[R] Help with K-Means output

Sat Dec 8 18:39:30 CET 2018

Thank you Bert.

From: Bert Gunter <bgunter.4567 using gmail.com>
Sent: Saturday, December 8, 2018 12:19 PM
To: Bill Poling <Bill.Poling using zelis.com>
Cc: R-help <r-help using r-project.org>
Subject: Re: [R] Help with K-Means output

See David Carlson's reply -- and his advice for learning about how to use lists.

"And I can just join this DF with my original DF used for the KMean, correct?"

Define "join" . See, e.g. http://desktop.arcgis.com/en/arcmap/10.3/manage-data/tables/essentials-of-joining-tables.htm
See also ?merge

I consider it to be your job to learn how to work with R's data structures. There are numerous web tutorials to help you do so. Others may disagree and reply to such queries.

Cheers,
Bert

Bert Gunter

"The trouble with having an open mind is that people keep coming along and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

On Sat, Dec 8, 2018 at 8:43 AM Bill Poling <mailto:Bill.Poling using zelis.com> wrote:
Thank you Bert, I see, so I think this is the process?

set.seed(213)
rr0a1 <- kmeans(rr0, 10)

summary(rr0a1) #Just the cluster
#Length Class  Mode
#cluster      14355  -none- numeric

head(rr0a1$cluster, n=35)
# [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

Xcluster <- as.data.frame(rr0a1$cluster)

head(Xcluster, n=5)
#rr0a1$cluster
# 1             7
# 2             7
# 3             7
# 4             7
# 5             7

tail(Xcluster, n=5)
#rr0a1$cluster
# 14351             6
# 14352             6
# 14353             6
# 14354             6
# 14355             6

And I can just join this DF with my original DF used for the KMean, correct?
The vertical order is the same?

WHP

From: Bert Gunter <mailto:bgunter.4567 using gmail.com>
Sent: Saturday, December 8, 2018 10:46 AM
To: Bill Poling <mailto:Bill.Poling using zelis.com>
Cc: R-help <mailto:r-help using r-project.org>
Subject: Re: [R] Help with K-Means output

Please see ?kmeans and note the "cluster" component of the returned value that would appear to provide the info you seek.

-- Bert

Bert Gunter

"The trouble with having an open mind is that people keep coming along and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

On Sat, Dec 8, 2018 at 7:03 AM Bill Poling <mailto:mailto:Bill.Poling using zelis.com> wrote:
Good afternoon. I hope I have provided enough info to get my question answered.

I am running windows 10 -- R3.5.1 -- RStudio Version 1.1.456

When running a K-Means clustering routine is it possible to get the actual data from each cluster into a DF?

I have reviewed a number of tutorials and unless I missed it somewhere I would like to know if it is possible.

https://www.datacamp.com/community/tutorials/k-means-clustering-r
https://....guru99..../r-k-means-clustering.html
https://datascienceplus.com/k-means-clustering-in-r/
https://datascienceplus.com/finding-optimal-number-of-clusters/
http://enhancedatascience.com/2017/10/24/machine-learning-explained-kmeans/
http://enhancedatascience.com/2017/04/30/r-basics-k-means-r/

For example:

I ran the below and get K-means clustering with 10 clusters of sizes 1511, 1610, 702, 926, 996, 1076, 580, 2429, 728, 3797
Can the 1511 values of SavingsReversed and ProviderID , 1610 values of SavingsReversed and ProviderID, etc.. be run out into DF's?

Thank you for your help.

WHP

str(rr0)
Classes 'data.table' and 'data.frame':14355 obs. of  2 variables:
 $ SavingsReversed: num  0 0 61 128 160 ...
 $ ProviderID     : num  113676 113676 116494 116641 116641 ...
 - attr(*, ".internal.selfref")=<externalptr>

head(rr0, n=35)
    SavingsReversed ProviderID
 1:            0.00     113676
 2:            0.00     113676
 3:           61.00     116494
 4:          128.25     116641
 5:          159.60     116641
 6:          372.66     119316
 7:           18.79     121319
 8:           15.64     121319
 9:            0.00     121319
10:           18.79     121319
11:           23.00     121319
12:           18.79     121319
13:            0.00     121319
14:           25.86     121319
15:           14.00     121319
16:          113.00     121545
17:           50.00     121545
18:         1155.32     121545
19:          113.00     121545
20:          197.20     121545
21:            0.00     121780
22:           36.00     122536
23:         1171.32     125198
24:         1171.32     125198
25:           43.00     125303
26:            0.00     125881
27:           69.64     128435
28:          420.18     128435
29:          175.18     128435
30:           71.54     128435
31:           99.85     128435
32:            0.00     128435
33:           42.75     128435
34:          175.18     128435
35:          846.45     128435

set.seed(213)
rr0a <- kmeans(rr0, 10)
View(rr0a)
summary(rr0a)
# Length Class  Mode
# cluster      14355  -none- numeric
# centers         20  -none- numeric
# totss            1  -none- numeric
# withinss        10  -none- numeric
# tot.withinss     1  -none- numeric
# betweenss        1  -none- numeric
# size            10  -none- numeric
# iter             1  -none- numeric
# ifault           1  -none- numeric

x1 <- as.data.frame(rr0a$centers)
sort(x1)
#SavingsReversed ProviderID
# 2         75.19665  2773789.2
# 3         99.31959  4147091.6
# 5        101.21070  3558532.7
# 4        103.41147  3893274.4
# 1        105.38310  2241031.2
# 8        114.61562  3240701.5
# 10       121.14184  4718727.6
# 9        153.70536  4470878.9
# 6        156.84426  5560636.6
# 7        185.09745   173732.9
print(rr0a)
# K-means clustering with 10 clusters of sizes 1511, 1610, 702, 926, 996, 1076, 580, 2429, 728, 3797
#
# Cluster means:
#   SavingsReversed ProviderID
# 1        105.38310  2241031.2
# 2         75.19665  2773789.2
# 3         99.31959  4147091.6
# 4        103.41147  3893274.4
# 5        101.21070  3558532.7
# 6        156.84426  5560636.6
# 7        185.09745   173732.9
# 8        114.61562  3240701.5
# 9        153.70536  4470878.9
# 10       121.14184  4718727.6
#Within cluster sum of squares by cluster:
# [1] 74529288379846 25846368411171  4692898666512  6277704963344  8428785199973 90824041558798  1468798013919 12143462193009  5483877005233
# [10] 51547955737867
# (between_SS / total_SS =  98.7 %)
#
# Available components:
#
#   [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss" "betweenss"    "size"         "iter"         "ifault"

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