hw update

homework/lab13_hw.Rmd → homework/hw13.Rmd

@@ -33,94 +33,36 @@ We will use two separate data sets for this homework.
 2. The second data set is from Brandell, Ellen E (2021), Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America, Dryad, [Dataset](https://doi.org/10.5061/dryad.5hqbzkh51).  
 
 1. Load the `grizzly` data and evaluate its structure.  
-```{r}
-grizzly <- read_csv(here("lab13", "data", "bear-sightings.csv"))
-```
 
-```{r}
-summary(grizzly)
-```
 
 2. Use the range of the latitude and longitude to build an appropriate bounding box for your map.  
-```{r}
-lat <- c(55.02, 70.37)
-long <- c(-131.3, -166.2)
-bbox <- make_bbox(long, lat, f = 0.05)
-```
+
 
 3. Load a map from `stamen` in a terrain style projection and display the map.  
-```{r}
-map1 <- get_map(bbox, maptype = "terrain", source = "stamen")
-```
 
-```{r}
-ggmap(map1)
-```
 
 4. Build a final map that overlays the recorded observations of grizzly bears in Alaska.  
-```{r}
-ggmap(map1) + 
-  geom_point(data = grizzly, aes(longitude, latitude), size=0.8) +
-  labs(x = "Longitude", y = "Latitude", title = "Ursos arctos")
-```
+
 
 Let's switch to the wolves data. Brandell, Ellen E (2021), Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America, Dryad, [Dataset](https://doi.org/10.5061/dryad.5hqbzkh51).  
 
 5. Load the data and evaluate its structure.  
-```{r}
-wolves <- read_csv(here("lab12", "data", "wolves_data", "wolves_dataset.csv"))
-```
 
-```{r}
-glimpse(wolves)
-```
 
 6. How many distinct wolf populations are included in this study? Mae a new object that restricts the data to the wolf populations in the lower 48 US states.  
-```{r}
-wolves %>% count(pop)
-```
 
-```{r}
-us_wolves <- 
-  wolves %>% 
-  filter(pop %in% c("GTNP", "MEXICAN", "MI", "MT", "SNF", "YNP"))
-```
 
 7. Use the range of the latitude and longitude to build an appropriate bounding box for your map.  
-```{r}
-us_wolves %>% 
-  select(lat, long) %>% 
-  summary()
-```
 
-```{r}
-lat <- c(33.69, 47.75)
-long <- c(-110.99, -86.82)
-bbox2 <- make_bbox(long, lat, f = 0.05)
-```
 
 8.  Load a map from `stamen` in a `terrain-lines` projection and display the map.  
-```{r}
-map2 <- get_map(bbox2, maptype = "terrain-lines", source = "stamen")
-```
 
-```{r}
-ggmap(map2)
-```
 
 9. Build a final map that overlays the recorded observations of wolves in the lower 48 states.  
-```{r}
-ggmap(map2) + 
-  geom_point(us_wolves, mapping=aes(x=long, y=lat), size=2)+
-  labs(x = "Longitude", y = "Latitude", title = "Wolves in the Lower 48")
-```
+
 
 10. Use the map from #9 above, but add some aesthetics. Try to `fill` and `color` by population.  
-```{r}
-ggmap(map2) + 
-  geom_point(us_wolves, mapping=aes(x=long, y=lat, fill=pop, color=pop), size=3)+
-  labs(x = "Longitude", y = "Latitude", title = "Wolves in the Lower 48")
-```
+
 
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