[R] RFM Analysis Help
Jim Lemon
drjimlemon at gmail.com
Thu Oct 5 12:41:58 CEST 2017
Hi Hemant,
As I suspected, the code broke when I got to the line:
result <- rfm_auto(df, id="user_id", payment ="subtotal_amount",
date="created_at")
Error in rfm_auto(df, id = "user_id", payment = "subtotal_amount", date = "cr
eated_at") :
could not find function "rfm_auto"
It looks like you are using the hoxo-m/easyRFM function hosted on
GitHub. I may get a chance to look at this, but I can't promise
anything.
Jim
On Thu, Oct 5, 2017 at 5:28 PM, Hemant Sain <hemantsain55 at gmail.com> wrote:
> Hey Jim,
> I'm trying to perform a RFM analysis on a dataset,
> I'm able to get the results using the auto_rfm function but i want to define
> my own breaks values for RFM,
> when i tried to define my own breaks i got the identical result i.e 111 for
> every ID.
> please help me with this with working R script.
>
> Thanks
>
>
>
> Dataset:
>
> user_id subtotal_amount created_at
>
> 3451945 19.32 6/11/2017 17:40
> 5404261 20 6/16/2017 22:45
>
> 3572177 9.78 7/6/2017 0:41
> 1197515 11.97 5/20/2017 17:48
> 7288355 14.76 6/5/2017 17:48
> 3071276 7.99 6/11/2017 0:13
> 8568400 15.98 6/22/2017 0:59
> 429475 7.99 6/8/2017 18:14
> 6805938 13.97 7/1/2017 23:30
> 561442 11.67 6/22/2017 18:13
> 1127373 11.27 6/11/2017 16:43
> 5973764 12.07 6/19/2017 22:43
> 683302 12.37 6/19/2017 17:18
> 391019 26.64 6/23/2017 23:57
> 580790 22.85 5/26/2017 23:34
> 1315314 6.29 6/18/2017 23:10
> 6980574 8.67 5/13/2017 20:21
> 8279240 17.26 6/2/2017 2:46
> 8700821 9.48 6/26/2017 1:47
> 778933 13.05 5/11/2017 17:50
> 1028301 9.47 5/31/2017 20:56
> 8305179 8.49 6/16/2017 0:17
> 8294420 12.65 6/11/2017 18:04
> 5775051 11.28 6/26/2017 17:13
> 3527917 7.99 6/4/2017 21:31
> 7434689 9.78 5/5/2017 16:43
> 6299124 20.65 5/25/2017 22:55
> 407736 6.88 6/5/2017 23:40
> 6207916 11.48 6/17/2017 16:31
> 2284913 10.08 7/21/2017 16:22
> 5833389 15.67 7/31/2017 17:19
> 1537907 22.63 7/4/2017 17:23
> 8791577 12.97 7/27/2017 0:09
> 7390743 18.36 5/7/2017 16:22
> 8562057 17.64 5/27/2017 19:07
> 393153 7.98 7/19/2017 16:42
> 6764358 15.37 7/14/2017 20:20
> 6444042 9.28 5/3/2017 19:54
> 442647 15.96 7/22/2017 0:01
> 6665810 7.99 7/3/2017 16:20
> 3318928 7.99 6/11/2017 22:36
> 565493 24.56 7/11/2017 16:06
> 3337179 16.86 5/31/2017 0:20
> 394651 21.67 5/22/2017 23:30
> 421849 23.57 5/24/2017 17:12
> 404111 22.06 5/22/2017 23:05
> 3967182 8.29 7/23/2017 23:00
> 8380345 10.38 6/22/2017 15:11
> 6843512 6.49 6/16/2017 2:11
> 3562940 8.18 7/18/2017 17:09
> 678953 6.99 7/7/2017 16:37
> 477935 8.47 7/19/2017 16:07
> 8069635 6.79 7/27/2017 17:29
> 435287 15.98 7/25/2017 21:39
> 7210916 11.47 7/12/2017 17:30
> 320190 16.86 6/10/2017 23:16
> 7101677 9.28 6/6/2017 20:50
> 1358520 16.65 7/10/2017 17:52
> 485601 5.99 7/18/2017 17:46
> 7288355 24.32 7/28/2017 0:01
> 8657204 6.46 6/26/2017 22:32
> 368087 15.26 6/17/2017 17:20
> 5532715 11.78 7/24/2017 16:54
> 318181 11.16 5/14/2017 23:01
> 457094 10.92 6/15/2017 18:43
> 8733533 9.67 6/16/2017 17:06
> 2229405 15.37 6/16/2017 16:59
> 654301 11.77 6/24/2017 0:16
> 440110 26.85 5/3/2017 0:58
> 478324 8.79 7/10/2017 17:02
> 927885 17.05 5/20/2017 15:41
> 1489397 8.47 7/26/2017 17:55
> 454200 13.94 7/26/2017 1:11
> 7235501 13.81 5/19/2017 1:13
> 527673 9.97 5/20/2017 0:37
> 2438553 7.99 6/18/2017 23:09
> 2592988 39.8 6/24/2017 17:37
> 538511 15.15 6/11/2017 16:58
> 481081 14.87 5/19/2017 17:57
> 1999017 18.06 7/21/2017 16:28
> 3925889 8.08 6/26/2017 16:17
> 1046802 10.67 7/19/2017 23:07
> 434850 7.49 7/2/2017 23:00
> 370681 8.47 7/14/2017 18:19
> 3554336 21.54 7/28/2017 16:50
> 5731193 9.67 5/22/2017 16:41
> 1062134 15.05 6/5/2017 21:13
> 408175 17.94 5/23/2017 20:18
> 8733533 8.38 7/15/2017 18:16
> 588771 13.87 6/25/2017 23:49
> 6338225 6.79 7/1/2017 18:59
> 6340638 17.95 6/15/2017 23:25
> 6926244 9.07 7/17/2017 19:02
> 8880079 10.58 6/14/2017 20:37
> 7333070 20.76 5/6/2017 17:22
> 6409065 13.76 7/30/2017 18:00
> 946735 7.57 7/21/2017 19:33
> 386328 15.98 7/21/2017 1:50
> 377431 9.16 5/30/2017 22:53
> 1870101 10.28 6/1/2017 17:00
> 318128 10.74 7/15/2017 22:16
> 3122611 7.68 6/27/2017 18:01
> 7528015 17.16 6/26/2017 21:10
> 6993335 8.57 6/13/2017 17:14
> 3424400 9.27 5/11/2017 18:51
> 7515441 15.77 6/26/2017 22:25
> 3962258 10.26 6/21/2017 1:33
> 6470596 12.16 7/31/2017 17:57
> 3415331 8.08 7/17/2017 19:19
> 3301515 10 7/2/2017 20:09
> 5359396 9.47 6/26/2017 19:44
> 7975103 13.87 6/29/2017 17:08
> 440393 6.18 5/3/2017 16:48
> 2170350 17.26 7/29/2017 23:11
> 370726 19.25 6/2/2017 16:03
> 761305 21.82 7/14/2017 17:25
> 7849625 12.03 7/24/2017 16:50
> 321713 7.68 7/12/2017 16:49
> 7300770 9.57 6/27/2017 16:06
> 1095644 8.47 5/9/2017 14:40
> 321315 17.94 5/4/2017 16:07
> 7579307 11.78 7/6/2017 19:42
> 7981910 7.99 6/11/2017 20:30
> 2731720 56.86 7/21/2017 16:35
> 628140 17.45 7/20/2017 17:31
> 321926 9.48 7/25/2017 17:25
> 7000119 9.37 5/28/2017 23:37
> 9101259 14.86 6/26/2017 16:31
> 8682720 17.73 7/15/2017 17:29
> 445002 30.25 5/4/2017 23:05
> 7319495 13.48 6/26/2017 17:09
> 7900556 7.99 5/28/2017 20:17
> 927932 11.37 5/7/2017 22:35
> 674966 6.89 6/28/2017 21:27
> 323736 33.25 5/17/2017 18:02
> 1096148 12.46 7/28/2017 17:36
> 3195598 6.98 5/16/2017 19:30
> 685341 7.83 6/10/2017 17:43
> 7006511 11.15 7/10/2017 20:34
> 320245 20.47 5/20/2017 16:03
> 2387580 7.36 6/26/2017 16:54
> 492746 7.99 6/25/2017 0:59
> 974050 8.49 7/16/2017 19:01
> 1706322 14.99 5/24/2017 20:22
> 7288355 12.47 5/19/2017 1:56
> 50496250 16.58 7/26/2017 0:24
> 447509 6.29 5/5/2017 16:32
> 1330217 8.27 5/19/2017 17:34
> 2154446 7.99 7/6/2017 16:41
> 1038646 8.69 5/13/2017 22:35
> 314670 8.49 5/29/2017 23:09
> 563231 26.32 7/31/2017 18:16
> 699366 12 7/4/2017 17:46
> 8306831 6.99 5/22/2017 21:58
> 4378079 10.17 7/10/2017 18:07
> 8307283 15.87 5/31/2017 19:22
> 6493978 17.32 7/18/2017 16:13
> 1299335 17.32 7/19/2017 23:24
> 1041199 11.07 5/25/2017 17:32
> 956047 7.27 5/24/2017 16:35
> 377134 19.25 7/31/2017 18:03
> 3395660 20.56 7/24/2017 18:25
> 482106 51.29 7/14/2017 17:02
> 521363 16.27 7/18/2017 22:03
> 537518 10.98 6/13/2017 21:48
> 1943828 11.77 6/29/2017 18:30
> 606395 10.98 7/11/2017 1:15
> 1228153 15.34 5/1/2017 18:48
> 6437041 10.27 5/12/2017 15:39
> 3109401 10.28 6/26/2017 16:43
> 530302 8.57 7/10/2017 18:10
> 3109401 28.94 6/22/2017 15:43
> 6461282 12.58 6/9/2017 22:52
> 8296976 16.15 5/29/2017 23:11
> 2018954 9.98 7/14/2017 23:33
> 6241196 19.56 7/14/2017 22:54
> 8217936 11.47 6/16/2017 17:10
> 463122 8.29 7/15/2017 18:28
> 532110 10.46 7/26/2017 16:53
> 9100252 15.07 6/26/2017 15:43
> 439030 9.37 7/19/2017 17:23
> 326157 11.73 6/13/2017 0:27
> 1146325 21.85 5/16/2017 0:30
> 1502399 8.29 7/6/2017 18:57
> 2623130 7.99 5/22/2017 15:57
> 2747554 11.28 5/7/2017 17:15
> 1645383 7.77 5/31/2017 18:16
> 2739083 6.66 7/3/2017 17:53
> 6004810 10.08 6/4/2017 19:39
> 3759866 14.06 7/1/2017 18:19
> 770582 10.59 5/11/2017 22:01
> 1186104 19.92 6/12/2017 17:19
> 636778 7.99 5/5/2017 17:42
> 6147540 12.82 7/23/2017 22:26
> 5813054 14.88 6/26/2017 17:00
> 4115178 12 7/3/2017 21:24
> 8964829 7.99 6/27/2017 21:28
> 3944025 9.96 5/16/2017 16:33
> 8227862 10.48 7/23/2017 19:14
> 7104071 14.46 6/14/2017 18:53
> 4255115 7.38 5/21/2017 21:36
> 2550433 8.49 6/8/2017 17:30
> 6824172 13.17 6/14/2017 20:36
> 455032 6.97 5/17/2017 17:38
> 1206605 10.67 6/24/2017 21:08
> 337571 8.67 7/7/2017 18:52
> 633665 11.98 7/9/2017 17:16
> 2202055 8.47 6/1/2017 17:54
> 3581705 15.15 6/4/2017 17:48
> 50484076 8.28 7/26/2017 18:44
> 1145712 14.85 7/12/2017 17:48
> 599316 13.47 5/5/2017 17:35
> 321097 17.85 5/10/2017 16:17
> 316951 6.88 7/11/2017 16:59
> 566170 8.49 6/11/2017 17:19
> 4183643 9.67 6/20/2017 16:54
> 3312216 27.02 6/15/2017 20:22
> 507997 8.78 5/31/2017 20:20
> 1564977 10.77 5/15/2017 18:26
> 1063879 10.67 5/11/2017 16:56
> 6768611 10.08 6/18/2017 16:02
> 466723 9.52 5/12/2017 0:21
> 461771 9.78 6/9/2017 19:00
> 1160016 15.05 6/27/2017 16:31
> 7808075 7.99 5/10/2017 18:05
> 3388025 7.28 5/26/2017 21:04
> 367393 16.94 7/31/2017 16:09
> 748047 8.37 7/12/2017 20:35
> 5312887 7.18 7/9/2017 23:32
> 9503792 44.46 7/16/2017 23:59
> 794037 16.66 7/28/2017 23:19
> 7742605 13.28 5/19/2017 17:45
> 424303 8.47 7/3/2017 16:56
> 423285 10.07 5/16/2017 16:30
> 744532 15.45 5/10/2017 21:47
> 1315758 12.45 5/4/2017 17:17
> 5768484 9.28 6/1/2017 16:47
> 1749414 7.88 7/23/2017 18:19
> 1943828 11.77 5/24/2017 18:45
> 6554452 9.58 7/14/2017 20:16
> 7333070 6.18 5/17/2017 22:34
> 6335238 9.27 5/23/2017 0:12
> 7898943 14.96 7/27/2017 18:50
> 439121 8.78 6/28/2017 16:56
> 321315 9.47 6/2/2017 16:43
> 970063 9.67 5/15/2017 13:20
> 744532 17.54 6/11/2017 19:27
> 380324 9.47 6/24/2017 1:00
> 50512368 9.47 7/27/2017 19:13
> 475428 9.48 6/17/2017 18:44
> 378535 17.36 7/6/2017 17:08
> 483547 11.29 7/31/2017 17:53
> 7825238 9.87 5/15/2017 17:55
> 3881856 8.08 6/26/2017 17:39
> 1188351 6.77 7/18/2017 17:29
> 3955854 9.37 5/1/2017 17:18
> 431483 7.88 7/2/2017 21:45
> 6906319 7.97 5/31/2017 17:05
> 2414336 17.26 5/10/2017 18:20
> 838785 9.47 6/28/2017 17:59
> 317459 9.97 7/10/2017 16:50
> 439914 9.67 7/14/2017 18:20
> 760177 17.94 6/27/2017 16:09
> 318091 14.77 5/14/2017 17:26
> 1274436 10.28 7/26/2017 17:29
> 2626118 8.49 7/7/2017 17:29
> 3224795 13.28 7/27/2017 23:22
> 7210916 10.78 6/30/2017 17:09
> 374293 13.58 7/28/2017 23:46
> 466723 8.07 5/16/2017 16:34
> 1591942 7.99 6/22/2017 17:04
> 1967943 6.49 5/5/2017 16:42
> 477759 6.38 5/12/2017 16:50
> 331106 10.67 7/5/2017 17:05
> 1170093 7.99 5/8/2017 18:37
> 3993066 22.26 6/4/2017 23:34
> 385427 10.78 5/10/2017 21:07
> 3563453 8.29 6/6/2017 16:43
> 992545 18.35 5/3/2017 15:35
> 430623 15.17 5/5/2017 16:10
> 7288355 14.76 6/3/2017 18:38
> 9124832 8.49 7/26/2017 21:53
> 7737402 14.54 5/17/2017 23:30
> 3913032 8.18 6/19/2017 17:37
> 335627 17.35 5/31/2017 16:34
> 1340737 11.87 7/12/2017 21:47
> 468557 7.98 5/10/2017 16:47
> 458337 7.49 5/23/2017 16:36
> 606287 10.48 5/1/2017 16:33
> 2321336 8.38 5/22/2017 16:25
>
>
>
>
>
> R code
>
>
> library(data.table)
> df = fread("rfm_90.csv", header = T , stringsAsFactors = FALSE, check.names
> = FALSE)[-1,]
>
> sum(is.na(df))
>
> df$user_id = as.integer(df$user_id)
> df$subtotal_amount = as.numeric(df$subtotal_amount)
> df$created_at = as.POSIXct(df$created_at)
> df$created_at = as.Date(df$created_at)
>
>
> occurences = table(unlist(df$user_id))
> occurences = as.data.frame(occurences)
> occurences = occurences[order(-occurences$Freq),]
>
> r <-c(30,60,90)
> f <-c(2,5,8)
> m <-c(10,20,30)
>
> getScoreWithBreaks <- function(df,r,f,m) {
>
> ## scoring the Recency
> len = length(r)
> R_Score <- c(rep(1,length(df[,1])))
> df <- cbind(df,R_Score)
> for(i in 1:len){
> if(i == 1){
> p1=0
> }else{
> p1=r[i-1]
> }
> p2=r[i]
>
> if(dim(df[p1<df$Recency & df$Recency<=p2,])[1]>0) df[p1<df$Recency &
> df$Recency<=p2,]$R_Score = len - i+ 2
> }
>
> ## scoring the Frequency
> len = length(f)
> F_Score <- c(rep(1,length(df[,1])))
> df <- cbind(df,F_Score)
> for(i in 1:len){
> if(i == 1){
> p1=0
> }else{
> p1=f[i-1]
> }
> p2=f[i]
>
> if(dim(df[p1<df$Frequency & df$Frequency<=p2,])[1]>0)
> df[p1<df$Frequency & df$Frequency<=p2,]$F_Score = i
> }
> if(dim(df[f[len]<df$Frequency,])[1]>0) df[f[len]<df$Frequency,]$F_Score =
> len+1
>
> ## scoring the Monetary
> len = length(m)
> M_Score <- c(rep(1,length(df[,1])))
> df <- cbind(df,M_Score)
> for(i in 1:len){
> if(i == 1){
> p1=0
> }else{
> p1=m[i-1]
> }
> p2=m[i]
>
> if(dim(df[p1<df$Monetary & df$Monetary<=p2,])[1]>0) df[p1<df$Monetary &
> df$Monetary<=p2,]$M_Score = i
> }
> if(dim(df[m[len]<df$Monetary,])[1]>0) df[m[len]<df$Monetary,]$M_Score =
> len+1
>
> #order the dfframe by R_Score, F_Score, and M_Score desc
> df <- df[order(-df$R_Score,-df$F_Score,-df$M_Score),]
>
> # caculate the total score
> Total_Score <- c(100*df$R_Score + 10*df$F_Score+df$M_Score)
>
> df <- cbind(df,Total_Score)
>
> return(df)
>
> }
>
>
>
> df2<-getScoreWithBreaks(df,r,f,m)
>
> # result <- rfm_auto(df, id="user_id", payment ="subtotal_amount",
> date="created_at")
>
> breaks = result$breaks
> classes = result$classes
> summary = result$get_table
>
> classes = as.data.frame(classes)
> breaks = as.data.frame(breaks)
> summary = as.data.frame(summary)
>
>
>
> On 5 October 2017 at 02:52, Jim Lemon <drjimlemon at gmail.com> wrote:
>>
>> Hi Hemant,
>> No data or source code came through with your message. Try including
>> the source code in the body of the message and if your data is small,
>> the output of the "dput" function. Otherwise make up a small dataset
>> that does the same thing you describe. Also let us know what R package
>> you are using.
>>
>> Jim
>>
>>
>> On Wed, Oct 4, 2017 at 7:11 PM, Hemant Sain <hemantsain55 at gmail.com>
>> wrote:
>> > I'm trying to perform a RFM analysis on attached dataset,
>> > i'm able to get the results using the auto_rfm function but i want to
>> > define my own breaks for RFM,
>> > when i tried to define my own breaks i got the identical result i.e 111
>> > for
>> > every ID.
>> > please help me with this with working R script.
>> > Thanks
>> > hemantsain.com
>> > ______________________________________________
>> > 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.
>
>
>
>
> --
> hemantsain.com
More information about the R-help
mailing list