• emacs 27
• org-mode
• org-babel
• ess (Emacs speaks statistics)

<s <tab> R

#+BEGIN_SRC R

#+END_SRC

library(dplyr)
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

gets us

K-means clustering with 3 clusters of sizes 50, 62, 38

Cluster means:
  Sepal.Length Sepal.Width Petal.Length Petal.Width
1     5.006000    3.428000     1.462000    0.246000
2     5.901613    2.748387     4.393548    1.433871
3     6.850000    3.073684     5.742105    2.071053

Clustering vector:
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3 3 3 3 2 3 3 3 3
[112] 3 3 2 2 3 3 3 3 2 3 2 3 2 3 3 2 2 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3 3 2 3
[149] 3 2

Within cluster sum of squares by cluster:
[1] 15.15100 39.82097 23.87947
 (between_SS / total_SS =  88.4 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
[6] "betweenss"    "size"         "iter"         "ifault"   

in the \*R:<location>\* buffer.

The source executions are contiguous

km$centers

  Sepal.Length Sepal.Width Petal.Length Petal.Width
1     5.006000    3.428000     1.462000    0.246000
2     5.901613    2.748387     4.393548    1.433871
3     6.850000    3.073684     5.742105    2.071053

Plotting

# add species information back into PCA data
# 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))

library('ggplot2')
ggplot(iris_clustered, aes(x=Petal.Width, y=Sepal.Width, color=cluster, shape=Species)) + geom_point()
ggsave("irisclusters.png")

later I should do some analysis to public retinal fundus image corpora.