05_wind_turbines.Rmd

# Wind Turbine Locations 

This data on wind turbine locations comes via the #TidyTuesday project.  I map the positions of the turbines in the US. 

```{r, echo = F}

dir <- "raw_data"
file <- paste0(dir, "/", "us_wind.csv")
url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-11-06/us_wind.csv"

# create raw_data folder
if (!dir.exists(dir)) { dir.create(dir) }

# download data from internet and save
if (!file.exists(file)) { download.file(url = url, destfile = file) }

# read in downloaded data
us_wind <- readr::read_csv(file)
```
A random sample from the data set: 
```{r, echo = F}
knitr::kable(head(us_wind, 5), format = "html")
```


```{r, echo = F}
usa <- map_data("state")
```



---
  
```{r wind_01, eval=F, echo=F}
ggplot(data = usa) +  
  aes(long, lat, group = group) + 
  geom_polygon(fill = "blue", alpha = .4) + 
  theme_classic() +
  coord_map(projection = "mercator") +
  geom_point(data = us_wind %>% filter(ylat < 50 & ylat > 25), mapping = aes(xlong, ylat, group = NULL), size = .5, col = "orange", alpha = .6) + 
  theme_void() + 
  labs(title = "Wind turbines in the continental US") + 
  labs(caption = "Tidy Tuesday exercise") +
  labs(subtitle = "Data Source: | Visualization: Gina Reynolds") 
```


`r apply_reveal("wind_01")`


---

```{r, echo=F}
data <- us_wind %>% 
  filter(ylat < 50 & ylat > 25)
```


```{r wind_02, eval=F, echo=F}
  ggplot(data) +
  aes(xlong, ylat) + 
  borders("state") +
  geom_point(col = "steelblue", alpha = .2) +
  theme_void() +
  geom_density_2d(col = "darkgrey") + 
  labs(title = "Wind turbines in the continental US") + 
  labs(caption = "Tidy Tuesday exercise") +
  labs(subtitle = "Data Source: | Visualization: Gina Reynolds")
```

`r apply_reveal("wind_02")`

---