geog176A.Rmd

---
title: ""
output:
  html_document:
    theme: spacelab
---
# {.tabset .tabset-fade}

```{r library, include=FALSE, message=FALSE, warning=FALSE}
library(tidyverse)
library(leaflet)
library(sf)
```


***
## Current Work
***
### 16 Years of Killer Whale Sightings with Reported Location data mapped:

```{r orcamap, warning=FALSE, message=FALSE, echo=FALSE}
orcadata <- read_csv("C:/Users/Catherine/Downloads/orca.csv") %>%
  st_as_sf(coords = c("longitude", "latitude"), crs = 4326)

orcabb <- orcadata %>%
# transform to CRS:5070 and create a 5 km buffer
st_transform(5070) %>%
  st_buffer(5000) %>%
  # get bounding box of buffer and make bbox object a sfc and then sf object
  st_bbox() %>%
  st_as_sfc() %>%
  st_as_sf()

pal <- colorFactor(palette = "RdBu", domain = c(orcadata$orca_type))

lab <- orcadata$orca_type

leaflet(data = orcadata) %>%
  addProviderTiles(providers$Esri.WorldImagery, options = providerTileOptions(opacity = .9)) %>%
  addProviderTiles(providers$Stamen.TonerLines, options = providerTileOptions(opacity = .5)) %>%
  addProviderTiles(providers$CartoDB, options = providerTileOptions(opacity = .7) ) %>%
  addCircleMarkers(data = orcadata, color = ~pal(orca_type),
                   label = lab,
                   popup = leafpop::popupTable(cbind(st_drop_geometry(orcadata)[,4:5], st_drop_geometry(orcadata)[,8]),
                                               feature.id = FALSE, row.numbers = FALSE),
                   radius = ~quantity, fillOpacity = 1.5) %>%
  addMarkers(clusterOptions = markerClusterOptions()) %>%
  addMiniMap() %>%
  addControl("Killer Whale Sightings reported by The Whale Museum", position = "topright")

```

Raw data collected from [the Whale Museums' Hotline](https://hotline.whalemuseum.org/) public API .
Transient or Southern Resident refers to the type of killer whale. Transients consume other mammals including porpoises, sea lions, and whales, while Southern Residents consume only fish and are the only population of killer whales critically endangered. More info can be found [here](https://us.whales.org/whales-dolphins/meet-the-different-types-of-orcas/) .

***

## Previous Work
***
> ## [Creating a FIM with Flood Data from Mission Creek](https://catherinerauch.github.io/geog-176A-labs/lab-06.html)
```{r, echo=FALSE, out.width="40%",out.height="30%",fig.cap="  ",fig.align='left'}
knitr::include_graphics(c("img/lab06plot.png"))
```

- Estimated the number of buildings impacted in the 2017 Santa Barbara flood

- This terrain data was prepped for analysis by aligning object and field data

- Data was loaded into R using Web APIs and the whitebox frontend was used to create a partial Flood Inudation Map (FIM) with structural damage assessment


***

> ## [Examination and Visualization of 2016 Palo, Iowa Flood Data](https://catherinerauch.github.io/geog-176A-labs/lab-05.html)
```{r, echo=FALSE, out.width="40%",out.height="30%",fig.cap="  ",fig.align='left'}
knitr::include_graphics(c("img/lab5plot.png"))
```

- Created multiband raster files to detect/analyze a flood event

- Raster manipulation was used to threshold and classify data to be analyzed

- Flood data from 2016 Palo, Iowa was graphed and examined

***

> ## [Impact of the MAUP on National Dam Inventory data](https://catherinerauch.github.io/geog-176A-labs/lab-04.html)
```{r, echo=FALSE, out.width="50%",out.height="40%",fig.cap="   ",fig.align='left'}
knitr::include_graphics("img/lab4plot.png")
```

- Examined the impacts of different tessellated surfaces and the modifiable areal unit problem (MAUP) 

- Point-in-polygon counts were calculated to aggregate point data and visualize the distribution of dams over the US

- Functions were written to more efficiently report and map information

***

> ## [Exploration of US city data](https://catherinerauch.github.io/geog-176A-labs/lab-03.html)
```{r, echo=FALSE, out.width="50%",out.height="40%",fig.cap="   ",fig.align='left'}
knitr::include_graphics("img/CopyofUSthememap.jpg")
```

- Explored working with sf, sfc, and sfg features & objects

- Coordinate transformations and calculations were made to visualize distances between cities and borders

- Ggplot, gghighlight, and ggrepel were used to create graphics that are clear and interesting 

***

> ## [Analysis of COVID-19 data](https://catherinerauch.github.io/geog-176A-labs/lab-02.html)
```{r, echo=FALSE, out.width="40%",out.height="30%",fig.cap="  ",fig.align='left'}
knitr::include_graphics("img/Copyofcovidplot.jpg")
```

- Data wrangling and visualization with real-time COVID-19 data

- It includes an in-depth look at daily new case data as well as cumulative data at State and County levels

- Pipes and other tidyverse tools were used to manipulate data and present it as legible tables and plots


***