Question 1:
In order to calculate the number of hexagons in each dataset, the
easiest way I could think of at the time was to go to each file in
Excel, and use the sort by largest to smallest according to the
OBJECT_ID column. After searching online, I’ve found the max() argument
which displays the largest number within a column. We’re using the
OBJECT_ID column becauses that relates to the total number of
hexagons.
max(bird$OBJECTID)
## [1] 63890
max(mam$OBJECTID)
## [1] 63890
max(rep$OBJECTID)
## [1] 63890
A three-panel graph showing the distribution of CA Native Birds,
Mammals and Reptiles, shown above.
par(mfrow = c(1, 3))
plot(dar$NtvBird, dar$NtvMamm, xlab = "Number of Birds", ylab = "Number of Mammals", main = "Native CA Mammals & Birds", col = 1)
plot(dar$NtvBird, dar$NtvRept, xlab = "Number of Birds", ylab = "Number of Reptiles", main = "Native CA Reptiles & Birds", col = 4)
plot(dar$NtvMamm, dar$NtvRept, xlab = "Number of Mammals", ylab = "Number of Reptiles", main = "Native CA Reptiles and Mammals", col = 2)

A three panel graph showing the relationship of each of the two
classes of Animals, shown above.
Question 2
A map showing the density of CA Native Birds,
with dark purple regions representing larger populations and white
regions representing areas with low species numbers.
Looking at the map, we can see the darkest shade of purple primarily
are within the Central Valley, as the historic wetland is a great place
for birds during their annual migrations. The areas that are white, are
within desert and high altitude regions, possibly suggesting the low
thermal heat tolerance of birds. This also could suggest the lack of
food and water availabilty in arid regions, which makes it very
difficult for birds to survive with those limiting factors.