```{r global_options, include=FALSE}
library(knitr)
opts_chunk$set(fig.align="center", fig.height=3, fig.width=4)
set.seed(456) # make document knit the same every time
```
## In-class worksheet 10
**Feb 21, 2019**
In this worksheet, we will use the libraries tidyverse and ggthemes:
```{r message=FALSE}
library(tidyverse)
theme_set(theme_bw(base_size=12)) # set default ggplot2 theme
library(ggthemes)
```
# 1. k-means clustering of the iris data set
We can do k-means clustering in R using the function `kmeans()`. The primary argument to this function is `centers`, which tells the function how many cluster centers we want to use:
```{r}
iris %>%
select(-Species) %>% # remove Species column
kmeans(centers=3) -> # do k-means clustering with 3 centers
km # store result as `km`
# now display the results from the analysis
km
```
The cluster means give us the locations of the centroids of the three clusters. We can access them directly via `km$centers`:
```{r}
km$centers
```
The clustering vector tells us for each data point to which cluster it belongs. We can access this vector directly via `km$cluster`:
```{r}
km$cluster
```
We can add this vector to the iris data frame and then plot, coloring by cluster and using different symbol shapes according to species, for comparison:
```{r}
# we use `factor(km$cluster)` to tell R that the cluster numbers represent distinct categories,
# not continuous values
iris_clustered <- data.frame(iris, cluster = factor(km$cluster))
ggplot(
iris_clustered,
aes(x = Petal.Width, y = Sepal.Width, color = cluster, shape = Species)
) +
geom_point() +
scale_color_colorblind()
```
Let's do everything once more:
```{r}
iris %>% select(-Species) %>% kmeans(centers = 3) -> km
iris_clustered <- data.frame(iris, cluster = factor(km$cluster))
ggplot(
iris_clustered,
aes(x = Petal.Width, y = Sepal.Width, color = cluster, shape = Species)
) +
geom_point() +
scale_color_colorblind()
```
You see that the results do not look the same. This happens because the k-means algorithm uses a random starting point. To make the results less random, we can set the parameter `nstart` to a value other than 1, e.g. `nstart=10`:
```{r}
iris %>% select(-Species) %>% kmeans(centers = 3, nstart = 10) -> km
iris_clustered <- data.frame(iris, cluster = factor(km$cluster))
ggplot(
iris_clustered,
aes(x = Petal.Width, y = Sepal.Width, color = cluster, shape = Species)
) +
geom_point() +
scale_color_colorblind()
```
To determine the best number of clusters in a systematic way, we can plot the within-groups sum of squares against the number of clusters and look for a characteristic slowdown in the decline:
```{r}
iris_numeric <- select(iris, -Species) # make a data table with only the numeric measurements from iris
# calculate the weighted sum squares (wss) for a single cluster
wss <- (nrow(iris_numeric)-1)*sum(apply(iris_numeric, 2, var))
# now calculate the weighted sum squares for all cluster numbers from 2 to 15
for (i in 2:15) {
wss[i] <- sum(
kmeans(
iris_numeric,
nstart = 10,
centers = i
)$withinss
)
}
# turn wss data into a data frame for plotting
wss_data <- data.frame(centers = 1:15, wss)
# make plot
ggplot(wss_data, aes(x = centers, y = wss)) +
geom_point() +
geom_line() +
xlab("Number of Clusters") +
ylab("Within groups sum of squares")
```
We see that around 3 clusters, the decline slows down a lot. We would then choose 3 or 4 clusters for this data set.
For more information about choosing the number of clusters, read [this post](http://stackoverflow.com/a/15376462/4975218) on stackoverflow.com.
# 2. Clustering the biopsy data set
Take the `biopsy` data set, cluster it into 2, 3, or 4 clusters, and display the clusters in principal-component space. (In other words, plot PC2 vs PC1 and color by cluster. You can set the plotting symbol by outcome, to see how clustering compares with known outcomes.)
```{r}
# load in the biopsy data set
biopsy <- read.csv("http://wilkelab.org/classes/SDS348/data_sets/biopsy.csv")
head(biopsy)
# do PCA on biopsy data set
biopsy %>%
select(-outcome) %>%
scale() %>%
prcomp() ->
pca
```
Hint: To create the plot, you will have to make a new data frame out of the pca coordinates (`pca$x`) and the clustering results (`km$cluster`).
```{r}
# your R code goes here
```
# 3. If this was easy
For the `biopsy` dataset, plot the within-group sum of squares against the number of clusters, to determine the appropriate number of clusters.
```{r}
# your R code goes here
```
Now, go back to the original clustering of the `iris` data set, with 3 clusters:
```{r}
iris %>%
select(-Species) %>%
kmeans(centers = 3, nstart = 10) ->
km
iris_clustered <- data.frame(iris, cluster = factor(km$cluster))
ggplot(
iris_clustered,
aes(x = Petal.Width, y = Sepal.Width, color = cluster, shape = Species)
) +
geom_point() +
scale_color_colorblind()
```
Take this plot and add to it the location of the centroids for each cluster, plotted as large, colored circles with a black outline.
```{r}
# your R code goes here
```