R help - Oct 2017 - 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
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]]

> On Oct 12, 2017, at 1:17 AM, Hemant Sain <hemantsain55 at gmail.com>
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]]
> 
> ______________________________________________
> R-help at r-project.org 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

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:r-help-bounces at r-project.org] On Behalf Of Hemant
Sain
> Sent: Thursday, October 12, 2017 10:18 AM
> To: r-help mailing list <r-help at r-project.org>
> 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
> 1
> 3
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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 <petr.pikal at precheza.cz> 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:r-help-bounces at r-project.org] On Behalf Of
Hemant
> Sain
> > Sent: Thursday, October 12, 2017 10:18 AM
> > To: r-help mailing list <r-help at r-project.org>
> > 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
> > 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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