I'm trying to perform an RFM analysis on the attached dataset,
I'm able to get the results using the auto_rfm function but i want to
define my own breaks for RFM.
as follow
r <-c(30,60,90)
f <-c(2,5,8)
m <-c(10,20,30)
but when i tried to define my own breaks i got the identical result for RFM
i.e 111 for every ID.
please help me with this with working R script so that i can get
output according to my own defined values of RFM instead of system defined
breaks.
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 <http://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$M_Score)*
* df <- cbind(df,Total_Score)*
* return(df)*
* }*
* df2<-getScoreWithBreaks(df,r,f,m)*
*# *
*library(easyRFM)*
*result <- rfm_auto(df, id="user_id", payment ="subtotal_amount",
date="created_at")*
*breaks = result$breaks*
*classes = result$classes*
*summary = result$get_table*
*classes = as.data.frame(classes)*
*breaks = as.data.frame(breaks)*
*summary = as.data.frame(summary)*
--
hemantsain.com
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