## Lab Worksheet 5

In 1898, Hermon Bumpus, an American biologist working at Brown University, collected data on one of the first examples of natural selection directly observed in nature. Immediately following a bad winter storm, he collected 136 English house sparrows, Passer domesticus, and brought them indoors. Of these birds, 64 had died during the storm, but 72 recovered and survived. By comparing measurements of physical traits, Bumpus demonstrated physical differences between the dead and living birds. He interpreted this finding as evidence for natural selection as a result of this storm:

``````bumpus <- read.csv("http://wilkelab.org/classes/SDS348/data_sets/bumpus_full.csv")
``````##    Sex   Age Survival Length Wingspread Weight Skull_Length Humerus_Length
## 1 Male Adult    Alive    154        241   24.5         31.2           17.4
## 2 Male Adult    Alive    160        252   26.9         30.8           18.7
## 3 Male Adult    Alive    155        243   26.9         30.6           18.6
## 4 Male Adult    Alive    154        245   24.3         31.7           18.8
## 5 Male Adult    Alive    156        247   24.1         31.5           18.2
## 6 Male Adult    Alive    161        253   26.5         31.8           19.8
##   Femur_Length Tarsus_Length Sternum_Length Skull_Width
## 1         17.0          26.0           21.1        14.9
## 2         18.0          30.0           21.4        15.3
## 3         17.9          29.2           21.5        15.3
## 4         17.5          29.1           21.3        14.8
## 5         17.9          28.7           20.9        14.6
## 6         18.9          29.1           22.7        15.4``````

The data set has three categorical variables (`Sex`, with levels `Male` and `Female`, `Age`, with levels `Adult` and `Young`, and `Survival`, with levels `Alive` and `Dead`) and nine numerical variables that hold various aspects of the birdsâ€™ anatomy, such as wingspread, weight, etc.

Question 1: Perform a PCA on the numerical columns of this data set. Then make three plots potting the data as PC2 vs.Â PC1, colored by (i) sex, (ii) age, (iii) survival.

``````# do PCA
bumpus %>% select(-Sex, -Age, -Survival) %>%
scale() %>%
prcomp() ->
pca

bumpus.pca <- data.frame(bumpus, pca\$x)  # combine original data frame with PCs

# plot by sex
ggplot(bumpus.pca, aes(x=PC1, y=PC2, color=Sex)) + geom_point()``````

``````# plot by age
ggplot(bumpus.pca, aes(x=PC1, y=PC2, color=Age)) + geom_point()``````

``````# plot by survival
ggplot(bumpus.pca, aes(x=PC1, y=PC2, color=Survival)) + geom_point()``````

Question 2: Now visualize the rotation matrix of the PCA obtained under Question 1.

From the worksheet to class 9:

``````# capture the rotation matrix in a data frame
rotation_data <- data.frame(pca\$rotation, variable=row.names(pca\$rotation))
# define a pleasing arrow style
arrow_style <- arrow(length = unit(0.05, "inches"),
type = "closed")
# now plot, using geom_segment() for arrows and geom_text for labels
ggplot(rotation_data) +
geom_segment(aes(xend=PC1, yend=PC2), x=0, y=0, arrow=arrow_style) +
geom_text(aes(x=PC1, y=PC2, label=variable), hjust=0, size=3, color='red') +
xlim(-1.,1.25) +
ylim(-1.,1.) +
coord_fixed() # fix aspect ratio to 1:1``````