source.R
8장 소스코드
데이터를 읽어들이기 위한 함수 작성
readTsvDates <- function(base.dir, app.name, date.from, date.to) {
date.from <- as.Date(date.from)
date.to <- as.Date(date.to)
dates <- seq.Date(date.from, date.to, by = "day")
x <- ldply(foreach(day = dates, combine = rbind) %do% {
read.csv(sprintf("%s/%s/%s/data.tsv", base.dir, app.name, day),
header = T,
sep = "\t", stringsAsFactors = F)
})
x
}DAU를 읽어들이는 함수
readDau <- function(app.name, date.from, date.to = date.from) {
data <- readTsvDates("sample-data/section8/daily/dau", app.name,
date.from, date.to)
data
}DPU를 읽어들이는 함수
readDpu <- function(app.name, date.from, date.to = date.from) {
data <- readTsvDates("sample-data/section8/daily/dpu", app.name,
date.from, date.to)
data
}행동데이터를 읽어들이는 함수
readActionDaily <- function(app.name, date.from, date.to = date.from) {
data <- readTsvDates("sample-data/section8/daily/action", app.name,
date.from, date.to)
data
}데이터 읽어들이기
DAU
dau <- readDau("game-01", "2013-05-01", "2013-10-31")
head(dau)## log_date app_name user_id
## 1 2013-05-01 game-01 608801
## 2 2013-05-01 game-01 712453
## 3 2013-05-01 game-01 776853
## 4 2013-05-01 game-01 823486
## 5 2013-05-01 game-01 113600
## 6 2013-05-01 game-01 452478# DPU
dpu <- readDpu("game-01", "2013-05-01", "2013-10-31")
head(dpu)## log_date app_name user_id payment
## 1 2013-05-01 game-01 804005 571
## 2 2013-05-01 game-01 793537 81
## 3 2013-05-01 game-01 317717 81
## 4 2013-05-01 game-01 317717 81
## 5 2013-05-01 game-01 426525 324
## 6 2013-05-01 game-01 540544 243# Action
user.action <- readActionDaily("game-01", "2013-10-31", "2013-10-31")
head(user.action)## log_date app_name user_id A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12
## 1 2013-10-31 game-01 654133 0 0 0 0 0 0 0 0.00 0 0 0 0
## 2 2013-10-31 game-01 425530 0 0 0 0 10 1 233 58.25 288 230 19 2
## 3 2013-10-31 game-01 709596 0 0 0 0 0 0 0 0.00 0 0 0 0
## 4 2013-10-31 game-01 525047 0 2 0 0 9 0 0 0.00 177 160 0 0
## 5 2013-10-31 game-01 796908 0 0 0 0 0 0 0 0.00 5 30 0 0
## 6 2013-10-31 game-01 776120 0 0 0 0 9 0 0 0.00 325 195 38 8
## A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 A27 A28
## 1 0 0 0 0 0 0 0 0 0 0 0 0.0 0.00000 0.00000 0 0
## 2 19 13 13 28 6 11 0 0 0 0 0 0.5 0.92857 0.93750 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0.0 0.00000 0.00000 0 0
## 4 30 8 9 13 8 9 0 2 0 0 0 0.0 0.93333 0.90960 0 0
## 5 1 1 1 2 2 0 0 0 0 0 0 0.0 1.00000 1.00000 0 0
## 6 19 10 11 25 11 11 0 0 0 0 0 0.0 0.92424 0.94462 0 0
## A29 A30 A31 A32 A33 A34 A35 A36 A37 A38 A39 A40 A41 A42 A43 A44 A45
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00000 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0.92174 19
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00000 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 0.93125 22
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0.83333 29
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0.90256 19
## A46 A47 A48 A49 A50 A51 A52 A53 A54
## 1 0 380 25655 0 0 0 0 0 46
## 2 20 180543 347 36 22 4 0 0 71
## 3 0 416 24817 0 0 0 0 0 2
## 4 22 35200 6412 21 0 0 0 0 109
## 5 29 388 25444 1 0 0 0 0 64
## 6 38 142214 684 37 15 0 0 0 312DAU에 DPU를 결합시키기
과금 데이터 결합
dau2 <- merge(dau
,dpu[, c("log_date", "user_id", "payment"), ]
,by = c("log_date", "user_id"), all.x = T)과금 플래그를 추가하기
dau2$is.payment <- ifelse(is.na(dau2$payment), 0, 1)
head(dau2)## log_date user_id app_name payment is.payment
## 1 2013-05-01 1141 game-01 NA 0
## 2 2013-05-01 1689 game-01 NA 0
## 3 2013-05-01 2218 game-01 NA 0
## 4 2013-05-01 3814 game-01 NA 0
## 5 2013-05-01 3816 game-01 NA 0
## 6 2013-05-01 4602 game-01 NA 0비과금 레코드의 과금액에 0을 넣기
dau2$payment <- ifelse(is.na(dau2$payment), 0, dau2$payment)
head(dau2)## log_date user_id app_name payment is.payment
## 1 2013-05-01 1141 game-01 0 0
## 2 2013-05-01 1689 game-01 0 0
## 3 2013-05-01 2218 game-01 0 0
## 4 2013-05-01 3814 game-01 0 0
## 5 2013-05-01 3816 game-01 0 0
## 6 2013-05-01 4602 game-01 0 0월별로 집계하기
월 항목을 작성하기
dau2$log_month <- substr(dau2$log_date, 1, 7)월별 집계하기
mau <- ddply(dau2
,.(log_month, user_id)
,summarize
,payment = sum(payment)
,access_days = length(log_date))
head(mau)## log_month user_id payment access_days
## 1 2013-05 65 0 1
## 2 2013-05 115 0 1
## 3 2013-05 194 0 1
## 4 2013-05 426 0 4
## 5 2013-05 539 0 1
## 6 2013-05 654 0 1랭킹 범위 결정
library(ykmeans)
library(ggplot2)
library(scales)
# A47항목이 랭킹 포인트
user.action2 <- ykmeans(user.action, "A47", "A47", 3)
# 각 클러스터의 유저수
table(user.action2$cluster)##
## 1 2 3
## 2096 479 78head(user.action2)## log_date app_name user_id A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12
## 1 2013-10-31 game-01 654133 0 0 0 0 0 0 0 0.00 0 0 0 0
## 2 2013-10-31 game-01 425530 0 0 0 0 10 1 233 58.25 288 230 19 2
## 3 2013-10-31 game-01 709596 0 0 0 0 0 0 0 0.00 0 0 0 0
## 4 2013-10-31 game-01 525047 0 2 0 0 9 0 0 0.00 177 160 0 0
## 5 2013-10-31 game-01 796908 0 0 0 0 0 0 0 0.00 5 30 0 0
## 6 2013-10-31 game-01 776120 0 0 0 0 9 0 0 0.00 325 195 38 8
## A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 A27 A28
## 1 0 0 0 0 0 0 0 0 0 0 0 0.0 0.00000 0.00000 0 0
## 2 19 13 13 28 6 11 0 0 0 0 0 0.5 0.92857 0.93750 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0.0 0.00000 0.00000 0 0
## 4 30 8 9 13 8 9 0 2 0 0 0 0.0 0.93333 0.90960 0 0
## 5 1 1 1 2 2 0 0 0 0 0 0 0.0 1.00000 1.00000 0 0
## 6 19 10 11 25 11 11 0 0 0 0 0 0.0 0.92424 0.94462 0 0
## A29 A30 A31 A32 A33 A34 A35 A36 A37 A38 A39 A40 A41 A42 A43 A44 A45
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00000 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0.92174 19
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00000 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 0.93125 22
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0.83333 29
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0.90256 19
## A46 A47 A48 A49 A50 A51 A52 A53 A54 cluster .average.sd
## 1 0 380 25655 0 0 0 0 0 46 1 35707.63
## 2 20 180543 347 36 22 4 0 0 71 3 35707.63
## 3 0 416 24817 0 0 0 0 0 2 1 35707.63
## 4 22 35200 6412 21 0 0 0 0 109 1 35707.63
## 5 29 388 25444 1 0 0 0 0 64 1 35707.63
## 6 38 142214 684 37 15 0 0 0 312 3 35707.63par(mfrow=c(1,1))랭킹 포인트의 분포
ggplot(arrange(user.action2, desc(A47)), aes(x = 1:length(user_id), y = A47, col = as.factor(cluster), shape = as.factor(cluster))) +
geom_line(size=2) +
xlab("user") +
ylab("Ranking point") +
scale_y_continuous(label = comma) +
ggtitle("Ranking Point") +
theme(legend.position = "right")
랭킹 상위 유저만 고르기
user.action.h <- user.action2[user.action2$cluster >= 2, names(user.action)]
View(user.action.h)주성분 분석 실행
데이터 전처리에 편리한 함수를 이용하기 위해
기계학습 라이브러리를 사용함
library(caret)## Loading required package: latticeuser.action.f <- user.action.h[, -c(1:4)]
row.names(user.action.f) <- user.action.h$user_id
user.action.f## A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18
## 425530 0 0 0 10 1 233 58.25 288 230 19 2 19 13 13 28 6 11
## 776120 0 0 0 9 0 0 0.00 325 195 38 8 19 10 11 25 11 11
## 276197 0 0 0 7 0 58 7.25 150 100 15 3 11 5 5 19 3 7
## 221572 0 0 0 1 0 0 0.00 40 14 0 0 3 0 1 13 1 1
## 692433 0 0 0 6 0 0 0.00 102 95 0 0 2 2 5 12 5 3
## 424225 0 0 0 9 0 0 0.00 200 190 29 8 14 7 7 24 8 9
## 107707 0 0 0 7 6 96 10.67 202 330 27 5 9 9 10 29 8 5
## 476345 1 0 0 2 0 69 23.00 211 100 16 3 16 8 8 25 5 3
## 611831 0 0 0 6 0 64 7.11 249 220 19 7 12 10 12 25 7 6
## 801373 0 0 0 2 1 0 0.00 77 120 19 2 15 6 6 17 2 2
## 233368 0 0 0 6 0 0 0.00 112 70 20 2 6 4 7 23 2 6
## 705026 0 0 0 8 0 257 85.67 224 180 0 0 90 16 17 30 3 6
## 633348 0 0 0 2 0 0 0.00 70 120 0 0 50 2 3 13 2 1
## 737893 0 0 0 4 0 64 32.00 190 130 0 0 34 8 10 13 3 6
## 343705 0 0 0 6 1 67 16.75 381 190 34 9 12 12 15 29 12 10
## 827371 0 0 0 7 0 0 0.00 224 140 37 6 14 6 8 29 6 5
## 401184 0 0 0 8 1 0 0.00 282 160 23 3 90 15 16 18 5 8
## 698195 0 0 0 5 1 0 0.00 290 80 35 6 28 15 15 31 6 12
## 871680 0 0 0 6 0 0 0.00 179 200 16 1 0 3 6 17 5 6
## 777158 0 0 0 6 0 42 21.00 331 140 33 7 13 17 18 19 8 11
## 927801 1 0 0 5 0 8 8.00 171 112 0 0 1 5 5 12 4 5
## 742696 0 0 0 3 0 0 0.00 73 50 0 0 16 4 5 13 2 3
## 215714 0 0 0 12 2 151 50.33 292 220 35 5 12 7 7 30 7 12
## 491502 0 0 0 4 1 0 0.00 115 150 0 0 0 2 3 15 4 4
## 31580 0 0 0 0 1 0 0.00 154 220 34 5 0 5 6 31 6 0
## 92249 0 0 0 11 1 0 0.00 216 190 41 5 64 15 16 29 5 5
## 265317 0 0 0 4 0 50 25.00 160 180 30 7 0 6 6 23 7 5
## 202858 0 0 0 10 3 50 8.33 296 320 22 5 5 6 9 30 9 10
## 488929 0 0 0 2 0 0 0.00 118 180 22 1 45 1 4 21 4 2
## 833462 0 0 0 9 0 55 55.00 267 205 14 4 46 16 17 29 7 8
## 93246 0 0 0 7 0 0 0.00 212 170 0 0 21 5 8 15 4 7
## 325178 0 0 0 3 0 0 0.00 120 70 27 3 0 2 3 26 3 0
## 564132 0 0 0 5 0 1 1.00 128 160 33 5 8 6 6 19 5 5
## 776348 0 0 0 4 0 0 0.00 171 170 0 0 21 4 5 21 4 5
## 671661 0 0 0 5 1 97 12.13 209 189 0 0 2 4 7 20 7 8
## 464778 0 0 0 5 0 0 0.00 81 90 35 3 3 3 3 20 3 1
## 473858 1 0 0 7 0 0 0.00 198 152 0 0 0 7 9 16 9 7
## 7973 0 0 0 4 0 0 0.00 50 16 29 1 13 4 4 16 1 3
## 696807 0 0 0 6 0 0 0.00 141 150 0 0 7 1 4 22 3 6
## 196694 0 0 0 4 0 0 0.00 95 72 0 0 4 3 3 10 2 5
## 824345 0 0 0 6 1 0 0.00 268 190 36 5 20 11 12 29 8 9
## 668014 0 0 0 8 0 0 0.00 230 130 21 6 2 6 7 24 7 5
## 482337 0 0 0 4 0 0 0.00 90 120 0 0 5 3 4 19 5 3
## 882098 0 0 0 0 0 0 0.00 0 0 0 0 0 0 0 0 0 0
## 759364 0 0 0 5 0 20 10.00 150 170 16 5 7 9 11 26 5 5
## 525162 0 0 0 4 0 0 0.00 60 40 27 1 10 4 4 14 3 4
## 808508 0 0 0 6 0 0 0.00 127 100 25 6 24 10 10 22 6 5
## 678698 0 0 0 6 0 0 0.00 141 28 27 1 16 4 4 18 3 4
## 593170 0 0 0 1 0 0 0.00 30 60 40 3 3 2 3 13 3 0
## 485383 1 0 0 2 0 0 0.00 133 42 0 0 13 8 8 9 3 5
## 390021 0 0 0 5 0 0 0.00 199 190 36 6 19 11 12 25 6 6
## 821067 0 0 0 2 1 45 5.63 110 150 32 4 14 5 5 23 4 2
## 747218 0 0 0 9 1 116 23.20 280 120 34 5 11 13 13 27 6 10
## 689976 0 0 0 6 0 0 0.00 216 130 17 2 5 10 11 25 6 7
## 320747 0 0 0 5 0 17 17.00 148 90 0 0 0 2 3 17 3 4
## 184158 0 0 0 2 0 0 0.00 153 20 35 2 42 12 13 11 2 4
## 540753 0 0 0 12 3 113 22.60 318 260 30 9 35 13 15 29 10 12
## 167067 0 0 0 3 1 0 0.00 70 180 14 4 1 3 3 21 4 2
## 17423 0 0 0 7 0 95 31.67 260 190 23 4 8 9 10 19 5 7
## 748904 0 0 0 7 0 0 0.00 213 120 23 3 19 5 7 24 5 7
## 865209 0 0 0 5 0 46 46.00 190 120 0 0 0 6 6 21 7 5
## 150752 0 0 0 11 1 60 60.00 179 180 15 3 19 5 5 41 3 6
## 421427 0 0 0 3 0 0 0.00 30 40 0 0 0 0 1 14 1 0
## 356232 0 0 0 7 0 58 19.33 309 170 24 5 44 12 13 24 5 7
## 10773 0 0 0 5 0 0 0.00 108 40 22 2 12 8 12 16 2 8
## 751023 0 0 0 2 0 0 0.00 133 70 29 3 19 4 7 21 4 3
## 27244 0 0 0 1 0 0 0.00 90 90 0 0 12 4 4 13 2 4
## 312189 0 0 0 1 0 0 0.00 59 40 26 2 7 5 5 10 2 3
## 846116 1 0 0 8 1 22 22.00 182 120 0 0 0 6 7 16 6 7
## 105678 0 0 0 5 0 43 21.50 230 200 0 0 1 5 6 22 5 8
## 213086 0 0 0 6 1 18 9.00 226 150 0 0 29 7 7 19 7 5
## 188181 0 0 0 2 0 31 15.50 121 98 14 3 9 4 4 15 4 2
## 435520 0 0 0 9 0 13 13.00 188 170 16 8 11 10 11 17 9 9
## 390687 0 0 0 4 1 0 0.00 150 220 12 5 12 4 5 16 5 4
## 800776 0 0 0 8 2 134 26.80 181 181 13 3 15 8 10 26 3 7
## 505683 0 0 0 4 0 22 22.00 100 110 35 6 17 12 12 18 6 3
## 74681 0 0 0 8 2 111 6.53 271 240 23 3 9 8 12 18 8 8
## 419508 0 0 0 2 0 0 0.00 50 50 0 0 7 1 2 11 2 2
## 800412 0 0 0 10 0 0 0.00 295 215 33 3 60 18 19 27 6 9
## 436153 0 0 0 8 0 0 0.00 251 90 0 0 112 13 13 18 4 6
## 565035 0 0 0 5 0 74 24.67 215 200 0 0 18 5 7 20 5 5
## 473727 0 0 0 7 0 66 6.60 271 180 38 6 44 8 10 35 8 10
## 732116 0 0 0 8 0 96 48.00 197 110 22 3 19 6 6 19 5 8
## 217932 0 0 0 1 0 0 0.00 0 0 0 0 4 0 0 0 0 0
## 692383 0 0 0 3 0 0 0.00 80 60 0 0 12 3 4 17 3 4
## 641058 0 0 0 2 0 0 0.00 142 180 28 5 15 6 6 18 5 2
## 361127 0 0 0 6 0 0 0.00 51 28 18 2 0 2 2 20 2 0
## 872934 0 0 0 6 0 0 0.00 210 160 12 2 4 3 5 18 6 6
## 757576 0 0 0 4 0 0 0.00 201 230 35 6 32 14 17 20 6 5
## 314117 0 0 0 3 0 0 0.00 145 110 20 4 31 9 9 14 4 3
## 156429 0 0 0 0 0 0 0.00 0 0 0 0 11 0 0 0 0 0
## 241663 0 0 0 7 0 129 43.00 223 100 35 2 12 7 7 15 3 7
## 98766 0 0 0 4 3 46 11.50 251 270 38 3 23 10 12 25 7 4
## 22672 0 0 0 4 0 0 0.00 160 70 0 0 8 3 4 17 5 6
## 492588 0 0 0 5 0 0 0.00 120 80 0 0 2 2 3 13 3 5
## 570537 0 0 0 2 0 0 0.00 80 30 23 1 5 4 4 12 1 4
## 394008 0 0 0 8 0 0 0.00 162 180 0 0 0 4 4 15 5 6
## 870737 0 0 0 9 0 124 62.00 192 189 11 2 0 3 4 28 5 7
## 484271 0 0 0 5 2 75 25.00 332 190 0 0 14 17 17 41 15 16
## 596056 0 0 0 6 0 56 14.00 228 180 34 6 25 9 9 29 6 6
## 914273 0 0 0 4 0 10 2.50 289 210 0 0 160 13 14 22 5 8
## 703890 0 0 0 6 0 0 0.00 75 44 36 3 8 6 6 23 3 1
## 752265 0 0 0 6 0 0 0.00 120 70 0 0 4 2 3 22 3 6
## 274452 0 0 0 7 0 0 0.00 251 220 39 5 57 13 15 25 5 8
## 594112 1 0 0 6 0 0 0.00 121 30 25 2 25 5 5 16 3 6
## 533350 0 0 0 4 0 0 0.00 194 160 21 5 30 6 9 25 6 3
## 171027 0 0 0 5 0 0 0.00 235 60 17 3 29 12 12 14 4 5
## 583817 0 0 0 5 2 80 11.43 262 288 0 0 50 5 10 19 6 5
## 955901 0 0 0 2 0 108 54.00 50 70 34 1 14 1 1 23 2 2
## 152689 0 0 0 3 0 0 0.00 10 10 22 1 0 0 1 17 1 0
## 731882 0 0 0 5 0 165 33.00 132 112 0 0 12 5 5 15 2 5
## 826159 1 0 0 5 3 68 13.60 165 281 0 0 9 4 5 15 6 5
## 689723 0 0 0 8 0 0 0.00 169 109 14 3 9 8 8 12 4 6
## 611618 0 0 0 7 4 16 2.67 173 54 0 0 2 8 8 20 7 11
## 814817 0 0 0 5 0 0 0.00 249 170 36 6 37 8 8 28 7 5
## 761819 0 0 0 9 1 0 0.00 233 200 18 2 13 7 8 29 6 9
## 50175 0 0 0 5 0 0 0.00 200 60 0 0 3 4 4 23 4 6
## 652848 0 0 0 12 1 45 22.50 208 91 15 5 15 9 9 18 6 12
## 237780 0 0 0 3 0 55 13.75 130 126 0 0 0 2 3 18 4 3
## 82502 0 0 0 6 0 0 0.00 127 120 0 0 2 3 4 19 4 6
## 710112 0 0 0 4 0 0 0.00 108 72 14 7 0 4 5 26 7 6
## 379455 0 0 0 4 0 0 0.00 141 120 18 2 4 5 5 21 4 6
## 932452 0 0 0 4 0 0 0.00 150 180 0 0 21 6 9 14 4 5
## 921809 0 0 0 11 4 108 36.00 302 220 33 5 51 8 12 30 7 11
## 5958 0 0 0 1 2 65 32.50 120 250 0 0 9 2 3 22 3 1
## 655642 1 0 0 9 0 13 6.50 233 210 35 2 27 9 10 25 8 9
## 191892 0 0 0 9 1 0 0.00 290 130 1 6 15 8 9 29 7 5
## 269907 0 0 0 4 0 0 0.00 123 130 0 0 9 5 5 15 4 5
## 80035 0 0 0 4 0 0 0.00 58 40 33 3 13 6 6 20 3 4
## 404644 0 0 0 6 1 72 72.00 229 150 34 1 17 9 9 26 6 9
## 340034 0 0 0 6 0 52 52.00 121 40 21 4 12 5 7 22 4 5
## 995627 0 0 0 6 0 61 15.25 180 130 0 0 0 3 4 17 5 6
## 110215 0 0 0 8 0 0 0.00 245 220 36 8 4 9 10 27 9 9
## 372771 0 0 0 7 1 87 12.43 204 250 16 6 0 5 6 21 6 6
## 488697 0 0 0 10 0 0 0.00 212 70 34 3 22 14 14 14 5 10
## 624651 0 0 0 1 1 0 0.00 70 100 27 2 10 3 3 17 2 1
## 88019 0 0 0 9 0 0 0.00 290 180 30 5 32 13 14 15 6 10
## 669552 0 0 0 3 1 0 0.00 110 160 0 0 0 3 3 18 5 3
## 377104 0 0 0 8 1 0 0.00 279 180 37 7 40 13 14 30 7 7
## 858602 1 0 0 2 0 0 0.00 154 110 30 7 0 7 7 16 7 7
## 312131 0 0 0 7 2 112 28.00 273 285 14 6 27 12 13 30 8 6
## 853549 0 0 0 13 0 0 0.00 358 120 29 6 17 16 16 18 9 16
## 45539 0 0 0 4 0 0 0.00 108 150 14 5 2 3 3 17 5 4
## 711765 0 0 0 0 0 0 0.00 0 30 0 0 7 0 0 16 1 0
## 809735 0 0 0 3 0 0 0.00 80 100 26 3 51 3 3 18 5 3
## 933122 0 0 0 4 0 0 0.00 148 120 26 3 24 9 10 26 3 0
## 107581 0 0 0 3 2 109 36.33 109 216 0 0 22 3 6 15 4 2
## 742677 0 0 0 4 0 51 25.50 120 100 38 4 27 4 4 17 5 4
## 866758 0 0 0 5 1 78 6.50 230 200 28 4 10 7 8 24 6 5
## 496794 0 0 0 9 0 0 0.00 239 90 0 0 4 7 8 21 6 7
## 286418 0 0 0 8 1 112 56.00 180 160 0 0 22 6 6 21 6 5
## 281925 0 0 0 5 2 52 17.33 192 210 0 0 14 6 7 15 4 5
## 591933 0 0 0 1 0 0 0.00 114 72 0 0 12 2 4 17 3 3
## 700025 0 0 0 1 0 27 9.00 30 49 19 1 11 0 1 18 1 0
## 823545 0 0 0 7 0 0 0.00 321 200 23 7 21 13 13 31 7 7
## 662713 0 0 0 3 0 0 0.00 235 210 20 4 17 6 8 22 6 3
## 709249 0 0 0 5 0 57 11.40 145 120 28 3 35 9 9 16 3 5
## 21223 0 0 0 6 1 90 30.00 211 230 35 6 19 7 7 26 6 7
## 315944 0 0 0 5 0 53 26.50 212 120 27 8 2 7 7 24 8 9
## 4718 0 0 0 6 0 84 12.00 220 150 16 5 30 9 9 25 7 6
## 50129 1 0 0 9 0 0 0.00 256 100 36 3 30 9 9 13 7 13
## 758987 0 0 0 7 1 0 0.00 285 110 37 3 41 9 10 26 7 15
## 819151 0 0 0 6 0 0 0.00 197 140 38 6 44 9 10 25 6 6
## 63947 0 0 0 1 1 0 0.00 48 110 0 0 5 0 2 15 2 1
## 689622 0 0 0 3 0 0 0.00 30 60 24 1 1 0 1 24 1 0
## 608630 0 0 0 1 1 57 19.00 171 190 0 0 0 3 5 30 4 1
## 123387 0 0 0 5 0 139 6.04 157 156 7 4 7 5 5 18 6 5
## 434101 0 0 0 4 0 0 0.00 155 50 0 0 12 3 3 18 3 7
## 478722 0 0 0 4 0 115 57.50 267 200 23 3 17 9 10 20 6 4
## 196570 0 0 0 12 0 0 0.00 338 180 29 2 80 18 19 27 9 16
## 882588 0 0 0 2 1 30 6.00 157 186 14 5 7 6 6 19 5 4
## 28636 0 0 0 2 3 90 11.25 110 240 0 0 8 2 3 19 4 2
## 639240 0 0 0 7 0 34 11.33 286 190 0 0 29 8 9 21 9 9
## 839555 0 0 0 4 0 76 25.33 130 140 0 0 6 3 4 17 4 3
## 393250 0 0 0 1 0 0 0.00 137 130 36 2 4 4 4 22 3 5
## 923467 0 0 0 8 0 0 0.00 307 230 28 4 72 27 27 24 4 8
## 570270 0 0 0 2 0 0 0.00 151 135 0 0 19 4 7 16 5 3
## 11242 0 0 0 0 0 0 0.00 0 0 0 0 0 0 0 0 0 0
## 766653 0 0 0 1 0 0 0.00 0 0 0 0 6 0 0 0 0 0
## 912969 0 0 0 7 0 0 0.00 144 210 21 5 15 7 7 18 7 2
## 23186 0 0 0 5 0 45 11.25 140 130 22 4 6 5 6 21 4 5
## 159913 0 0 1 7 5 75 18.75 184 260 36 3 15 8 10 22 6 6
## 5671 0 0 0 3 0 70 23.33 60 70 14 1 13 3 3 22 2 3
## 700158 0 0 0 7 0 66 13.20 179 120 27 3 24 9 11 16 5 7
## 430129 0 0 0 0 0 0 0.00 70 0 0 0 13 2 3 0 0 1
## 746797 0 0 0 2 0 0 0.00 44 48 0 0 3 1 2 15 1 2
## 573540 0 0 0 1 0 0 0.00 100 130 8 4 6 3 5 19 4 1
## 703809 0 0 0 7 0 78 26.00 210 180 32 4 16 7 7 21 4 6
## 571419 0 0 0 7 1 13 13.00 198 200 26 4 12 7 7 27 4 7
## 577303 0 0 0 2 0 0 0.00 40 20 0 0 0 1 1 13 1 1
## 989183 1 0 0 5 0 53 10.60 189 152 8 6 12 6 6 18 7 8
## 524903 0 0 0 3 0 0 0.00 71 60 0 0 5 3 3 16 4 3
## 906535 0 0 0 3 0 53 26.50 104 120 19 3 0 4 4 21 4 3
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## 355353 63681 3536 17 3 0 0 0.00000 80
## 182274 54032 4269 18 2 3 0 0.00000 74정보량이 0에 가까운 변수를 삭제
nzv <- nearZeroVar(user.action.f)
user.action.f.filterd <- user.action.f[,-nzv]변수간에 상관이 높은 것을 삭제
user.action.cor <- cor(user.action.f.filterd)
highly.cor.f <- findCorrelation(user.action.cor,cutoff=.7)
user.action.f.filterd <- user.action.f.filterd[,-highly.cor.f]주성분 분석 실행
pca
user.action.pca.base <- prcomp(user.action.f.filterd, scale = T)
user.action.pca.base$rotation## PC1 PC2 PC3 PC4 PC5
## A2 0.02165096 -0.27673387 0.121454768 0.50035652 -0.192214594
## A5 0.32139471 -0.04190589 -0.118495133 0.13330903 -0.150310890
## A6 0.14461112 -0.34546707 -0.148306593 -0.23264370 0.184507799
## A8 0.11435129 -0.28058549 -0.036116514 -0.11592293 -0.182899271
## A11 0.25377524 0.26232214 0.262472749 -0.01440850 0.371848845
## A12 0.27425630 0.08852295 0.084593591 0.11998022 0.562656582
## A13 0.22541185 0.23987208 -0.283680299 -0.07105150 -0.281280089
## A14 0.34855233 0.08951403 -0.157235790 0.12043774 -0.051537415
## A17 0.34284483 -0.16917200 0.008209125 0.21258618 0.196500742
## A20 0.09955915 -0.23924453 0.109651167 0.55865375 -0.019037878
## A25 0.21733180 -0.08548726 0.439915738 -0.15667497 -0.274796445
## A43 0.29036648 -0.31722015 -0.055008918 -0.16300536 0.167967554
## A44 0.22259828 -0.09081834 0.440940821 -0.17132377 -0.306602869
## A45 0.20367652 0.19343759 0.378233716 -0.15489437 -0.106287669
## A47 0.27652862 0.12641301 -0.347028010 -0.06038622 -0.119309260
## A50 0.27439431 0.09421103 -0.291231102 0.07856812 -0.243309589
## A51 0.09577609 -0.31330243 -0.112627247 -0.33724909 0.121169331
## A53 0.02719125 -0.26906960 -0.024858007 -0.20933983 0.002657109
## A54 0.19652788 0.38277049 0.044539879 -0.03203229 0.014472392
## PC6 PC7 PC8 PC9 PC10
## A2 0.26056657 -0.083450498 0.10991966 -0.392139160 0.18210868
## A5 -0.10418351 -0.044907281 -0.08281366 0.204742907 0.20444007
## A6 0.29509290 0.083076903 -0.27205024 -0.097906790 -0.57621628
## A8 0.35618572 0.167254233 0.70256292 0.335151400 0.01890298
## A11 0.11428196 0.071271620 0.22603531 -0.151369288 -0.01972767
## A12 -0.16203896 -0.139895012 0.27286525 -0.007841965 0.02361493
## A13 -0.04326329 -0.088587239 0.13793211 -0.557610398 -0.20444104
## A14 -0.10760357 -0.092013204 0.19560240 -0.226914658 -0.10026155
## A17 -0.12014703 0.004506251 -0.17116466 0.233579432 0.14738189
## A20 0.09248643 0.250924331 -0.16029493 -0.032088255 -0.20486844
## A25 -0.23067057 -0.187978770 0.03041915 0.027103702 -0.14521959
## A43 -0.09490177 -0.064356195 -0.15201793 -0.003335045 -0.06866936
## A44 -0.18663386 -0.179759795 -0.06546737 0.030890975 -0.03518880
## A45 0.40586771 0.303095427 -0.14991608 -0.016899100 0.01230350
## A47 0.02499915 0.116012841 0.07878720 0.240030003 -0.14470635
## A50 -0.01090363 -0.010092716 -0.21118793 0.180474695 0.19710938
## A51 0.30898860 -0.322584471 -0.09515750 -0.249014831 0.51755933
## A53 -0.44424468 0.709621687 0.07684606 -0.294058786 0.25185905
## A54 0.27604220 0.259524963 -0.24303330 -0.027607955 0.24059050
## PC11 PC12 PC13 PC14 PC15
## A2 0.5238706804 -0.100785308 -0.140803078 0.145022303 -0.09526185
## A5 -0.0447412394 0.147365281 0.655828843 0.417149638 -0.24396656
## A6 0.2134123114 0.133130678 0.123638290 -0.009081881 -0.12832604
## A8 -0.0555298297 -0.114444660 0.145544388 -0.225904889 0.07542678
## A11 0.0975897986 0.335499830 0.051898124 -0.045145066 -0.14784610
## A12 0.0320383896 0.030066427 -0.066932853 -0.080035477 0.09074536
## A13 -0.1382726611 -0.106748236 0.140934569 -0.176766162 0.12351561
## A14 -0.1437497715 -0.004187666 0.001008161 0.187367036 0.06812156
## A17 0.0558548867 -0.198576764 -0.127050211 -0.037351225 0.09471090
## A20 -0.6109587446 0.100246018 -0.076625211 -0.161892500 0.03414348
## A25 -0.0573464319 -0.054070239 -0.153044504 -0.217919970 -0.65687767
## A43 0.1386514963 -0.390391877 0.113887277 -0.088426643 0.17463413
## A44 0.0078616948 0.012318001 -0.059334645 0.091199469 0.54143181
## A45 0.0176353321 0.196855102 -0.017086701 0.239829906 0.19515345
## A47 0.0001733853 -0.050645936 -0.610416761 0.426564934 -0.13071361
## A50 0.2756572497 0.469936035 -0.100205319 -0.551543083 0.08217862
## A51 -0.3810421727 0.147717501 -0.185186069 0.041201154 -0.07962688
## A53 0.0567677958 0.089858164 -0.046765307 0.017368568 -0.04702809
## A54 -0.0302839071 -0.561616136 0.072160694 -0.205274819 -0.15333908
## PC16 PC17 PC18 PC19
## A2 0.0640477140 0.05580463 0.009267409 0.072008178
## A5 0.1087264895 0.15421119 0.078995939 0.088842738
## A6 0.3069752804 -0.16143632 0.131727863 -0.084984590
## A8 0.0273730508 -0.02413135 0.029447267 -0.050940487
## A11 0.0247815911 0.34050501 -0.429046660 -0.342881332
## A12 0.1882460137 -0.05633512 0.421209665 0.469928635
## A13 -0.1185027603 0.31452280 0.343941547 -0.131896398
## A14 -0.0765114578 -0.71635410 -0.356881403 -0.044216782
## A17 -0.2106133578 -0.06864919 0.325248526 -0.652698164
## A20 0.0557253293 0.15417393 -0.093166038 0.126638011
## A25 -0.1184595186 -0.07325619 0.089670158 0.067284160
## A43 -0.4090764000 0.25727135 -0.399650816 0.316378245
## A44 0.4766741138 0.08742472 -0.077636425 -0.068580040
## A45 -0.4609003010 -0.12696423 0.252310180 0.189928663
## A47 0.1030630693 0.27545246 -0.042875712 0.088569841
## A50 -0.0005861195 -0.06355080 -0.079474845 0.145682441
## A51 0.0562964775 0.01035262 0.011181162 -0.018762928
## A53 0.0826268870 -0.03623045 0.038231002 0.009490416
## A54 0.3795262132 -0.10061183 -0.081477426 0.048903043클러스터링 실행하기
user.action.pca <- data.frame(user.action.pca.base$x)
keys <- names(user.action.pca)
user.action.km <- ykmeans(user.action.pca, keys, "PC1", 3:6)
table(user.action.km$cluster)##
## 1 2 3 4 5
## 23 228 88 164 54ggplot(user.action.km, aes(x=PC1,y=PC2,col=as.factor(cluster), shape=as.factor(cluster))) +
geom_point(size=2)
클러스터별 평균을 산출하기
user.action.f.filterd$cluster <- user.action.km$cluster
user.action.f.center <- ldply(
lapply(sort(unique(user.action.f.filterd$cluster))
,function(i) {
x <- user.action.f.filterd[user.action.f.filterd$cluster == i, -ncol(user.action.f.filterd)]
apply(x, 2, function(d) mean(d)) }
))레이더 차트용 데이터 작성하기
library(fmsb)레이더 차트에 맞게 데이터를 만들어주는 함수
createRadarChartDataFrame <- function(df) {
df <- data.frame(df)
dfmax <- apply(df, 2, max) + 1
dfmin <- apply(df, 2, min) - 1
as.data.frame(rbind(dfmax, dfmin, df))
}상관이 높은 변수 제외하기
df <- user.action.f.center[, -(ncol(user.action.f.center) - 1)]
df.cor <- cor(df)
df.highly.cor <- findCorrelation(df.cor, cutoff = 0.91)해석하기 쉬운 숫자가 되도록 직접 조절
df.filterd <- df[, -df.highly.cor]레이터 차트용 데이터 작성하기
df.filterd <- createRadarChartDataFrame(scale(df.filterd))
names(df.filterd)## [1] "A2" "A11" "A20" "A44" "A47" "A51"names(df.filterd) <- c("레벨", "지원횟수", "지원받은 횟수", "보스를 쓰러뜨린 횟수", "싸움횟수", "게임이용횟수")
radarchart(df.filterd, seg = 5, plty=1:5, plwd=4, pcol=rainbow(5))
legend("right", legend = 1:5, col=rainbow(5), lty=1:5)
# 클러스터별 KPI를 산출하기
user.action.f.filterd$user_id <- as.numeric(rownames(user.action.f.filterd))
user.action.kpi <- merge(user.action.f.filterd, mau,by = "user_id")
ddply(user.action.kpi, .(cluster), summarize, arpu = round(mean(payment)), access_days = round(mean(access_days)))## cluster arpu access_days
## 1 1 1065 20
## 2 2 216 23
## 3 3 257 17
## 4 4 150 27
## 5 5 1504 28