How to define proper breaks in RFM analysis

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How to define proper breaks in RFM analysis

hemantsain55
Hello,
I'm working on RFM analysis and i wanted to define my own breaks but my
frequency distribution is not normally distributed so when I'm using
quartile its not giving the optimal results.
so I'm looking for a better approach where i can define breaks dynamically
because after visualization i can do it easily but i want to apply this
model so that it can automatically define the breaks according to data set.
I'm attaching sample data for reference.

Thanks

                           *Freq*
5
15
1
8
2
2
2
1
1
2
2
1
1
1
1
4
10
22
24
1
1
2
2
7
1
1
4
6
1
1
2
2
17
2
7
1
5
2
7
7
1
4
13
4
1
9
1
1
1
3
2
1
1
2
4
5
8
4
14
18
4
1
1
1
7
1
1
1
1
10
6
4
6
1
3
13
2
4
8
11
2
4
6
1
1
3
1
2
3
2
2
5
2
1
1
1
3
2
3
1
2
9
34
8
1
1
1
1
2
1
5
4
9
1
1
4
2
2
1
1
2
25
5
2
1
1
1
6
1
36
3
4
5
3
3
13
2
1
4
1
1
9
7
1
1
15
1
1
2
5
7
3
3
1
8
7
5
1
1
5
14
3
2
2
1
5
3
4
3
1
2
9
2
15
2
1
3
56
3
2
4
2
26
3
1
4
1
1
12
1
18
7
6
7
2
3
2
1
4
15
10
7
5
3
6
4
9
1
2
2
2
3
1
1
8
3
1
2
3
1
10
3
1
3
5
1
8
3
2
3
7
1
5
2
1
1
6
2
1
9
1
20
2
1
4
21
5
4
4
1
1
1
11
7
4
6
1
3
3
12
4
7
4
3
3
1
2
10
5
11
3
2
1
3
30
1
4
5
1
7
3
1
3
9
2
2
14
10
1
1
1
1
5
1
1
18
32
1
4
5
4
3
2
9
2
6
3
2
2
2
2
2
2
4
4
3
1
1
2
4
6
1
1
1
2
2
1
1
3
1
1
3
1
2
3
2
9
4
1
2
4
3
3
3
1
2
1
3
4
3
1
3
1
5
14
7
1
1
1
1
4
3
4
5
8
1
10
3
2
7
5
4
5
7
4
4
10
3
7
12
1
1
3
1
6
1
5
9
2
1
3
4
3
2
2
5
1
4
6
5
3
1
1
6
3
1
1
2
1
5
5
2
1
2
5
1
2
1
6
5
5
3
2
4
8
1
2
5
1
1
1
4
1
4
1
2
6
3
4
4
2
2
2
2
1
1
2
5
2
2
1
5
2
2
1
2
4
1
3
3
1
3
4
2
2
3
1
4
1
1
6
6
2
4
5
2
1
8
1
2
1
1
3
4
3
3
2
2
1
4
1
5
1
4
1
1
2
1
1
1
8
1
1
1
1
1
3
3
5
2
3
1
1
5
2
3
6
3
3
14
2
1
1
2
1
2
4
2
1
6
1
7
2
3
3
2
2
2
2
1
2
4
1
6
2
5
2
1
2
2
5
8
4
1
1
1
1
4
1
3
2
1
2
2
3
3
3
6
1
1
1
5
7
1
5
2
1
1
1
3
20
2
3
3
1
2
1
15
4
4
1
1
2
1
1
3
2
6
5
1
5
1
7
4
3
2
5
2
1
1
3
2
6
2
4
2
1
24
4
17
1
3
2
2
2
2
8
1
3
1
9
2
4
1
1
6
3
4
1
9
2
1
3
2
6
2
1
3
1
20
3
4
7
3
7
1
7
1
5
2
1
1
1
2
1
2
4
1
1
2
3
4
1
4
1
1
1
1
1
2
1
6
2
3
2
1
9
8
11
1
1
2
1
3
1
2
15
5
2
2
9
1
4
2
1
1
14
1
1
2
1
3
10
1
1
3
1
1
1
4
2
8
1
2
2
1
11
1
3
1
7
1
3
10
3
3
1
1
1
11
1
2
1
1
2
2
5
5
4
1
2
5
2
4
3
1
1
2
2
4
2
1
1
4
1
4
1
5
1
1
2
1
4
7
1
2
1
2
9
3
1
7
2
2
1
2
3
5
2
7
1
5
1
1
2
2
2
4
2
8
4
6
1
1
1
1
1
16
2
1
6
4
4
1
1
1
1
4
3
2
6
11
10
21
2
1
1
3
2
2
2
7
1
6
4
1
7
4
11
1
2
8
1
1
1
1
2
17
1
2
3
4
2
1
2
2
4
3
2
3
1
3
3
1
3
37
4
3
1
2
1
1
3
1
2
3
1
5
1
2
1
3
2
3
3
4
4
2
4
1
1
3
3
2
1
2
1
1
3
1
1
3
3
4
2
4
1
1
1
10
3
2
2
2
2
2
2
1
19
2
2
4
1
3
1
13
5
2
1
2
2
4
2
1
3
5
1
1
1
6
3
9
4
1
1
1
3
1
17
4
1
4
6
2
1
2
4
4
2
2
2
4
4
1
1
1
2
7
2
1
2
8
1
1
7
4
1
2
1
1
3
1
3
1
1
3
5
8
1
1
4
1
1
15
1
1
6
2
3
3
8
4
1
4
2
4
2
5
4
4
1
1
7
1
10
1
10
2
15
7
3
1
3
2
19
2
9
1
10
2
1
2
2
4
6
1
4
3
1
2
3
2
1
3
7
1
4
2
13
2
5
3
6
2
1
2
1
5
1


--
hemantsain.com

        [[alternative HTML version deleted]]

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Re: How to define proper breaks in RFM analysis

David Winsemius

> On Oct 12, 2017, at 1:17 AM, Hemant Sain <[hidden email]> wrote:
>
> Hello,
> I'm working on RFM analysis and i wanted to define my own breaks but my
> frequency distribution is not normally distributed so when I'm using
> quartile its not giving the optimal results.
> so I'm looking for a better approach where i can define breaks dynamically
> because after visualization i can do it easily but i want to apply this
> model so that it can automatically define the breaks according to data set.
> I'm attaching sample data for reference.
>
> Thanks
>
>                           *Freq*
> 5
> 15
> 1
> 8
> 2

Have you read the Posting Guide?
And don't skip:  https://stat.ethz.ch/mailman/listinfo/r-help

--
David.
> 2
>
snipped reams of useless "data".

>
> --
> hemantsain.com
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

David Winsemius
Alameda, CA, USA

'Any technology distinguishable from magic is insufficiently advanced.'   -Gehm's Corollary to Clarke's Third Law

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: How to define proper breaks in RFM analysis

PIKAL Petr
In reply to this post by hemantsain55
Hi

Your statement about attaching data is problematic. We cannot do much with it. Instead use output from dput(yourdata) to show us what exactly your data look like.

We also do not know how do you want to split your data. It would be nice if you can show also what should be the bins with respective data. Unless you provide this information you probably would not get any sensible answer.

Cheers
Petr


> -----Original Message-----
> From: R-help [mailto:[hidden email]] On Behalf Of Hemant Sain
> Sent: Thursday, October 12, 2017 10:18 AM
> To: r-help mailing list <[hidden email]>
> Subject: [R] How to define proper breaks in RFM analysis
>
> Hello,
> I'm working on RFM analysis and i wanted to define my own breaks but my
> frequency distribution is not normally distributed so when I'm using quartile its
> not giving the optimal results.
> so I'm looking for a better approach where i can define breaks dynamically
> because after visualization i can do it easily but i want to apply this model so
> that it can automatically define the breaks according to data set.
> I'm attaching sample data for reference.
>
> Thanks
>
>                            *Freq*
> 5
> 15
> 1
> 8
> 2
> 2
> 2
> 1
> 1
> 2
> 2
> 1
> 1
> 1
> 1
> 4
> 10
> 22
> 24
> 1
> 1
> 2
> 2
> 7
> 1
> 1
> 4
> 6
> 1
> 1
> 2
> 2
> 17
> 2
> 7
> 1
> 5
> 2
> 7
> 7
> 1
> 4
> 13
> 4
> 1
> 9
> 1
> 1
> 1
> 3
> 2
> 1
> 1
> 2
> 4
> 5
> 8
> 4
> 14
> 18
> 4
> 1
> 1
> 1
> 7
> 1
> 1
> 1
> 1
> 10
> 6
> 4
> 6
> 1
> 3
> 13
> 2
> 4
> 8
> 11
> 2
> 4
> 6
> 1
> 1
> 3
> 1
> 2
> 3
> 2
> 2
> 5
> 2
> 1
> 1
> 1
> 3
> 2
> 3
> 1
> 2
> 9
> 34
> 8
> 1
> 1
> 1
> 1
> 2
> 1
> 5
> 4
> 9
> 1
> 1
> 4
> 2
> 2
> 1
> 1
> 2
> 25
> 5
> 2
> 1
> 1
> 1
> 6
> 1
> 36
> 3
> 4
> 5
> 3
> 3
> 13
> 2
> 1
> 4
> 1
> 1
> 9
> 7
> 1
> 1
> 15
> 1
> 1
> 2
> 5
> 7
> 3
> 3
> 1
> 8
> 7
> 5
> 1
> 1
> 5
> 14
> 3
> 2
> 2
> 1
> 5
> 3
> 4
> 3
> 1
> 2
> 9
> 2
> 15
> 2
> 1
> 3
> 56
> 3
> 2
> 4
> 2
> 26
> 3
> 1
> 4
> 1
> 1
> 12
> 1
> 18
> 7
> 6
> 7
> 2
> 3
> 2
> 1
> 4
> 15
> 10
> 7
> 5
> 3
> 6
> 4
> 9
> 1
> 2
> 2
> 2
> 3
> 1
> 1
> 8
> 3
> 1
> 2
> 3
> 1
> 10
> 3
> 1
> 3
> 5
> 1
> 8
> 3
> 2
> 3
> 7
> 1
> 5
> 2
> 1
> 1
> 6
> 2
> 1
> 9
> 1
> 20
> 2
> 1
> 4
> 21
> 5
> 4
> 4
> 1
> 1
> 1
> 11
> 7
> 4
> 6
> 1
> 3
> 3
> 12
> 4
> 7
> 4
> 3
> 3
> 1
> 2
> 10
> 5
> 11
> 3
> 2
> 1
> 3
> 30
> 1
> 4
> 5
> 1
> 7
> 3
> 1
> 3
> 9
> 2
> 2
> 14
> 10
> 1
> 1
> 1
> 1
> 5
> 1
> 1
> 18
> 32
> 1
> 4
> 5
> 4
> 3
> 2
> 9
> 2
> 6
> 3
> 2
> 2
> 2
> 2
> 2
> 2
> 4
> 4
> 3
> 1
> 1
> 2
> 4
> 6
> 1
> 1
> 1
> 2
> 2
> 1
> 1
> 3
> 1
> 1
> 3
> 1
> 2
> 3
> 2
> 9
> 4
> 1
> 2
> 4
> 3
> 3
> 3
> 1
> 2
> 1
> 3
> 4
> 3
> 1
> 3
> 1
> 5
> 14
> 7
> 1
> 1
> 1
> 1
> 4
> 3
> 4
> 5
> 8
> 1
> 10
> 3
> 2
> 7
> 5
> 4
> 5
> 7
> 4
> 4
> 10
> 3
> 7
> 12
> 1
> 1
> 3
> 1
> 6
> 1
> 5
> 9
> 2
> 1
> 3
> 4
> 3
> 2
> 2
> 5
> 1
> 4
> 6
> 5
> 3
> 1
> 1
> 6
> 3
> 1
> 1
> 2
> 1
> 5
> 5
> 2
> 1
> 2
> 5
> 1
> 2
> 1
> 6
> 5
> 5
> 3
> 2
> 4
> 8
> 1
> 2
> 5
> 1
> 1
> 1
> 4
> 1
> 4
> 1
> 2
> 6
> 3
> 4
> 4
> 2
> 2
> 2
> 2
> 1
> 1
> 2
> 5
> 2
> 2
> 1
> 5
> 2
> 2
> 1
> 2
> 4
> 1
> 3
> 3
> 1
> 3
> 4
> 2
> 2
> 3
> 1
> 4
> 1
> 1
> 6
> 6
> 2
> 4
> 5
> 2
> 1
> 8
> 1
> 2
> 1
> 1
> 3
> 4
> 3
> 3
> 2
> 2
> 1
> 4
> 1
> 5
> 1
> 4
> 1
> 1
> 2
> 1
> 1
> 1
> 8
> 1
> 1
> 1
> 1
> 1
> 3
> 3
> 5
> 2
> 3
> 1
> 1
> 5
> 2
> 3
> 6
> 3
> 3
> 14
> 2
> 1
> 1
> 2
> 1
> 2
> 4
> 2
> 1
> 6
> 1
> 7
> 2
> 3
> 3
> 2
> 2
> 2
> 2
> 1
> 2
> 4
> 1
> 6
> 2
> 5
> 2
> 1
> 2
> 2
> 5
> 8
> 4
> 1
> 1
> 1
> 1
> 4
> 1
> 3
> 2
> 1
> 2
> 2
> 3
> 3
> 3
> 6
> 1
> 1
> 1
> 5
> 7
> 1
> 5
> 2
> 1
> 1
> 1
> 3
> 20
> 2
> 3
> 3
> 1
> 2
> 1
> 15
> 4
> 4
> 1
> 1
> 2
> 1
> 1
> 3
> 2
> 6
> 5
> 1
> 5
> 1
> 7
> 4
> 3
> 2
> 5
> 2
> 1
> 1
> 3
> 2
> 6
> 2
> 4
> 2
> 1
> 24
> 4
> 17
> 1
> 3
> 2
> 2
> 2
> 2
> 8
> 1
> 3
> 1
> 9
> 2
> 4
> 1
> 1
> 6
> 3
> 4
> 1
> 9
> 2
> 1
> 3
> 2
> 6
> 2
> 1
> 3
> 1
> 20
> 3
> 4
> 7
> 3
> 7
> 1
> 7
> 1
> 5
> 2
> 1
> 1
> 1
> 2
> 1
> 2
> 4
> 1
> 1
> 2
> 3
> 4
> 1
> 4
> 1
> 1
> 1
> 1
> 1
> 2
> 1
> 6
> 2
> 3
> 2
> 1
> 9
> 8
> 11
> 1
> 1
> 2
> 1
> 3
> 1
> 2
> 15
> 5
> 2
> 2
> 9
> 1
> 4
> 2
> 1
> 1
> 14
> 1
> 1
> 2
> 1
> 3
> 10
> 1
> 1
> 3
> 1
> 1
> 1
> 4
> 2
> 8
> 1
> 2
> 2
> 1
> 11
> 1
> 3
> 1
> 7
> 1
> 3
> 10
> 3
> 3
> 1
> 1
> 1
> 11
> 1
> 2
> 1
> 1
> 2
> 2
> 5
> 5
> 4
> 1
> 2
> 5
> 2
> 4
> 3
> 1
> 1
> 2
> 2
> 4
> 2
> 1
> 1
> 4
> 1
> 4
> 1
> 5
> 1
> 1
> 2
> 1
> 4
> 7
> 1
> 2
> 1
> 2
> 9
> 3
> 1
> 7
> 2
> 2
> 1
> 2
> 3
> 5
> 2
> 7
> 1
> 5
> 1
> 1
> 2
> 2
> 2
> 4
> 2
> 8
> 4
> 6
> 1
> 1
> 1
> 1
> 1
> 16
> 2
> 1
> 6
> 4
> 4
> 1
> 1
> 1
> 1
> 4
> 3
> 2
> 6
> 11
> 10
> 21
> 2
> 1
> 1
> 3
> 2
> 2
> 2
> 7
> 1
> 6
> 4
> 1
> 7
> 4
> 11
> 1
> 2
> 8
> 1
> 1
> 1
> 1
> 2
> 17
> 1
> 2
> 3
> 4
> 2
> 1
> 2
> 2
> 4
> 3
> 2
> 3
> 1
> 3
> 3
> 1
> 3
> 37
> 4
> 3
> 1
> 2
> 1
> 1
> 3
> 1
> 2
> 3
> 1
> 5
> 1
> 2
> 1
> 3
> 2
> 3
> 3
> 4
> 4
> 2
> 4
> 1
> 1
> 3
> 3
> 2
> 1
> 2
> 1
> 1
> 3
> 1
> 1
> 3
> 3
> 4
> 2
> 4
> 1
> 1
> 1
> 10
> 3
> 2
> 2
> 2
> 2
> 2
> 2
> 1
> 19
> 2
> 2
> 4
> 1
> 3
> 1
> 13
> 5
> 2
> 1
> 2
> 2
> 4
> 2
> 1
> 3
> 5
> 1
> 1
> 1
> 6
> 3
> 9
> 4
> 1
> 1
> 1
> 3
> 1
> 17
> 4
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>
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Re: How to define proper breaks in RFM analysis

hemantsain55
Hey,
i want to define 3 ideal breaks (bin) for each variable one of those
variables is attached in the previous email,
i don't want to consider quartile method because quartile is not working
ideally for that data set because data distribution is non normal.
so i want you to suggest another method so that i can define 3 breaks with
the ideal interval for Recency, frequency and monetary to calculate RFM
score.
i'm again attaching you some of the data set.
please look into it and help me with the R code.
Thanks



*Data*

user_id subtotal_amount created_at Recency Frequency Monetary
194849 6.99 8/22/2017 9 5 9.996
194978 14.78 8/28/2017 3 15 16.308
198614 18.44 7/31/2017 31 1 18.44
234569 34.99 8/20/2017 11 8 13.5075
252686 7.99 7/31/2017 31 2 7.99
291719 21.26 8/25/2017 6 2 15.67
291787 46.1 8/31/2017 0 2 32.57
292630 24.34 7/31/2017 31 1 24.34
295204 21.86 7/18/2017 44 1 21.86
295989 8.98 8/20/2017 11 2 14.095
298883 14.38 8/24/2017 7 2 11.185
308824 10.77 7/31/2017 31 1 10.77
308874 8.29 6/11/2017 81 1 8.29
309088 17.16 8/3/2017 28 1 17.16
309126 20.54 7/30/2017 32 1 20.54
309127 15.24 8/2/2017 29 4 13.3925
309159 10.78 8/28/2017 3 10 13.694
309170 8.66 8/29/2017 2 22 9.383636364
309190 7.19 8/31/2017 0 24 10.33791667
309218 8.49 6/22/2017 70 1 8.49
309250 18.27 7/30/2017 32 1 18.27
309358 8 8/31/2017 0 2 11.99
309418 43.21 8/13/2017 18 2 26.35
309421 6.49 8/26/2017 5 7 10.72428571
309440 20.37 6/24/2017 68 1 20.37
309468 11.37 6/10/2017 82 1 11.37
309538 9.08 7/30/2017 32 4 10.075
309548 7.06 8/30/2017 1 6 7.83
309564 9.57 6/10/2017 82 1 9.57
309616 7.37 6/27/2017 65 1 7.37
309751 8.87 8/5/2017 26 2 8.925
309788 11.21 8/4/2017 27 2 10.81
309842 10.68 8/31/2017 0 17 10.49647059
309938 17.77 8/20/2017 11 2 12.38
310017 8.06 8/31/2017 0 7 12.12
310125 8.47 8/4/2017 27 1 8.47
310126 23.66 8/5/2017 26 5 21.908
310294 12.57 8/13/2017 18 2 9.675
312589 18.34 8/29/2017 2 7 12.93
312591 11.96 8/16/2017 15 7 15.12571429
312593 8.98 7/2/2017 60 1 8.98
312595 19.37 8/18/2017 13 4 11.8025
312633 8.77 8/27/2017 4 13 7.446923077
312634 8.49 6/29/2017 63 4 8.49
312659 10.08 6/23/2017 69 1 10.08
313602 7.49 8/26/2017 5 9 8.704444444
313615 10.47 6/6/2017 86 1 10.47
313618 10.49 7/23/2017 39 1 10.49
313625 7.28 7/26/2017 36 1 7.28
313630 8.37 8/23/2017 8 3 23.95
313635 9.07 8/3/2017 28 2 8.025
313651 7.78 8/30/2017 1 1 7.78
313668 15.77 6/3/2017 89 1 15.77
313679 10.17 8/14/2017 17 2 10.17
313691 10.56 8/8/2017 23 4 12.03
313693 93.86 8/24/2017 7 5 38.108
313695 7.99 8/23/2017 8 8 7.615
313706 17.05 8/27/2017 4 4 16.355
313708 20.87 8/5/2017 26 14 12.20857143
313715 7.99 8/28/2017 3 18 10.63333333
313741 32.5 8/12/2017 19 4 17.245
313744 16.96 8/8/2017 23 1 16.96
313765 7.49 8/19/2017 12 1 7.49
313778 8.38 7/24/2017 38 1 8.38
313785 11.97 8/29/2017 2 7 10.25571429
313818 6.49 7/31/2017 31 1 6.49
313822 20.35 7/18/2017 44 1 20.35
313828 10.28 7/20/2017 42 1 10.28
313843 11.87 6/19/2017 73 1 11.87
313847 19.36 8/25/2017 6 10 9.525
313858 8.08 8/25/2017 6 6 9.076666667
313862 6 7/28/2017 34 4 11.3575
313866 11.16 8/31/2017 0 6 15.6
313868 9.27 8/26/2017 5 1 9.27
313879 10.08 7/3/2017 59 3 12.01
313889 7.97 8/18/2017 13 13 8.322307692
313890 19.86 8/6/2017 25 2 17.51
313891 17.94 7/26/2017 36 4 15.0475
313892 9.88 8/30/2017 1 8 10.39875
313899 9.27 8/31/2017 0 11 12.88909091
313904 19.94 8/9/2017 22 2 14.705
313905 19.12 8/12/2017 19 4 22.3525
313914 9.08 8/18/2017 13 6 13.64333333
313917 10.17 8/28/2017 3 1 10.17
313922 7.99 6/30/2017 62 1 7.99
313923 9.57 8/14/2017 17 3 10.07333333
313927 6.99 7/25/2017 37 1 6.99
313928 8.79 8/31/2017 0 2 7.78
313934 7.19 8/29/2017 2 3 11.01666667
313936 9.38 6/27/2017 65 2 9.83
313937 7.56 8/15/2017 16 2 9.86
313938 22.34 8/30/2017 1 5 18.678
313948 21.16 8/5/2017 26 2 19.81
313951 9.27 8/29/2017 2 1 9.27
313958 8.49 8/30/2017 1 1 8.49
313972 10.77 6/12/2017 80 1 10.77
313975 11.74 7/25/2017 37 3 13.19666667
313989 6.48 8/22/2017 9 2 14.415
313992 8.49 8/22/2017 9 3 8.323333333
313997 9.38 6/12/2017 80 1 9.38
314000 8.27 7/10/2017 52 2 8.27
314003 20.35 8/20/2017 11 9 9.475555556
314005 9.88 8/28/2017 3 34 10.44970588
314006 8.47 8/28/2017 3 8 24.32625
314017 6.88 8/3/2017 28 1 6.88
314018 17.24 7/18/2017 44 1 17.24
314020 21.36 8/29/2017 2 1 21.36
314022 10.28 8/5/2017 26 1 10.28
314023 21.64 7/4/2017 58 2 17.895
314035 12.77 7/10/2017 52 1 12.77
314037 21.74 8/12/2017 19 5 13.4
314048 10.47 8/25/2017 6 4 9.8975
314054 12.78 8/30/2017 1 9 13.40333333
314059 22.94 8/5/2017 26 1 22.94
314082 23.04 8/23/2017 8 1 23.04
314086 13.26 8/21/2017 10 4 12.39
314090 7.08 8/6/2017 25 2 8.08
314091 10.28 6/26/2017 66 2 10.28
314092 13.94 8/7/2017 24 1 13.94
314099 6.19 7/30/2017 32 1 6.19
314107 24.35 8/18/2017 13 2 21.155
314108 8.17 8/31/2017 0 25 9.0932
314111 10.58 8/28/2017 3 5 10.816
314114 7.23 8/16/2017 15 2 7.23
314120 27.24 7/22/2017 40 1 27.24
314121 14.37 8/7/2017 24 1 14.37
314122 17.66 6/21/2017 71 1 17.66
314127 21.16 8/28/2017 3 6 19.955
314134 24.62 6/30/2017 62 1 24.62
314140 27.72 8/25/2017 6 36 9.754166667
314143 14.48 8/17/2017 14 3 12.10666667
314145 21.56 7/14/2017 48 4 18.0125
314146 8.26 8/15/2017 16 5 9.788
314153 13.17 8/1/2017 30 3 15.4
314160 24.56 8/9/2017 22 3 12.84
314161 16.15 8/29/2017 2 13 18.17
314163 7.88 8/21/2017 10 2 8.175
314164 9.97 7/14/2017 48 1 9.97
314167 13.46 8/28/2017 3 4 10.96
314173 19.75 6/19/2017 73 1 19.75
314175 50.55 6/12/2017 80 1 50.55
314178 34.04 8/28/2017 3 9 18.92666667
314179 11.47 8/22/2017 9 7 15.49857143
314181 17.97 7/13/2017 49 1 17.97
314186 9.74 7/28/2017 34 1 9.74
314189 6.97 8/29/2017 2 15 9.236666667
314190 10.06 8/6/2017 25 1 10.06
314192 26.76 7/31/2017 31 1 26.76
314198 8.07 8/21/2017 10 2 7.78
314202 21.82 8/12/2017 19 5 16.184
314207 9.67 8/29/2017 2 7 11.39571429
314208 9.27 8/28/2017 3 3 9.27
314214 9.36 8/6/2017 25 3 12.54
314221 10.67 6/30/2017 62 1 10.67
314222 18.39 8/24/2017 7 8 16.1175
314223 62.42 8/24/2017 7 7 33.48285714
314226 16.71 8/16/2017 15 5 12.082
314229 18.56 8/26/2017 5 1 18.56
314231 32.21 7/9/2017 53 1 32.21
314238 16.86 8/13/2017 18 5 13.928
314239 13.66 8/25/2017 6 14 9.75
314246 22.72 8/28/2017 3 3 17.17666667
314255 8.18 8/30/2017 1 2 7.485
314256 10 7/3/2017 59 2 11.68
314258 9.47 8/11/2017 20 1 9.47
314260 18.66 8/4/2017 27 5 16.464
314263 14.16 7/25/2017 37 3 22.13333333
314274 32.82 8/6/2017 25 4 19.73
314276 13.26 8/4/2017 27 3 12.43
314283 20.25 6/16/2017 76 1 20.25
314288 8.07 7/9/2017 53 2 8.67
314289 20.14 8/30/2017 1 9 16.61555556
314296 7.99 6/30/2017 62 2 7.99
314298 7.49 8/28/2017 3 15 8.435333333
314299 30.15 7/11/2017 51 2 21.4
314301 8.69 7/19/2017 43 1 8.69
314306 13.07 7/23/2017 39 3 13.64
314314 7.74 8/31/2017 0 56 7.876071429
314315 18.94 8/17/2017 14 3 16.41333333
314325 6.79 7/29/2017 33 2 7.39
314331 7.57 8/17/2017 14 4 11.9975
314338 10.07 8/24/2017 7 2 10.07
314340 8.07 8/31/2017 0 26 11.98923077
314343 19.34 8/17/2017 14 3 19.74
314344 26.07 8/7/2017 24 1 26.07
314348 19.44 7/31/2017 31 4 16.9
314353 27.14 6/19/2017 73 1 27.14
314355 13.98 7/24/2017 38 1 13.98
314356 9.98 8/29/2017 2 12 10.505
314359 15.54 8/15/2017 16 1 15.54
314371 6.97 8/27/2017 4 18 9.247222222
314375 10.48 7/12/2017 50 7 9.217142857
314376 8.58 7/4/2017 58 6 7.795
314377 9.77 8/15/2017 16 7 13.2
314384 13.66 8/4/2017 27 2 17.995
314387 17.15 7/23/2017 39 3 16.84666667
314389 11.77 8/25/2017 6 2 11.77
314390 19.74 8/23/2017 8 1 19.74
314395 9.67 8/24/2017 7 4 9.1375
314396 7.18 8/25/2017 6 15 7.585333333
314398 12.02 8/22/2017 9 10 11.365
314401 16.54 8/31/2017 0 7 19.61571429
314408 16.27 8/25/2017 6 5 10.136
314410 12.17 7/27/2017 35 3 11.84
314413 8.28 8/29/2017 2 6 7.73
314416 20.65 8/14/2017 17 4 12.075
314420 11.47 8/26/2017 5 9 9.922222222
314424 39.88 6/14/2017 78 1 39.88
314425 8.98 8/3/2017 28 2 8.98
314431 9.87 7/23/2017 39 2 9.12
314434 25.57 8/25/2017 6 2 17.545
314439 7.39 8/29/2017 2 3 7.39
314445 7.67 8/4/2017 27 1 7.67
314446 18.14 8/12/2017 19 1 18.14
314460 7.97 8/31/2017 0 8 11.92875
314466 6.06 8/22/2017 9 3 10.51
314472 20.26 8/30/2017 1 1 20.26
314473 16.95 8/9/2017 22 2 15.025
314474 22.53 8/5/2017 26 3 20.16666667
314475 11.97 6/11/2017 81 1 11.97
314484 8.8 8/27/2017 4 10 9.492
314486 7.19 7/17/2017 45 3 7.186666667
314504 28.33 6/10/2017 82 1 28.33
314509 6.08 7/17/2017 45 3 6.846666667
314512 12.45 8/12/2017 19 5 13.516
314519 14.08 7/31/2017 31 1 14.08
314527 8.08 8/21/2017 10 8 8.51625
314531 8.27 8/31/2017 0 3 9.096666667
314532 6.38 7/10/2017 52 2 7.23
314535 29.81 7/8/2017 54 3 17.15333333
314538 8.27 8/14/2017 17 7 8.647142857
314541 9.27 8/28/2017 3 1 9.27
314544 18.16 7/30/2017 32 5 13.646
314549 8.27 8/24/2017 7 2 11.62
314556 8.07 6/15/2017 77 1 8.07
314566 7.99 8/11/2017 20 1 7.99
314571 10.27 8/29/2017 2 6 10.28666667
314581 49.94 7/25/2017 37 2 41.975
314587 7.97 8/15/2017 16 1 7.97
314595 11.18 8/23/2017 8 9 11.93333333
314597 11.95 7/4/2017 58 1 11.95
314598 10.08 8/28/2017 3 20 10.2225
314600 8.98 8/24/2017 7 2 8.03
314601 24.34 7/16/2017 46 1 24.34
314616 10.08 8/18/2017 13 4 14.52
314619 17.66 8/27/2017 4 21 15.1752381
314623 10.17 8/10/2017 21 5 11.036
314628 18.76 7/19/2017 43 4 14.9125
314632 6.68 8/25/2017 6 4 7.935
314639 17.44 7/12/2017 50 1 17.44
314640 9.67 8/4/2017 27 1 9.67
314646 29.3 6/24/2017 68 1 29.3
314650 9.47 8/31/2017 0 11 11.36727273
314670 8.49 8/30/2017 1 7 8.49
314672 7.18 7/10/2017 52 4 7.585
314678 8.17 8/30/2017 1 6 11.43666667
314688 9.47 8/1/2017 30 1 9.47
314689 29.42 8/6/2017 25 3 28.91666667
314708 20.83 8/30/2017 1 3 12.76
314717 15.36 8/31/2017 0 12 10.28833333
314721 17.26 7/24/2017 38 4 11.6425
314723 6.79 8/26/2017 5 7 8.287142857
314726 8.37 8/18/2017 13 4 9.0675
314727 10.27 8/29/2017 2 3 10.33666667
314728 10.48 8/27/2017 4 3 9.91
314731 10.67 8/31/2017 0 1 10.67
314733 7.18 6/13/2017 79 2 7.68
314738 9.06 8/12/2017 19 10 13.196
314744 18.06 8/31/2017 0 5 19.202
314745 7.78 8/29/2017 2 11 9.722727273
314747 9.76 8/28/2017 3 3 9.693333333
314756 14.27 8/20/2017 11 2 11.625
314762 8.47 8/24/2017 7 1 8.47
314763 9.67 8/4/2017 27 3 9.206666667
314767 11.95 8/29/2017 2 30 11.36366667
314775 8.67 8/22/2017 9 1 8.67
314776 13.47 8/15/2017 16 4 10.7325
314782 8.48 8/27/2017 4 5 9.754
314783 8.57 8/18/2017 13 1 8.57
314785 7.63 8/31/2017 0 7 7.832857143
314787 23.72 8/30/2017 1 3 13.33
314793 6.99 6/10/2017 82 1 6.99
314797 10.78 8/23/2017 8 3 10.78
314803 7.28 8/28/2017 3 9 9.412222222
314807 7.32 7/18/2017 44 2 7.32
314811 11.67 8/31/2017 0 2 9.83
314814 8.27 8/31/2017 0 14 7.998571429
314828 9.85 8/19/2017 12 10 16.641
314829 22.96 7/6/2017 56 1 22.96
314832 9.38 6/8/2017 84 1 9.38
314843 8.28 6/5/2017 87 1 8.28
314863 16.14 6/14/2017 78 1 16.14
314868 7.37 8/21/2017 10 5 14.546
314871 6.98 8/28/2017 3 1 6.98
314882 13.38 7/30/2017 32 1 13.38
314883 7.77 8/25/2017 6 18 8.441666667
314898 9.67 8/31/2017 0 32 7.9753125
314900 6.47 8/15/2017 16 1 6.47
314902 7.44 8/19/2017 12 4 12.2425
314904 16.56 8/16/2017 15 5 15.222
314909 16.27 8/19/2017 12 4 14.9175
314912 7.77 8/1/2017 30 3 8.71
314915 8.16 7/11/2017 51 2 10.18
314933 11.67 8/21/2017 10 9 11.67
314940 9.06 8/8/2017 23 2 12.9
314957 8.57 8/31/2017 0 6 12.78833333
314972 11.47 6/29/2017 63 3 11.14
314975 9.66 8/9/2017 22 2 9.615
314985 9.38 7/7/2017 55 2 8.54
314996 13.54 7/13/2017 49 2 12.295
315002 11.43 7/8/2017 54 2 16.525
315032 7.19 6/23/2017 69 2 8.09
315048 17.98 8/31/2017 0 2 17.98
315051 6.79 7/7/2017 55 4 6.8125
315054 11.97 8/22/2017 9 4 10.025
315056 8.78 6/27/2017 65 3 8.766666667
315059 25.14 8/9/2017 22 1 25.14
315061 30.44 6/24/2017 68 1 30.44
315063 9.67 8/30/2017 1 2 9.72
315070 6.67 8/15/2017 16 4 8.94
315072 16.96 8/15/2017 16 6 17.21833333
315073 16.66 6/19/2017 73 1 16.66
315082 7.67 8/7/2017 24 1 7.67
315083 30.89 6/8/2017 84 1 30.89
315089 9.37 7/19/2017 43 2 9.67
315097 8.44 7/18/2017 44 2 12.13
315098 11.37 6/30/2017 62 1 11.37
315110 9.78 8/16/2017 15 1 9.78
315111 40.17 8/11/2017 20 3 20.54
315116 11.68 7/19/2017 43 1 11.68
315122 8.27 6/30/2017 62 1 8.27
315126 9.59 7/2/2017 60 3 10.34
315128 17.83 8/21/2017 10 1 17.83
315132 7.99 7/25/2017 37 2 12.665
315147 8 8/26/2017 5 3 10.71333333
315155 10 7/3/2017 59 2 9.785
315156 8.16 8/23/2017 8 9 9.218888889
315160 16.77 8/27/2017 4 4 12.85
315161 11.28 8/1/2017 30 1 11.28
315166 7.98 8/28/2017 3 2 10.175
315177 14.05 8/15/2017 16 4 10.45
315184 5.99 6/27/2017 65 3 7.413333333
315187 9.52 8/3/2017 28 3 10.01333333
315191 7.98 8/18/2017 13 3 12.70666667
315195 18.85 7/29/2017 33 1 18.85
315198 10.98 7/27/2017 35 2 18.06
315203 6.99 7/7/2017 55 1 6.99
315204 16.26 8/25/2017 6 3 13.83333333
315205 31.63 8/22/2017 9 4 25.605
315230 20.55 8/12/2017 19 3 21.18666667
315233 20.95 8/5/2017 26 1 20.95
315235 8.47 8/6/2017 25 3 7.71
315242 11.16 6/9/2017 83 1 11.16
315246 8.98 8/30/2017 1 5 8.86
315252 8.99 8/20/2017 11 14 9.035
315262 11.87 8/29/2017 2 7 23.83
315264 13.75 6/3/2017 89 1 13.75
315266 10.59 6/11/2017 81 1 10.59
315270 11.98 8/26/2017 5 1 11.98
315273 15.16 8/24/2017 7 1 15.16
315278 9.28 8/31/2017 0 4 11.775
315287 27.03 8/24/2017 7 3 15.45333333
315293 8.34 8/31/2017 0 4 8.1175
315294 8.47 8/24/2017 7 5 9.28
315295 24.54 8/26/2017 5 8 18.445
315296 8.47 6/1/2017 91 1 8.47
315323 21.94 8/27/2017 4 10 14.309
315329 12.37 7/31/2017 31 3 12.87
315333 6.88 6/18/2017 74 2 6.935
315337 9.28 8/28/2017 3 7 8.272857143
315347 6.78 8/10/2017 21 5 7.678
315348 5.99 8/11/2017 20 4 13.7975
315355 15.74 8/15/2017 16 5 16.822
315364 6.89 8/26/2017 5 7 11.82428571
315372 20.92 8/3/2017 28 4 15.1725
315375 7.55 8/6/2017 25 4 11.4875
315377 11.37 8/25/2017 6 10 10.366
315384 9.47 8/30/2017 1 3 7.546666667
315385 6.47 8/8/2017 23 7 6.727142857
315388 7.89 8/31/2017 0 12 11.265
315391 12 8/21/2017 10 1 12
315396 7.36 6/28/2017 64 1 7.36
315398 12.37 8/27/2017 4 3 10.07666667
315400 17.34 8/25/2017 6 1 17.34
315401 8.98 8/12/2017 19 6 9.126666667
315415 12.36 6/30/2017 62 1 12.36
315417 10.58 8/28/2017 3 5 9.052
315424 8.27 8/23/2017 8 9 10.50222222
315427 9.47 8/5/2017 26 2 10.57
315437 11.87 7/13/2017 49 1 11.87
315440 10.56 7/31/2017 31 3 11.42
315446 6.17 8/3/2017 28 4 17.525
315447 9.08 8/10/2017 21 3 9.806666667
315448 7.99 7/29/2017 33 2 9.28
315449 18.94 8/30/2017 1 2 12.865
315453 13.26 8/21/2017 10 5 8.512
315461 7.18 7/26/2017 36 1 7.18
315466 19.75 8/30/2017 1 4 22.1525
315468 6.99 7/29/2017 33 6 10.36166667
315473 12.94 8/29/2017 2 5 13.476
315474 8.37 8/17/2017 14 3 9.466666667
315477 6.49 8/31/2017 0 1 6.49
315480 18.94 6/25/2017 67 1 18.94
315483 12.07 8/6/2017 25 6 12.48833333
315489 8.17 8/8/2017 23 3 13.06
315492 6.67 8/8/2017 23 1 6.67
315497 9.65 8/21/2017 10 1 9.65
315498 12.36 8/5/2017 26 2 10.265
315499 13.17 7/30/2017 32 1 13.17
315503 8.71 6/29/2017 63 5 12.854
315511 9.67 8/15/2017 16 5 9.992
315513 9.58 8/24/2017 7 2 9.125
315522 8.47 7/12/2017 50 1 8.47
315523 10.47 8/1/2017 30 2 8.63
315532 8.47 8/16/2017 15 5 11.362
315533 10.29 6/7/2017 85 1 10.29
315538 6.39 7/8/2017 54 2 16.51
315542 18.66 7/6/2017 56 1 18.66
315549 21.54 8/22/2017 9 6 20.71333333
315550 59.33 8/1/2017 30 5 19.566
315551 17.56 8/24/2017 7 5 12.908
315552 10.75 7/22/2017 40 3 8.796666667
315556 6.06 7/26/2017 36 2 7.66
315559 14.98 8/15/2017 16 4 21.93
315562 13.15 8/6/2017 25 8 9.9725
315563 9.47 8/30/2017 1 1 9.47
315567 18.77 8/28/2017 3 2 25.955
315575 10.86 8/22/2017 9 5 10.626
315579 7.38 7/31/2017 31 1 7.38
315581 8.78 8/17/2017 14 1 8.78
315582 6.99 8/19/2017 12 1 6.99
315591 22.86 8/11/2017 20 4 22.4925
315599 7.77 8/9/2017 22 1 7.77
315602 6.18 8/20/2017 11 4 6.18
315608 12.36 8/21/2017 10 1 12.36
315609 8.98 7/10/2017 52 2 11.21
315610 7.99 8/25/2017 6 6 14.73833333
315611 8.49 8/31/2017 0 3 8.323333333
315618 0 7/25/2017 37 4 17.85
315629 8.67 8/6/2017 25 4 8.17
315632 14.66 8/15/2017 16 2 10.475
315634 8.47 7/25/2017 37 2 8.82
315638 13.25 7/25/2017 37 2 13.055
315642 17.47 7/22/2017 40 2 12.13
315645 6.99 7/6/2017 56 1 6.99
315649 22.03 8/6/2017 25 1 22.03
315650 8.43 8/25/2017 6 2 9.15
315651 12.94 8/15/2017 16 5 14.666
315654 7.49 8/8/2017 23 2 9.98
315655 13.95 7/28/2017 34 2 11.21
315660 8.27 7/27/2017 35 1 8.27
315663 6.99 8/29/2017 2 5 6.664
315665 9.48 6/30/2017 62 2 8.885
315670 10.07 8/17/2017 14 2 8.47
315672 10.78 8/5/2017 26 1 10.78
315673 12.48 8/3/2017 28 2 17.265
315680 14.26 8/21/2017 10 4 14.13
315684 8.07 6/2/2017 90 1 8.07
315685 11.97 7/20/2017 42 3 10.64666667
315688 11.9 8/27/2017 4 3 10.49
315689 39.9 7/2/2017 60 1 39.9
315697 30.23 8/4/2017 27 3 18.56
315700 11.05 8/6/2017 25 4 10.335
315702 12.06 8/4/2017 27 2 10.765
315703 8.47 8/20/2017 11 2 9.915
315705 8.07 8/14/2017 17 3 8.043333333
315707 23.34 7/29/2017 33 1 23.34
315711 10.57 7/6/2017 56 4 11.3325
315712 22.36 8/7/2017 24 1 22.36
315717 8.88 7/22/2017 40 1 8.88
315723 10.47 8/21/2017 10 6 10.95166667
315725 6.79 8/22/2017 9 6 7.338333333
315726 10.97 6/23/2017 69 2 9.48
315730 12.01 8/30/2017 1 4 11.875
315731 28.73 8/15/2017 16 5 14.042
315740 7.28 8/9/2017 22 2 7.28
315754 8.18 6/11/2017 81 1 8.18
315755 9.24 8/27/2017 4 8 8.22375
315760 22 7/3/2017 59 1 22
315768 18.76 8/4/2017 27 2 19.405
315779 21.55 6/10/2017 82 1 21.55
315785 6.79 6/5/2017 87 1 6.79
315788 10.58 8/15/2017 16 3 10.05333333
315793 6.79 7/25/2017 37 4 9.23
315799 12.59 8/23/2017 8 3 10.35333333
315802 11.86 8/31/2017 0 3 17.02
315809 8.76 8/1/2017 30 2 8.76
315817 11.26 7/30/2017 32 2 9.765
315818 9.67 6/20/2017 72 1 9.67
315826 8.48 8/6/2017 25 4 8.8525
315845 11.07 8/5/2017 26 1 11.07
315853 8.47 7/29/2017 33 5 16.268
315854 27.93 7/9/2017 53 1 27.93
315855 12.76 7/5/2017 57 4 10.57
315856 10.78 7/28/2017 34 1 10.78
315860 17.46 8/24/2017 7 1 17.46
315861 8.49 8/8/2017 23 2 7.39
315873 32.84 7/30/2017 32 1 32.84
315875 20.75 6/12/2017 80 1 20.75
315883 19.64 6/13/2017 79 1 19.64

On 13 October 2017 at 10:35, PIKAL Petr <[hidden email]> wrote:

> Hi
>
> Your statement about attaching data is problematic. We cannot do much with
> it. Instead use output from dput(yourdata) to show us what exactly your
> data look like.
>
> We also do not know how do you want to split your data. It would be nice
> if you can show also what should be the bins with respective data. Unless
> you provide this information you probably would not get any sensible answer.
>
> Cheers
> Petr
>
>
> > -----Original Message-----
> > From: R-help [mailto:[hidden email]] On Behalf Of Hemant
> Sain
> > Sent: Thursday, October 12, 2017 10:18 AM
> > To: r-help mailing list <[hidden email]>
> > Subject: [R] How to define proper breaks in RFM analysis
> >
> > Hello,
> > I'm working on RFM analysis and i wanted to define my own breaks but my
> > frequency distribution is not normally distributed so when I'm using
> quartile its
> > not giving the optimal results.
> > so I'm looking for a better approach where i can define breaks
> dynamically
> > because after visualization i can do it easily but i want to apply this
> model so
> > that it can automatically define the breaks according to data set.
> > I'm attaching sample data for reference.
> >
> > Thanks
> >
> >                            *Freq*
> > 5
> > 15
> > 1
> > 8
> > 2
> > 2
> > 2
> > 1
> > 1
> > 2
> > 2
> > 1
> > 1
> > 1
> > 1
> > 4
> > 10
> > 22
> > 24
> > 1
> > 1
> > 2
> > 2
> > 7
> > 1
> > 1
> > 4
> > 6
> > 1
> > 1
> > 2
> > 2
> > 17
> > 2
> > 7
> > 1
> > 5
> > 2
> > 7
> > 7
> > 1
> > 4
> > 13
> > 4
> > 1
> > 9
> > 1
> > 1
> > 1
> > 3
> > 2
> > 1
> > 1
> > 2
> > 4
> > 5
> > 8
> > 4
> > 14
> > 18
> > 4
> > 1
> > 1
> > 1
> > 7
> > 1
> > 1
> > 1
> > 1
> > 10
> > 6
> > 4
> > 6
> > 1
> > 3
> > 13
> > 2
> > 4
> > 8
> > 11
> > 2
> > 4
> > 6
> > 1
> > 1
> > 3
> > 1
> > 2
> > 3
> > 2
> > 2
> > 5
> > 2
> > 1
> > 1
> > 1
> > 3
> > 2
> > 3
> > 1
> > 2
> > 9
> > 34
> > 8
> > 1
> > 1
> > 1
> > 1
> > 2
> > 1
> > 5
> > 4
> > 9
> > 1
> > 1
> > 4
> > 2
> > 2
> > 1
> > 1
> > 2
> > 25
> > 5
> > 2
> > 1
> > 1
> > 1
> > 6
> > 1
> > 36
> > 3
> > 4
> > 5
> > 3
> > 3
> > 13
> > 2
> > 1
> > 4
> > 1
> > 1
> > 9
> > 7
> > 1
> > 1
> > 15
> > 1
> > 1
> > 2
> > 5
> > 7
> > 3
> > 3
> > 1
> > 8
> > 7
> > 5
> > 1
> > 1
> > 5
> > 14
> > 3
> > 2
> > 2
> > 1
> > 5
> > 3
> > 4
> > 3
> > 1
> > 2
> > 9
> > 2
> > 15
> > 2
> > 1
> > 3
> > 56
> > 3
> > 2
> > 4
> > 2
> > 26
> > 3
> > 1
> > 4
> > 1
> > 1
> > 12
> > 1
> > 18
> > 7
> > 6
> > 7
> > 2
> > 3
> > 2
> > 1
> > 4
> > 15
> > 10
> > 7
> > 5
> > 3
> > 6
> > 4
> > 9
> > 1
> > 2
> > 2
> > 2
> > 3
> > 1
> > 1
> > 8
> > 3
> > 1
> > 2
> > 3
> > 1
> > 10
> > 3
> > 1
> > 3
> > 5
> > 1
> > 8
> > 3
> > 2
> > 3
> > 7
> > 1
> > 5
> > 2
> > 1
> > 1
> > 6
> > 2
> > 1
> > 9
> > 1
> > 20
> > 2
> > 1
> > 4
> > 21
> > 5
> > 4
> > 4
> > 1
> > 1
> > 1
> > 11
> > 7
> > 4
> > 6
> > 1
> > 3
> > 3
> > 12
> > 4
> > 7
> > 4
> > 3
> > 3
> > 1
> > 2
> > 10
> > 5
> > 11
> > 3
> > 2
> > 1
> > 3
> > 30
> > 1
> > 4
> > 5
> > 1
> > 7
> > 3
> > 1
> > 3
> > 9
> > 2
> > 2
> > 14
> > 10
> > 1
> > 1
> > 1
> > 1
> > 5
> > 1
> > 1
> > 18
> > 32
> > 1
> > 4
> > 5
> > 4
> > 3
> > 2
> > 9
> > 2
> > 6
> > 3
> > 2
> > 2
> > 2
> > 2
> > 2
> > 2
> > 4
> > 4
> > 3
> > 1
> > 1
> > 2
> > 4
> > 6
> > 1
> > 1
> > 1
> > 2
> > 2
> > 1
> > 1
> > 3
> > 1
> > 1
> > 3
> > 1
> > 2
> > 3
> > 2
> > 9
> > 4
> > 1
> > 2
> > 4
> > 3
> > 3
> > 3
> > 1
> > 2
> > 1
> > 3
> > 4
> > 3
> > 1
> > 3
> > 1
> > 5
> > 14
> > 7
> > 1
> > 1
> > 1
> > 1
> > 4
> > 3
> > 4
> > 5
> > 8
> > 1
> > 10
> > 3
> > 2
> > 7
> > 5
> > 4
> > 5
> > 7
> > 4
> > 4
> > 10
> > 3
> > 7
> > 12
> > 1
> > 1
> > 3
> > 1
> > 6
> > 1
> > 5
> > 9
> > 2
> > 1
> > 3
> > 4
> > 3
> > 2
> > 2
> > 5
> > 1
> > 4
> > 6
> > 5
> > 3
> > 1
> > 1
> > 6
> > 3
> > 1
> > 1
> > 2
> > 1
> > 5
> > 5
> > 2
> > 1
> > 2
> > 5
> > 1
> > 2
> > 1
> > 6
> > 5
> > 5
> > 3
> > 2
> > 4
> > 8
> > 1
> > 2
> > 5
> > 1
> > 1
> > 1
> > 4
> > 1
> > 4
> > 1
> > 2
> > 6
> > 3
> > 4
> > 4
> > 2
> > 2
> > 2
> > 2
> > 1
> > 1
> > 2
> > 5
> > 2
> > 2
> > 1
> > 5
> > 2
> > 2
> > 1
> > 2
> > 4
> > 1
> > 3
> > 3
> > 1
> > 3
> > 4
> > 2
> > 2
> > 3
> > 1
> > 4
> > 1
> > 1
> > 6
> > 6
> > 2
> > 4
> > 5
> > 2
> > 1
> > 8
> > 1
> > 2
> > 1
> > 1
> > 3
> > 4
> > 3
> > 3
> > 2
> > 2
> > 1
> > 4
> > 1
> > 5
> > 1
> > 4
> > 1
> > 1
> > 2
> > 1
> > 1
> > 1
> > 8
> > 1
> > 1
> > 1
> > 1
> > 1
> > 3
> > 3
> > 5
> > 2
> > 3
> > 1
> > 1
> > 5
> > 2
> > 3
> > 6
> > 3
> > 3
> > 14
> > 2
> > 1
> > 1
> > 2
> > 1
> > 2
> > 4
> > 2
> > 1
> > 6
> > 1
> > 7
> > 2
> > 3
> > 3
> > 2
> > 2
> > 2
> > 2
> > 1
> > 2
> > 4
> > 1
> > 6
> > 2
> > 5
> > 2
> > 1
> > 2
> > 2
> > 5
> > 8
> > 4
> > 1
> > 1
> > 1
> > 1
> > 4
> > 1
> > 3
> > 2
> > 1
> > 2
> > 2
> > 3
> > 3
> > 3
> > 6
> > 1
> > 1
> > 1
> > 5
> > 7
> > 1
> > 5
> > 2
> > 1
> > 1
> > 1
> > 3
> > 20
> > 2
> > 3
> > 3
> > 1
> > 2
> > 1
> > 15
> > 4
> > 4
> > 1
> > 1
> > 2
> > 1
> > 1
> > 3
> > 2
> > 6
> > 5
> > 1
> > 5
> > 1
> > 7
> > 4
> > 3
> > 2
> > 5
> > 2
> > 1
> > 1
> > 3
> > 2
> > 6
> > 2
> > 4
> > 2
> > 1
> > 24
> > 4
> > 17
> > 1
> > 3
> > 2
> > 2
> > 2
> > 2
> > 8
> > 1
> > 3
> > 1
> > 9
> > 2
> > 4
> > 1
> > 1
> > 6
> > 3
> > 4
> > 1
> > 9
> > 2
> > 1
> > 3
> > 2
> > 6
> > 2
> > 1
> > 3
> > 1
> > 20
> > 3
> > 4
> > 7
> > 3
> > 7
> > 1
> > 7
> > 1
> > 5
> > 2
> > 1
> > 1
> > 1
> > 2
> > 1
> > 2
> > 4
> > 1
> > 1
> > 2
> > 3
> > 4
> > 1
> > 4
> > 1
> > 1
> > 1
> > 1
> > 1
> > 2
> > 1
> > 6
> > 2
> > 3
> > 2
> > 1
> > 9
> > 8
> > 11
> > 1
> > 1
> > 2
> > 1
> > 3
> > 1
> > 2
> > 15
> > 5
> > 2
> > 2
> > 9
> > 1
> > 4
> > 2
> > 1
> > 1
> > 14
> > 1
> > 1
> > 2
> > 1
> > 3
> > 10
> > 1
> > 1
> > 3
> > 1
> > 1
> > 1
> > 4
> > 2
> > 8
> > 1
> > 2
> > 2
> > 1
> > 11
> > 1
> > 3
> > 1
> > 7
> > 1
> > 3
> > 10
> > 3
> > 3
> > 1
> > 1
> > 1
> > 11
> > 1
> > 2
> > 1
> > 1
> > 2
> > 2
> > 5
> > 5
> > 4
> > 1
> > 2
> > 5
> > 2
> > 4
> > 3
> > 1
> > 1
> > 2
> > 2
> > 4
> > 2
> > 1
> > 1
> > 4
> > 1
> > 4
> > 1
> > 5
> > 1
> > 1
> > 2
> > 1
> > 4
> > 7
> > 1
> > 2
> > 1
> > 2
> > 9
> > 3
> > 1
> > 7
> > 2
> > 2
> > 1
> > 2
> > 3
> > 5
> > 2
> > 7
> > 1
> > 5
> > 1
> > 1
> > 2
> > 2
> > 2
> > 4
> > 2
> > 8
> > 4
> > 6
> > 1
> > 1
> > 1
> > 1
> > 1
> > 16
> > 2
> > 1
> > 6
> > 4
> > 4
> > 1
> > 1
> > 1
> > 1
> > 4
> > 3
> > 2
> > 6
> > 11
> > 10
> > 21
> > 2
> > 1
> > 1
> > 3
> > 2
> > 2
> > 2
> > 7
> > 1
> > 6
> > 4
> > 1
> > 7
> > 4
> > 11
> > 1
> > 2
> > 8
> > 1
> > 1
> > 1
> > 1
> > 2
> > 17
> > 1
> > 2
> > 3
> > 4
> > 2
> > 1
> > 2
> > 2
> > 4
> > 3
> > 2
> > 3
> > 1
> > 3
> > 3
> > 1
> > 3
> > 37
> > 4
> > 3
> > 1
> > 2
> > 1
> > 1
> > 3
> > 1
> > 2
> > 3
> > 1
> > 5
> > 1
> > 2
> > 1
> > 3
> > 2
> > 3
> > 3
> > 4
> > 4
> > 2
> > 4
> > 1
> > 1
> > 3
> > 3
> > 2
> > 1
> > 2
> > 1
> > 1
> > 3
> > 1
> > 1
> > 3
> > 3
> > 4
> > 2
> > 4
> > 1
> > 1
> > 1
> > 10
> > 3
> > 2
> > 2
> > 2
> > 2
> > 2
> > 2
> > 1
> > 19
> > 2
> > 2
> > 4
> > 1
> > 3
> > 1
> > 13
> > 5
> > 2
> > 1
> > 2
> > 2
> > 4
> > 2
> > 1
> > 3
> > 5
> > 1
> > 1
> > 1
> > 6
> > 3
> > 9
> > 4
> > 1
> > 1
> > 1
> > 3
> > 1
> > 17
> > 4
> > 1
> > 4
> > 6
> > 2
> > 1
> > 2
> > 4
> > 4
> > 2
> > 2
> > 2
> > 4
> > 4
> > 1
> > 1
> > 1
> > 2
> > 7
> > 2
> > 1
> > 2
> > 8
> > 1
> > 1
> > 7
> > 4
> > 1
> > 2
> > 1
> > 1
> > 3
> > 1
> > 3
> > 1
> > 1
> > 3
> > 5
> > 8
> > 1
> > 1
> > 4
> > 1
> > 1
> > 15
> > 1
> > 1
> > 6
> > 2
> > 3
> > 3
> > 8
> > 4
> > 1
> > 4
> > 2
> > 4
> > 2
> > 5
> > 4
> > 4
> > 1
> > 1
> > 7
> > 1
> > 10
> > 1
> > 10
> > 2
> > 15
> > 7
> > 3
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Re: How to define proper breaks in RFM analysis

PIKAL Petr
Hi

You expect us to solve your problem but you ignore advice already recieved.

Your data are unreadable, use dput(yourdata) instead. see ?dput

> test<-read.table("clipboard", heade=T)
Error in scan(file = file, what = what, sep = sep, quote = quote, dec = dec,  :
  line 115 did not have 6 elements

What is „ideal interval“ can you define it? Should it be such to provide eqal number of observations?

Or maybe you could normalise your values and use quartile method.

Cheers
Petr

From: Hemant Sain [mailto:[hidden email]]
Sent: Friday, October 13, 2017 8:51 AM
To: PIKAL Petr <[hidden email]>
Cc: r-help mailing list <[hidden email]>
Subject: Re: [R] How to define proper breaks in RFM analysis

Hey,
i want to define 3 ideal breaks (bin) for each variable one of those variables is attached in the previous email,
i don't want to consider quartile method because quartile is not working ideally for that data set because data distribution is non normal.
so i want you to suggest another method so that i can define 3 breaks with the ideal interval for Recency, frequency and monetary to calculate RFM score.
i'm again attaching you some of the data set.
please look into it and help me with the R code.
Thanks



Data

user_id

subtotal_amount

created_at

Recency

Frequency

Monetary

194849

6.99

8/22/2017

9

5

9.996

194978

14.78

8/28/2017

3

15

16.308

198614

18.44

7/31/2017

31

1

18.44

234569

34.99

8/20/2017

11

8

13.5075

252686

7.99

7/31/2017

31

2

7.99

291719

21.26

8/25/2017

6

2

15.67

291787

46.1

8/31/2017

0

2

32.57

292630

24.34

7/31/2017

31

1

24.34

295204

21.86

7/18/2017

44

1

21.86

295989

8.98

8/20/2017

11

2

14.095

298883

14.38

8/24/2017

7

2

11.185

308824

10.77

7/31/2017

31

1

10.77

308874

8.29

6/11/2017

81

1

8.29

309088

17.16

8/3/2017

28

1

17.16

309126

20.54

7/30/2017

32

1

20.54

309127

15.24

8/2/2017

29

4

13.3925

309159

10.78

8/28/2017

3

10

13.694

309170

8.66

8/29/2017

2

22

9.383636364

309190

7.19

8/31/2017

0

24

10.33791667

309218

8.49

6/22/2017

70

1

8.49

309250

18.27

7/30/2017

32

1

18.27

309358

8

8/31/2017

0

2

11.99

309418

43.21

8/13/2017

18

2

26.35

309421

6.49

8/26/2017

5

7

10.72428571

309440

20.37

6/24/2017

68

1

20.37

309468

11.37

6/10/2017

82

1

11.37

309538

9.08

7/30/2017

32

4

10.075

309548

7.06

8/30/2017

1

6

7.83

309564

9.57

6/10/2017

82

1

9.57

309616

7.37

6/27/2017

65

1

7.37

309751

8.87

8/5/2017

26

2

8.925

309788

11.21

8/4/2017

27

2

10.81

309842

10.68

8/31/2017

0

17

10.49647059

309938

17.77

8/20/2017

11

2

12.38

310017

8.06

8/31/2017

0

7

12.12

310125

8.47

8/4/2017

27

1

8.47

310126

23.66

8/5/2017

26

5

21.908

310294

12.57

8/13/2017

18

2

9.675

312589

18.34

8/29/2017

2

7

12.93

312591

11.96

8/16/2017

15

7

15.12571429

312593

8.98

7/2/2017

60

1

8.98

312595

19.37

8/18/2017

13

4

11.8025

312633

8.77

8/27/2017

4

13

7.446923077

312634

8.49

6/29/2017

63

4

8.49

312659

10.08

6/23/2017

69

1

10.08

313602

7.49

8/26/2017

5

9

8.704444444

313615

10.47

6/6/2017

86

1

10.47

313618

10.49

7/23/2017

39

1

10.49

313625

7.28

7/26/2017

36

1

7.28

313630

8.37

8/23/2017

8

3

23.95

313635

9.07

8/3/2017

28

2

8.025

313651

7.78

8/30/2017

1

1

7.78

313668

15.77

6/3/2017

89

1

15.77

313679

10.17

8/14/2017

17

2

10.17

313691

10.56

8/8/2017

23

4

12.03

313693

93.86

8/24/2017

7

5

38.108

313695

7.99

8/23/2017

8

8

7.615

313706

17.05

8/27/2017

4

4

16.355

313708

20.87

8/5/2017

26

14

12.20857143

313715

7.99

8/28/2017

3

18

10.63333333

313741

32.5

8/12/2017

19

4

17.245

313744

16.96

8/8/2017

23

1

16.96

313765

7.49

8/19/2017

12

1

7.49

313778

8.38

7/24/2017

38

1

8.38

313785

11.97

8/29/2017

2

7

10.25571429

313818

6.49

7/31/2017

31

1

6.49

313822

20.35

7/18/2017

44

1

20.35

313828

10.28

7/20/2017

42

1

10.28

313843

11.87

6/19/2017

73

1

11.87

313847

19.36

8/25/2017

6

10

9.525

313858

8.08

8/25/2017

6

6

9.076666667

313862

6

7/28/2017

34

4

11.3575

313866

11.16

8/31/2017

0

6

15.6

313868

9.27

8/26/2017

5

1

9.27

313879

10.08

7/3/2017

59

3

12.01

313889

7.97

8/18/2017

13

13

8.322307692

313890

19.86

8/6/2017

25

2

17.51

313891

17.94

7/26/2017

36

4

15.0475

313892

9.88

8/30/2017

1

8

10.39875

313899

9.27

8/31/2017

0

11

12.88909091

313904

19.94

8/9/2017

22

2

14.705

313905

19.12

8/12/2017

19

4

22.3525

313914

9.08

8/18/2017

13

6

13.64333333

313917

10.17

8/28/2017

3

1

10.17

313922

7.99

6/30/2017

62

1

7.99

313923

9.57

8/14/2017

17

3

10.07333333

313927

6.99

7/25/2017

37

1

6.99

313928

8.79

8/31/2017

0

2

7.78

313934

7.19

8/29/2017

2

3

11.01666667

313936

9.38

6/27/2017

65

2

9.83

313937

7.56

8/15/2017

16

2

9.86

313938

22.34

8/30/2017

1

5

18.678

313948

21.16

8/5/2017

26

2

19.81

313951

9.27

8/29/2017

2

1

9.27

313958

8.49

8/30/2017

1

1

8.49

313972

10.77

6/12/2017

80

1

10.77

313975

11.74

7/25/2017

37

3

13.19666667

313989

6.48

8/22/2017

9

2

14.415

313992

8.49

8/22/2017

9

3

8.323333333

313997

9.38

6/12/2017

80

1

9.38

314000

8.27

7/10/2017

52

2

8.27

314003

20.35

8/20/2017

11

9

9.475555556

314005

9.88

8/28/2017

3

34

10.44970588

314006

8.47

8/28/2017

3

8

24.32625

314017

6.88

8/3/2017

28

1

6.88

314018

17.24

7/18/2017

44

1

17.24

314020

21.36

8/29/2017

2

1

21.36

314022

10.28

8/5/2017

26

1

10.28

314023

21.64

7/4/2017

58

2

17.895

314035

12.77

7/10/2017

52

1

12.77

314037

21.74

8/12/2017

19

5

13.4

314048

10.47

8/25/2017

6

4

9.8975

314054

12.78

8/30/2017

1

9

13.40333333

314059

22.94

8/5/2017

26

1

22.94

314082

23.04

8/23/2017

8

1

23.04

314086

13.26

8/21/2017

10

4

12.39

314090

7.08

8/6/2017

25

2

8.08

314091

10.28

6/26/2017

66

2

10.28

314092

13.94

8/7/2017

24

1

13.94

314099

6.19

7/30/2017

32

1

6.19

314107

24.35

8/18/2017

13

2

21.155

314108

8.17

8/31/2017

0

25

9.0932

314111

10.58

8/28/2017

3

5

10.816

314114

7.23

8/16/2017

15

2

7.23

314120

27.24

7/22/2017

40

1

27.24

314121

14.37

8/7/2017

24

1

14.37

314122

17.66

6/21/2017

71

1

17.66

314127

21.16

8/28/2017

3

6

19.955

314134

24.62

6/30/2017

62

1

24.62

314140

27.72

8/25/2017

6

36

9.754166667

314143

14.48

8/17/2017

14

3

12.10666667

314145

21.56

7/14/2017

48

4

18.0125

314146

8.26

8/15/2017

16

5

9.788

314153

13.17

8/1/2017

30

3

15.4

314160

24.56

8/9/2017

22

3

12.84

314161

16.15

8/29/2017

2

13

18.17

314163

7.88

8/21/2017

10

2

8.175

314164

9.97

7/14/2017

48

1

9.97

314167

13.46

8/28/2017

3

4

10.96

314173

19.75

6/19/2017

73

1

19.75

314175

50.55

6/12/2017

80

1

50.55

314178

34.04

8/28/2017

3

9

18.92666667

314179

11.47

8/22/2017

9

7

15.49857143

314181

17.97

7/13/2017

49

1

17.97

314186

9.74

7/28/2017

34

1

9.74

314189

6.97

8/29/2017

2

15

9.236666667

314190

10.06

8/6/2017

25

1

10.06

314192

26.76

7/31/2017

31

1

26.76

314198

8.07

8/21/2017

10

2

7.78

314202

21.82

8/12/2017

19

5

16.184

314207

9.67

8/29/2017

2

7

11.39571429

314208

9.27

8/28/2017

3

3

9.27

314214

9.36

8/6/2017

25

3

12.54

314221

10.67

6/30/2017

62

1

10.67

314222

18.39

8/24/2017

7

8

16.1175

314223

62.42

8/24/2017

7

7

33.48285714

314226

16.71

8/16/2017

15

5

12.082

314229

18.56

8/26/2017

5

1

18.56

314231

32.21

7/9/2017

53

1

32.21

314238

16.86

8/13/2017

18

5

13.928

314239

13.66

8/25/2017

6

14

9.75

314246

22.72

8/28/2017

3

3

17.17666667

314255

8.18

8/30/2017

1

2

7.485

314256

10

7/3/2017

59

2

11.68

314258

9.47

8/11/2017

20

1

9.47

314260

18.66

8/4/2017

27

5

16.464

314263

14.16

7/25/2017

37

3

22.13333333

314274

32.82

8/6/2017

25

4

19.73

314276

13.26

8/4/2017

27

3

12.43

314283

20.25

6/16/2017

76

1

20.25

314288

8.07

7/9/2017

53

2

8.67

314289

20.14

8/30/2017

1

9

16.61555556

314296

7.99

6/30/2017

62

2

7.99

314298

7.49

8/28/2017

3

15

8.435333333

314299

30.15

7/11/2017

51

2

21.4

314301

8.69

7/19/2017

43

1

8.69

314306

13.07

7/23/2017

39

3

13.64

314314

7.74

8/31/2017

0

56

7.876071429

314315

18.94

8/17/2017

14

3

16.41333333

314325

6.79

7/29/2017

33

2

7.39

314331

7.57

8/17/2017

14

4

11.9975

314338

10.07

8/24/2017

7

2

10.07

314340

8.07

8/31/2017

0

26

11.98923077

314343

19.34

8/17/2017

14

3

19.74

314344

26.07

8/7/2017

24

1

26.07

314348

19.44

7/31/2017

31

4

16.9

314353

27.14

6/19/2017

73

1

27.14

314355

13.98

7/24/2017

38

1

13.98

314356

9.98

8/29/2017

2

12

10.505

314359

15.54

8/15/2017

16

1

15.54

314371

6.97

8/27/2017

4

18

9.247222222

314375

10.48

7/12/2017

50

7

9.217142857

314376

8.58

7/4/2017

58

6

7.795

314377

9.77

8/15/2017

16

7

13.2

314384

13.66

8/4/2017

27

2

17.995

314387

17.15

7/23/2017

39

3

16.84666667

314389

11.77

8/25/2017

6

2

11.77

314390

19.74

8/23/2017

8

1

19.74

314395

9.67

8/24/2017

7

4

9.1375

314396

7.18

8/25/2017

6

15

7.585333333

314398

12.02

8/22/2017

9

10

11.365

314401

16.54

8/31/2017

0

7

19.61571429

314408

16.27

8/25/2017

6

5

10.136

314410

12.17

7/27/2017

35

3

11.84

314413

8.28

8/29/2017

2

6

7.73

314416

20.65

8/14/2017

17

4

12.075

314420

11.47

8/26/2017

5

9

9.922222222

314424

39.88

6/14/2017

78

1

39.88

314425

8.98

8/3/2017

28

2

8.98

314431

9.87

7/23/2017

39

2

9.12

314434

25.57

8/25/2017

6

2

17.545

314439

7.39

8/29/2017

2

3

7.39

314445

7.67

8/4/2017

27

1

7.67

314446

18.14

8/12/2017

19

1

18.14

314460

7.97

8/31/2017

0

8

11.92875

314466

6.06

8/22/2017

9

3

10.51

314472

20.26

8/30/2017

1

1

20.26

314473

16.95

8/9/2017

22

2

15.025

314474

22.53

8/5/2017

26

3

20.16666667

314475

11.97

6/11/2017

81

1

11.97

314484

8.8

8/27/2017

4

10

9.492

314486

7.19

7/17/2017

45

3

7.186666667

314504

28.33

6/10/2017

82

1

28.33

314509

6.08

7/17/2017

45

3

6.846666667

314512

12.45

8/12/2017

19

5

13.516

314519

14.08

7/31/2017

31

1

14.08

314527

8.08

8/21/2017

10

8

8.51625

314531

8.27

8/31/2017

0

3

9.096666667

314532

6.38

7/10/2017

52

2

7.23

314535

29.81

7/8/2017

54

3

17.15333333

314538

8.27

8/14/2017

17

7

8.647142857

314541

9.27

8/28/2017

3

1

9.27

314544

18.16

7/30/2017

32

5

13.646

314549

8.27

8/24/2017

7

2

11.62

314556

8.07

6/15/2017

77

1

8.07

314566

7.99

8/11/2017

20

1

7.99

314571

10.27

8/29/2017

2

6

10.28666667

314581

49.94

7/25/2017

37

2

41.975

314587

7.97

8/15/2017

16

1

7.97

314595

11.18

8/23/2017

8

9

11.93333333

314597

11.95

7/4/2017

58

1

11.95

314598

10.08

8/28/2017

3

20

10.2225

314600

8.98

8/24/2017

7

2

8.03

314601

24.34

7/16/2017

46

1

24.34

314616

10.08

8/18/2017

13

4

14.52

314619

17.66

8/27/2017

4

21

15.1752381

314623

10.17

8/10/2017

21

5

11.036

314628

18.76

7/19/2017

43

4

14.9125

314632

6.68

8/25/2017

6

4

7.935

314639

17.44

7/12/2017

50

1

17.44

314640

9.67

8/4/2017

27

1

9.67

314646

29.3

6/24/2017

68

1

29.3

314650

9.47

8/31/2017

0

11

11.36727273

314670

8.49

8/30/2017

1

7

8.49

314672

7.18

7/10/2017

52

4

7.585

314678

8.17

8/30/2017

1

6

11.43666667

314688

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8/1/2017

30

1

9.47

314689

29.42

8/6/2017

25

3

28.91666667

314708

20.83

8/30/2017

1

3

12.76

314717

15.36

8/31/2017

0

12

10.28833333

314721

17.26

7/24/2017

38

4

11.6425

314723

6.79

8/26/2017

5

7

8.287142857

314726

8.37

8/18/2017

13

4

9.0675

314727

10.27

8/29/2017

2

3

10.33666667

314728

10.48

8/27/2017

4

3

9.91

314731

10.67

8/31/2017

0

1

10.67

314733

7.18

6/13/2017

79

2

7.68

314738

9.06

8/12/2017

19

10

13.196

314744

18.06

8/31/2017

0

5

19.202

314745

7.78

8/29/2017

2

11

9.722727273

314747

9.76

8/28/2017

3

3

9.693333333

314756

14.27

8/20/2017

11

2

11.625

314762

8.47

8/24/2017

7

1

8.47

314763

9.67

8/4/2017

27

3

9.206666667

314767

11.95

8/29/2017

2

30

11.36366667

314775

8.67

8/22/2017

9

1

8.67

314776

13.47

8/15/2017

16

4

10.7325

314782

8.48

8/27/2017

4

5

9.754

314783

8.57

8/18/2017

13

1

8.57

314785

7.63

8/31/2017

0

7

7.832857143

314787

23.72

8/30/2017

1

3

13.33

314793

6.99

6/10/2017

82

1

6.99

314797

10.78

8/23/2017

8

3

10.78

314803

7.28

8/28/2017

3

9

9.412222222

314807

7.32

7/18/2017

44

2

7.32

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8/31/2017

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9.83

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8/31/2017

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14

7.998571429

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10

16.641

314829

22.96

7/6/2017

56

1

22.96

314832

9.38

6/8/2017

84

1

9.38

314843

8.28

6/5/2017

87

1

8.28

314863

16.14

6/14/2017

78

1

16.14

314868

7.37

8/21/2017

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5

14.546

314871

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8/28/2017

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1

6.98

314882

13.38

7/30/2017

32

1

13.38

314883

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8/25/2017

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18

8.441666667

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9.67

8/31/2017

0

32

7.9753125

314900

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8/15/2017

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1

6.47

314902

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8/19/2017

12

4

12.2425

314904

16.56

8/16/2017

15

5

15.222

314909

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8/19/2017

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4

14.9175

314912

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8/1/2017

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3

8.71

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7/11/2017

51

2

10.18

314933

11.67

8/21/2017

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9

11.67

314940

9.06

8/8/2017

23

2

12.9

314957

8.57

8/31/2017

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6

12.78833333

314972

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6/29/2017

63

3

11.14

314975

9.66

8/9/2017

22

2

9.615

314985

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7/7/2017

55

2

8.54

314996

13.54

7/13/2017

49

2

12.295

315002

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7/8/2017

54

2

16.525

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6/23/2017

69

2

8.09

315048

17.98

8/31/2017

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2

17.98

315051

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7/7/2017

55

4

6.8125

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8/22/2017

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4

10.025

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6/27/2017

65

3

8.766666667

315059

25.14

8/9/2017

22

1

25.14

315061

30.44

6/24/2017

68

1

30.44

315063

9.67

8/30/2017

1

2

9.72

315070

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4

8.94

315072

16.96

8/15/2017

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6

17.21833333

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73

1

16.66

315082

7.67

8/7/2017

24

1

7.67

315083

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6/8/2017

84

1

30.89

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7/19/2017

43

2

9.67

315097

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7/18/2017

44

2

12.13

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6/30/2017

62

1

11.37

315110

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8/16/2017

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1

9.78

315111

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8/11/2017

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3

20.54

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7/19/2017

43

1

11.68

315122

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6/30/2017

62

1

8.27

315126

9.59

7/2/2017

60

3

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8/21/2017

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1

17.83

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7/25/2017

37

2

12.665

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8

8/26/2017

5

3

10.71333333

315155

10

7/3/2017

59

2

9.785

315156

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8/23/2017

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9

9.218888889

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16.77

8/27/2017

4

4

12.85

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1

11.28

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8/28/2017

3

2

10.175

315177

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8/15/2017

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4

10.45

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65

3

7.413333333

315187

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28

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8/18/2017

13

3

12.70666667

315195

18.85

7/29/2017

33

1

18.85

315198

10.98

7/27/2017

35

2

18.06

315203

6.99

7/7/2017

55

1

6.99

315204

16.26

8/25/2017

6

3

13.83333333

315205

31.63

8/22/2017

9

4

25.605

315230

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8/12/2017

19

3

21.18666667

315233

20.95

8/5/2017

26

1

20.95

315235

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8/6/2017

25

3

7.71

315242

11.16

6/9/2017

83

1

11.16

315246

8.98

8/30/2017

1

5

8.86

315252

8.99

8/20/2017

11

14

9.035

315262

11.87

8/29/2017

2

7

23.83

315264

13.75

6/3/2017

89

1

13.75

315266

10.59

6/11/2017

81

1

10.59

315270

11.98

8/26/2017

5

1

11.98

315273

15.16

8/24/2017

7

1

15.16

315278

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8/31/2017

0

4

11.775

315287

27.03

8/24/2017

7

3

15.45333333

315293

8.34

8/31/2017

0

4

8.1175

315294

8.47

8/24/2017

7

5

9.28

315295

24.54

8/26/2017

5

8

18.445

315296

8.47

6/1/2017

91

1

8.47

315323

21.94

8/27/2017

4

10

14.309

315329

12.37

7/31/2017

31

3

12.87

315333

6.88

6/18/2017

74

2

6.935

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3

7

8.272857143

315347

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8/10/2017

21

5

7.678

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8/11/2017

20

4

13.7975

315355

15.74

8/15/2017

16

5

16.822

315364

6.89

8/26/2017

5

7

11.82428571

315372

20.92

8/3/2017

28

4

15.1725

315375

7.55

8/6/2017

25

4

11.4875

315377

11.37

8/25/2017

6

10

10.366

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9.47

8/30/2017

1

3

7.546666667

315385

6.47

8/8/2017

23

7

6.727142857

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8/31/2017

0

12

11.265

315391

12

8/21/2017

10

1

12

315396

7.36

6/28/2017

64

1

7.36

315398

12.37

8/27/2017

4

3

10.07666667

315400

17.34

8/25/2017

6

1

17.34

315401

8.98

8/12/2017

19

6

9.126666667

315415

12.36

6/30/2017

62

1

12.36

315417

10.58

8/28/2017

3

5

9.052

315424

8.27

8/23/2017

8

9

10.50222222

315427

9.47

8/5/2017

26

2

10.57

315437

11.87

7/13/2017

49

1

11.87

315440

10.56

7/31/2017

31

3

11.42

315446

6.17

8/3/2017

28

4

17.525

315447

9.08

8/10/2017

21

3

9.806666667

315448

7.99

7/29/2017

33

2

9.28

315449

18.94

8/30/2017

1

2

12.865

315453

13.26

8/21/2017

10

5

8.512

315461

7.18

7/26/2017

36

1

7.18

315466

19.75

8/30/2017

1

4

22.1525

315468

6.99

7/29/2017

33

6

10.36166667

315473

12.94

8/29/2017

2

5

13.476

315474

8.37

8/17/2017

14

3

9.466666667

315477

6.49

8/31/2017

0

1

6.49

315480

18.94

6/25/2017

67

1

18.94

315483

12.07

8/6/2017

25

6

12.48833333

315489

8.17

8/8/2017

23

3

13.06

315492

6.67

8/8/2017

23

1

6.67

315497

9.65

8/21/2017

10

1

9.65

315498

12.36

8/5/2017

26

2

10.265

315499

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7/30/2017

32

1

13.17

315503

8.71

6/29/2017

63

5

12.854

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9.67

8/15/2017

16

5

9.992

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9.58

8/24/2017

7

2

9.125

315522

8.47

7/12/2017

50

1

8.47

315523

10.47

8/1/2017

30

2

8.63

315532

8.47

8/16/2017

15

5

11.362

315533

10.29

6/7/2017

85

1

10.29

315538

6.39

7/8/2017

54

2

16.51

315542

18.66

7/6/2017

56

1

18.66

315549

21.54

8/22/2017

9

6

20.71333333

315550

59.33

8/1/2017

30

5

19.566

315551

17.56

8/24/2017

7

5

12.908

315552

10.75

7/22/2017

40

3

8.796666667

315556

6.06

7/26/2017

36

2

7.66

315559

14.98

8/15/2017

16

4

21.93

315562

13.15

8/6/2017

25

8

9.9725

315563

9.47

8/30/2017

1

1

9.47

315567

18.77

8/28/2017

3

2

25.955

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10.86

8/22/2017

9

5

10.626

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7.38

7/31/2017

31

1

7.38

315581

8.78

8/17/2017

14

1

8.78

315582

6.99

8/19/2017

12

1

6.99

315591

22.86

8/11/2017

20

4

22.4925

315599

7.77

8/9/2017

22

1

7.77

315602

6.18

8/20/2017

11

4

6.18

315608

12.36

8/21/2017

10

1

12.36

315609

8.98

7/10/2017

52

2

11.21

315610

7.99

8/25/2017

6

6

14.73833333

315611

8.49

8/31/2017

0

3

8.323333333

315618

0

7/25/2017

37

4

17.85

315629

8.67

8/6/2017

25

4

8.17

315632

14.66

8/15/2017

16

2

10.475

315634

8.47

7/25/2017

37

2

8.82

315638

13.25

7/25/2017

37

2

13.055

315642

17.47

7/22/2017

40

2

12.13

315645

6.99

7/6/2017

56

1

6.99

315649

22.03

8/6/2017

25

1

22.03

315650

8.43

8/25/2017

6

2

9.15

315651

12.94

8/15/2017

16

5

14.666

315654

7.49

8/8/2017

23

2

9.98

315655

13.95

7/28/2017

34

2

11.21

315660

8.27

7/27/2017

35

1

8.27

315663

6.99

8/29/2017

2

5

6.664

315665

9.48

6/30/2017

62

2

8.885

315670

10.07

8/17/2017

14

2

8.47

315672

10.78

8/5/2017

26

1

10.78

315673

12.48

8/3/2017

28

2

17.265

315680

14.26

8/21/2017

10

4

14.13

315684

8.07

6/2/2017

90

1

8.07

315685

11.97

7/20/2017

42

3

10.64666667

315688

11.9

8/27/2017

4

3

10.49

315689

39.9

7/2/2017

60

1

39.9

315697

30.23

8/4/2017

27

3

18.56

315700

11.05

8/6/2017

25

4

10.335

315702

12.06

8/4/2017

27

2

10.765

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8.47

8/20/2017

11

2

9.915

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8.07

8/14/2017

17

3

8.043333333

315707

23.34

7/29/2017

33

1

23.34

315711

10.57

7/6/2017

56

4

11.3325

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22.36

8/7/2017

24

1

22.36

315717

8.88

7/22/2017

40

1

8.88

315723

10.47

8/21/2017

10

6

10.95166667

315725

6.79

8/22/2017

9

6

7.338333333

315726

10.97

6/23/2017

69

2

9.48

315730

12.01

8/30/2017

1

4

11.875

315731

28.73

8/15/2017

16

5

14.042

315740

7.28

8/9/2017

22

2

7.28

315754

8.18

6/11/2017

81

1

8.18

315755

9.24

8/27/2017

4

8

8.22375

315760

22

7/3/2017

59

1

22

315768

18.76

8/4/2017

27

2

19.405

315779

21.55

6/10/2017

82

1

21.55

315785

6.79

6/5/2017

87

1

6.79

315788

10.58

8/15/2017

16

3

10.05333333

315793

6.79

7/25/2017

37

4

9.23

315799

12.59

8/23/2017

8

3

10.35333333

315802

11.86

8/31/2017

0

3

17.02

315809

8.76

8/1/2017

30

2

8.76

315817

11.26

7/30/2017

32

2

9.765

315818

9.67

6/20/2017

72

1

9.67

315826

8.48

8/6/2017

25

4

8.8525

315845

11.07

8/5/2017

26

1

11.07

315853

8.47

7/29/2017

33

5

16.268

315854

27.93

7/9/2017

53

1

27.93

315855

12.76

7/5/2017

57

4

10.57

315856

10.78

7/28/2017

34

1

10.78

315860

17.46

8/24/2017

7

1

17.46

315861

8.49

8/8/2017

23

2

7.39

315873

32.84

7/30/2017

32

1

32.84

315875

20.75

6/12/2017

80

1

20.75

315883

19.64

6/13/2017

79

1

19.64


On 13 October 2017 at 10:35, PIKAL Petr <[hidden email]<mailto:[hidden email]>> wrote:
Hi

Your statement about attaching data is problematic. We cannot do much with it. Instead use output from dput(yourdata) to show us what exactly your data look like.

We also do not know how do you want to split your data. It would be nice if you can show also what should be the bins with respective data. Unless you provide this information you probably would not get any sensible answer.

Cheers
Petr


> -----Original Message-----
> From: R-help [mailto:[hidden email]<mailto:[hidden email]>] On Behalf Of Hemant Sain
> Sent: Thursday, October 12, 2017 10:18 AM
> To: r-help mailing list <[hidden email]<mailto:[hidden email]>>
> Subject: [R] How to define proper breaks in RFM analysis
>
> Hello,
> I'm working on RFM analysis and i wanted to define my own breaks but my
> frequency distribution is not normally distributed so when I'm using quartile its
> not giving the optimal results.
> so I'm looking for a better approach where i can define breaks dynamically
> because after visualization i can do it easily but i want to apply this model so
> that it can automatically define the breaks according to data set.
> I'm attaching sample data for reference.
>
> Thanks
>
>                            *Freq*
> 5
> 15
> 1
> 8
> 2
> 2
> 2
> 1
> 1
> 2
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>
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- odesílatel tohoto emailu informuje, že není oprávněn uzavírat za společnost žádné smlouvy s výjimkou případů, kdy k tomu byl písemně zmocněn nebo písemně pověřen a takové pověření nebo plná moc byly adresátovi tohoto emailu případně osobě, kterou adresát zastupuje, předloženy nebo jejich existence je adresátovi či osobě jím zastoupené známá.

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Re: How to define proper breaks in RFM analysis

David Winsemius

> On Oct 13, 2017, at 2:51 AM, PIKAL Petr <[hidden email]> wrote:
>
> Hi
>
> You expect us to solve your problem but you ignore advice already recieved.
>
> Your data are unreadable, use dput(yourdata) instead. see ?dput
>
>> test<-read.table("clipboard", heade=T)
> Error in scan(file = file, what = what, sep = sep, quote = quote, dec = dec,  :
>  line 115 did not have 6 elements

I didn't have such a problem: (illustrated with a more minimal example)

dat <-  scan( what=list("",1,"",1L,1L,1),
             text="194849 6.99 8/22/2017 9 5 9.996
194978 14.78 8/28/2017 3 15 16.308
198614 18.44 7/31/2017 31 1 18.44
234569 34.99 8/20/2017 11 8 13.5075
252686 7.99 7/31/2017 31 2 7.99
291719 21.26 8/25/2017 6 2 15.67
291787 46.1 8/31/2017 0 2 32.57
292630 24.34 7/31/2017 31 1 24.34
295204 21.86 7/18/2017 44 1 21.86
295989 8.98 8/20/2017 11 2 14.095
298883 14.38 8/24/2017 7 2 11.185
308824 10.77 7/31/2017 31 1 10.77")

names(dat) <- c("user_id", "subtotal_amount", "created_at", "Recency", "Frequency", "Monetary")
dat <- data.frame(dat,stringsAsFactors=FALSE)

I suspect read.table would also have worked for me, but I was expecting difficulties based on Petr's posting.


#And ended up with this result (on the original copied data):
> str(dat)
'data.frame': 500 obs. of  6 variables:
 $ user_id        : chr  "194849" "194978" "198614" "234569" ...
 $ subtotal_amount: num  6.99 14.78 18.44 34.99 7.99 ...
 $ created_at     : chr  "8/22/2017" "8/28/2017" "7/31/2017" "8/20/2017" ...
 $ Recency        : int  9 3 31 11 31 6 0 31 44 11 ...
 $ Frequency      : int  5 15 1 8 2 2 2 1 1 2 ...
 $ Monetary       : num  10 16.31 18.44 13.51 7.99 ...

...  but the following criticism seems, well, _critical_ (as in essential for one to address if a reasonable proposal is to be offered.)


> What is „ideal interval“ can you define it? Should it be such to provide eqal number of observations?

That is the crucial question for you to answer, Hemant. Read the ?quartile help page if your answer is "yes" or even "maybe".
>
> Or maybe you could normalise your values and use quartile method.

Well, maybe not so much on that last one, Petr. Normalization should not affect the classification based on quartiles. It doesn't change the ordering of variables.

--
David.

>
> Cheers
> Petr
>
> From: Hemant Sain [mailto:[hidden email]]
> Sent: Friday, October 13, 2017 8:51 AM
> To: PIKAL Petr <[hidden email]>
> Cc: r-help mailing list <[hidden email]>
> Subject: Re: [R] How to define proper breaks in RFM analysis
>
> Hey,
> i want to define 3 ideal breaks (bin) for each variable one of those variables is attached in the previous email,
> i don't want to consider quartile method because quartile is not working ideally for that data set because data distribution is non normal.
> so i want you to suggest another method so that i can define 3 breaks with the ideal interval for Recency, frequency and monetary to calculate RFM score.
> i'm again attaching you some of the data set.
> please look into it and help me with the R code.
> Thanks
>
>
>
> Data
>
> user_id
>
> subtotal_amount
>
> created_at
>
> Recency
>
> Frequency
>
> Monetary
>
> 194849
>
> 6.99
>
> 8/22/2017
>
snipped

>
>
> On 13 October 2017 at 10:35, PIKAL Petr <[hidden email]<mailto:[hidden email]>> wrote:
> Hi
>
> Your statement about attaching data is problematic. We cannot do much with it. Instead use output from dput(yourdata) to show us what exactly your data look like.
>
> We also do not know how do you want to split your data. It would be nice if you can show also what should be the bins with respective data. Unless you provide this information you probably would not get any sensible answer.
>
> Cheers
> Petr
>
>
>> -----Original Message-----
>> From: R-help [mailto:[hidden email]<mailto:[hidden email]>] On Behalf Of Hemant Sain
>> Sent: Thursday, October 12, 2017 10:18 AM
>> To: r-help mailing list <[hidden email]<mailto:[hidden email]>>
>> Subject: [R] How to define proper breaks in RFM analysis
>>
>> Hello,
>> I'm working on RFM analysis and i wanted to define my own breaks but my
>> frequency distribution is not normally distributed so when I'm using quartile its
>> not giving the optimal results.
>> so I'm looking for a better approach where i can define breaks dynamically
>> because after visualization i can do it easily but i want to apply this model so
>> that it can automatically define the breaks according to data set.
>> I'm attaching sample data for reference.
>>
>> Thanks
>>
>>                           *Freq*
>> 5
>> 15
>> 1
snipped
> .
>
> [[alternative HTML version deleted]]
>
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David Winsemius
Alameda, CA, USA

'Any technology distinguishable from magic is insufficiently advanced.'   -Gehm's Corollary to Clarke's Third Law

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Re: How to define proper breaks in RFM analysis

Jim Lemon-4
Hemant's problem is that the indicators are not distributed uniformly.
With a uniform distribution, categorization gives a reasonably optimal
separation of cases. One approach would be to drop categorization and
calculate the overall score as the mean of the standardized indicator
scores. Whether this is an option I do not know. I did offer an
"eyeball" set of breaks in a previous email, but apparently this was
not sufficient.

Jim

On Sat, Oct 14, 2017 at 4:27 AM, David Winsemius <[hidden email]> wrote:

>
>> On Oct 13, 2017, at 2:51 AM, PIKAL Petr <[hidden email]> wrote:
>>
>> Hi
>>
>> You expect us to solve your problem but you ignore advice already recieved.
>>
>> Your data are unreadable, use dput(yourdata) instead. see ?dput
>>
>>> test<-read.table("clipboard", heade=T)
>> Error in scan(file = file, what = what, sep = sep, quote = quote, dec = dec,  :
>>  line 115 did not have 6 elements
>
> I didn't have such a problem: (illustrated with a more minimal example)
>
> dat <-  scan( what=list("",1,"",1L,1L,1),
>              text="194849 6.99 8/22/2017 9 5 9.996
> 194978 14.78 8/28/2017 3 15 16.308
> 198614 18.44 7/31/2017 31 1 18.44
> 234569 34.99 8/20/2017 11 8 13.5075
> 252686 7.99 7/31/2017 31 2 7.99
> 291719 21.26 8/25/2017 6 2 15.67
> 291787 46.1 8/31/2017 0 2 32.57
> 292630 24.34 7/31/2017 31 1 24.34
> 295204 21.86 7/18/2017 44 1 21.86
> 295989 8.98 8/20/2017 11 2 14.095
> 298883 14.38 8/24/2017 7 2 11.185
> 308824 10.77 7/31/2017 31 1 10.77")
>
> names(dat) <- c("user_id", "subtotal_amount", "created_at", "Recency", "Frequency", "Monetary")
> dat <- data.frame(dat,stringsAsFactors=FALSE)
>
> I suspect read.table would also have worked for me, but I was expecting difficulties based on Petr's posting.
>
>
> #And ended up with this result (on the original copied data):
>> str(dat)
> 'data.frame':   500 obs. of  6 variables:
>  $ user_id        : chr  "194849" "194978" "198614" "234569" ...
>  $ subtotal_amount: num  6.99 14.78 18.44 34.99 7.99 ...
>  $ created_at     : chr  "8/22/2017" "8/28/2017" "7/31/2017" "8/20/2017" ...
>  $ Recency        : int  9 3 31 11 31 6 0 31 44 11 ...
>  $ Frequency      : int  5 15 1 8 2 2 2 1 1 2 ...
>  $ Monetary       : num  10 16.31 18.44 13.51 7.99 ...
>
> ...  but the following criticism seems, well, _critical_ (as in essential for one to address if a reasonable proposal is to be offered.)
>
>
>> What is „ideal interval“ can you define it? Should it be such to provide eqal number of observations?
>
> That is the crucial question for you to answer, Hemant. Read the ?quartile help page if your answer is "yes" or even "maybe".
>>
>> Or maybe you could normalise your values and use quartile method.
>
> Well, maybe not so much on that last one, Petr. Normalization should not affect the classification based on quartiles. It doesn't change the ordering of variables.
>
> --
> David.
>
>>
>> Cheers
>> Petr
>>
>> From: Hemant Sain [mailto:[hidden email]]
>> Sent: Friday, October 13, 2017 8:51 AM
>> To: PIKAL Petr <[hidden email]>
>> Cc: r-help mailing list <[hidden email]>
>> Subject: Re: [R] How to define proper breaks in RFM analysis
>>
>> Hey,
>> i want to define 3 ideal breaks (bin) for each variable one of those variables is attached in the previous email,
>> i don't want to consider quartile method because quartile is not working ideally for that data set because data distribution is non normal.
>> so i want you to suggest another method so that i can define 3 breaks with the ideal interval for Recency, frequency and monetary to calculate RFM score.
>> i'm again attaching you some of the data set.
>> please look into it and help me with the R code.
>> Thanks
>>
>>
>>
>> Data
>>
>> user_id
>>
>> subtotal_amount
>>
>> created_at
>>
>> Recency
>>
>> Frequency
>>
>> Monetary
>>
>> 194849
>>
>> 6.99
>>
>> 8/22/2017
>>
> snipped
>
>>
>>
>> On 13 October 2017 at 10:35, PIKAL Petr <[hidden email]<mailto:[hidden email]>> wrote:
>> Hi
>>
>> Your statement about attaching data is problematic. We cannot do much with it. Instead use output from dput(yourdata) to show us what exactly your data look like.
>>
>> We also do not know how do you want to split your data. It would be nice if you can show also what should be the bins with respective data. Unless you provide this information you probably would not get any sensible answer.
>>
>> Cheers
>> Petr
>>
>>
>>> -----Original Message-----
>>> From: R-help [mailto:[hidden email]<mailto:[hidden email]>] On Behalf Of Hemant Sain
>>> Sent: Thursday, October 12, 2017 10:18 AM
>>> To: r-help mailing list <[hidden email]<mailto:[hidden email]>>
>>> Subject: [R] How to define proper breaks in RFM analysis
>>>
>>> Hello,
>>> I'm working on RFM analysis and i wanted to define my own breaks but my
>>> frequency distribution is not normally distributed so when I'm using quartile its
>>> not giving the optimal results.
>>> so I'm looking for a better approach where i can define breaks dynamically
>>> because after visualization i can do it easily but i want to apply this model so
>>> that it can automatically define the breaks according to data set.
>>> I'm attaching sample data for reference.
>>>
>>> Thanks
>>>
>>>                           *Freq*
>>> 5
>>> 15
>>> 1
> snipped
>> .
>>
>>       [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
> David Winsemius
> Alameda, CA, USA
>
> 'Any technology distinguishable from magic is insufficiently advanced.'   -Gehm's Corollary to Clarke's Third Law
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.