index.rmd

---
title: "Data Vizualization"
output: html_document
---

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

I used the following Libraries for this page
```{r warning=FALSE, message=FALSE}
library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)
library(leaflet)
library(DT)

# these libraries have their own filter,lag functions. to call stats file, use   stats::filter()

```


#Reformat Data for Plotting


data from : https://knb.ecoinformatics.org/view/doi:10.5063/F1S46Q6M
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, echo=F}
esc <-  read.csv(url("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3Af119a05b-bbe7-4aea-93c6-85434dcb1c5e", method="libcurl"), stringsAsFactors = FALSE)
```

* calculate annual escapement by species and region
* filter for just pacific salmon (Sockeye, Chinook, coho, pink, chum)

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

#head(annual_esc)

```

```{r}

```

```{r}


```

#Generate some static plots


```{r }
ggplot(annual_esc, aes(x = Species, y = count)) +
    geom_col( fill="lavender")
```


```{r}
ggplot(annual_esc) +
    geom_col(aes(x = Species, y = count), fill="purple")
```



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

## just plot Kodiak
```{r}
ggplot(filter(annual_esc, SASAP.Region == "Kodiak"), aes(x = year, y = count, color = Species)) +
    geom_line() +
    geom_point() +
    ylab("Escapement (num of fish)") +
    xlab("year") +
    ggtitle("Kodiak Salmon Escapement") +
    theme_test() +
    theme(legend.position="bottom")
    
```

```{r}
my_theme <- theme_test() +
    theme(legend.position="bottom",
    legend.title=element_blank())
```


```{r}
ggplot(filter(annual_esc, SASAP.Region == "Kodiak"), aes(x = year, y = count, color = Species)) +
    geom_line() +
    geom_point() +
    ylab("Escapement (num of fish)") +
    xlab("year") +
    ggtitle("Kodiak Salmon Escapement") +
    my_theme
    
```

##Mulitple plts

```{r fig.width = 7, fig.height=8}
p <- ggplot(annual_esc, aes(x = year, y = count, color = Species)) +
    geom_line() +
    geom_point() +
    scale_y_continuous(label=comma) +
    facet_wrap(~SASAP.Region, scales = "free_y", ncol=2) +
    ylab("Escapement") +
    my_theme

p

ggsave("figures/regional_escapement.png",
       plot=p,
       width=7,
       height=8,
       units = "in")
     
```




# Create Interactive Map


* map the sampling locations  for escapement
* find distinct lat/lon combinations


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

```


```{r, echo=F}
datatable(locations)
```


### now make the map
<br/>
<br/>
<br/>
<br/>
<br/>
```{r}
leaflet(locations) %>% 
    addTiles() 
    
    
```
```{r}
leaflet(locations) %>% 
    addTiles() %>% 
    addMarkers(lng = ~Longitude, lat= ~ Latitude, popup = ~ Location)
    
```

```{r}
leaflet(locations) %>% 
    addTiles(urlTemplate="http://mt0.google.com/vt/lyrs=m&hl=en&x={x}&y={y}&z={z}&s=Ga") %>% 
    addMarkers(lng = ~Longitude, lat= ~ Latitude, popup = ~ Location)
    
```


leaflet() %>% addTiles(urlTemplate = "https://mts1.google.com/vt/lyrs=s&hl=en&src=app&x={x}&y={y}&z={z}&s=G", attribution = 'Google')