~Location)
```