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---
title: "Daja Vu, 2021, winter project"
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = F)
```
0. 사용한 패키지 모음
```{r message = F}
library(tidyverse)
library(gridExtra)
library(ggplot2)
library(lubridate)
library(scales)
library(xlsx)
```
---
## Train data를 통해 확인하자.
1. 전체 데이터 확인 작업
```{r}
df <- read.table("origin_data\\funda_train.csv", header = T, fileEncoding = "UTF-8", sep = ",", stringsAsFactors = F)
```
```{r}
head(df)
tail(df)
dim(df)
df[df$amount > 400000, ]
sum(is.na.data.frame(df))
```
type_of_business에 NULL이 있음<br />
store_id, card_id는 범주형 수니까 패스
```{r}
summary(df[, 3:9])
```
```{r}
attach(df)
unique(type_of_business)
summary(transacted_date)
# transacted_date: 2016-06-01 ~ 2019-02-28
dates = sort(unique(df$transacted_date))
head(dates)
tail(dates)
```
---
2. 맡은 파트별로 카테고리 붙이기
```{r}
ff = c("가정용 세탁업", "간판 및 광고물 제조업", "결혼 상담 및 준비 서비스업", "경영 컨설팅업",
"그 외 기타 분류 안된 사업지원 서비스업", "기록매체 복제업", "기타 건물 관련설비 설치 공사업",
"기타 엔지니어링 서비스업", "기타 일반 및 생활 숙박시설 운영업", "스포츠 및 레크리에이션 용품 임대업",
"애완동물 장묘 및 보호 서비스업", "애완용 동물 및 관련용품 소매업", "여관업", "여행사업", "예식장업",
"인물사진 및 행사용 영상 촬영업", "자동차 세차업", "자동차 전문 수리업", "자동차 종합 수리업",
"체형 등 기타 신체관리 서비스업", "통신장비 수리업", "택배업")
# length(ff)
```
```{r}
rectified = c()
for (i in ff) {
simple = df %>% filter(type_of_business == i)
simple["cate"] = "서비스업"
rectified = bind_rows(rectified, simple)
}
rectified[rectified$type_of_business == "택배업", "cate"] = "유통업"
rectified %>% head
```
```{r}
rectified %>% dim
rectified %>% filter(cate == "서비스업") %>% dim
rectified %>% filter(cate == "유통업") %>% dim
```
```{r}
write.csv(rectified, "업종_서비스유통.csv")
# write.csv(rectified, "업종_서비스.csv")
# write.csv(rectified, "업종_유통.csv")
```
---
3. 변수로 취급할 것들
- 날짜(transacted_date일, 월), 시간(transacted_time), 분류(cate), 판매량(amount)
```{r eval = F}
plot(df$type_of_business, df$amount)
corrplot(df$type_of_business, df$amount)
```
3-1. 판매량을 변수로 이용해볼 순 없는 걸까.
```{r}
rectified %>%
# filter(between(transacted_date, unique(rectified$transacted_date)[1], unique(rectified$transacted_date)[185])) %>%
group_by(cate, transacted_date) %>%
summarise(sumV = sum(amount)) %>%
# summarise(minV = min(amount), medianV = median(amount), meanV = mean(amount), maxV = max(amount)) %>%
ggplot(aes(color = cate)) +
geom_point(aes(x = transacted_date, y = sumV))
```
뭔가 이상한데, 원 데이터는 어떻게 보일까.
```{r}
df %>%
ggplot(aes(x = type_of_business, y = amount)) +
geom_boxplot()
```
- 결론: 판매량이 보통 이용하는 돈의 단위가 아니라서 안 될 것 같음.
<br />
3-2. 그렇다면 카드를 긁은 시간은 어떨까.
- 우리가 만든 카테고리로 '전체'를 먼저 만들자.
```{r}
categories = c("교육", "미용", "생활용품 소매업", "서비스업", "오락 및 여가", "유통업", "음식소매업", "의료", "의류", "일반 음식점", "전자기기", "휴게 음식점")
total = c()
for (i in categories) {
part = read.csv(file.choose(), header = T)
if (colnames(part)[1] != "store_id") {
part = part[, colnames(part)[-c(1)]]
}
if (length(colnames(part)) < 10) {
part["cate"] = i
}
coln = colnames(part)
if (tail(coln, 1) != "cate") {
colnames(part)[length(coln)] = "cate"
}
total = bind_rows(total, part)
}
total %>% dim
total %>% head
write.csv(total, "total.csv")
```
```{r eval = F}
total = read.csv(file.choose(), header = T)
total %>% select(transacted_date) %>% unique %>% sort %>% head
```
3-2-1. 전체에서 시각적으로 확인해보자.
```{r}
total %>%
group_by(cate, transacted_time) %>%
summarise(times = length(amount)) %>%
mutate(part = as.POSIXct(transacted_time, format = "%H:%M")) %>%
ggplot(aes(color = cate)) +
geom_point(aes(x = part, y = times)) +
scale_x_datetime(date_labels = "%H:%M") +
# geom_vline(xintercept = as.numeric(dt_val$dt[3]), color = "red", linetype = 2)
labs(x = NULL, y = NULL, color = NULL)
```
- 역시 코딩으로 저장이 최고다.
```{r eval = F}
name = paste("total_time.jpg", sep = "")
jpeg(name, width = 1200, height = 600)
plotting = total %>%
group_by(cate, transacted_time) %>%
summarise(times = length(amount)) %>%
mutate(part = as.POSIXct(transacted_time, format = "%H:%M")) %>%
ggplot(aes(color = cate)) +
geom_point(aes(x = part, y = times)) +
scale_x_datetime(date_labels = "%H:%M") +
# geom_vline(xintercept = as.numeric(dt_val$dt[3]), color = "red", linetype = 2)
labs(x = NULL, y = NULL, color = NULL)
print(plotting)
dev.off()
```
결론: 역시 판매량은 쓸 수 없을 것 같다.
<br />
3-2-2. category별로 할 순 없는 걸까.
```{r include = F}
total %>%
group_by(cate, transacted_time) %>%
summarise(times = length(amount)) %>%
mutate(part = as.POSIXct(transacted_time, format = "%H:%M")) %>%
ggplot(aes(color = cate)) +
facet_grid(cate ~ .) +
geom_point(aes(x = part, y = times)) +
scale_x_datetime(date_labels = "%H:%M") +
guides(color = F)
```
3-3. custom 카테고리 X 요일의 조합은 어떨까.
```{r}
total %>%
mutate(part = factor(wday(transacted_date))) %>%
group_by(cate, part) %>%
summarise(times = length(amount)) %>%
ggplot(aes(color = cate)) +
geom_line(aes(x = part, y = times, group = cate)) +
scale_x_discrete(label = c("일", "월", "화", "수", "목", "금", "토"))
```
<br /><br /><br />
---
## Test data를 이용해 확인해보자.
<!--
4. test 데이터를 store_id 기준, 시간 순으로 판매횟수를 시각화 해보자.
```{r}
test_tt = read.csv(file.choose(), header = T, stringsAsFactors = F)
test_tt %>% head
```
맡은 구역은 40 ~ 79
```{r}
part = test_tt %>% filter(between(store_id, 40, 79))
part %>% head
part %>% select(time) %>% unlist %>% sort %>% head
```
test 데이터에는 초가 달려있다.
```{r}
part %>%
group_by(store_id, time) %>%
summarise(times = length(amount)) %>%
.["times"] %>% summary
part %>%
mutate(part = format(as.POSIXct(time, format = "%H:%M:%S"), "%H:%M")) %>%
group_by(store_id, part) %>%
summarise(times = length(amount)) %>%
.["times"] %>% summary
```
없애자.
```{r}
final = part %>%
mutate(part = format(as.POSIXct(time, format = "%H:%M:%S"), "%H:%M")) %>%
group_by(store_id, part) %>%
summarise(times = length(amount))
```
4-1. 통합 (40 - 79)
```{r}
final %>%
ggplot(aes(color = factor(store_id))) +
geom_point(aes(x = part, y = times)) +
geom_vline(xintercept = c("12:00", "15:00", "18:00", "21:00")) +
labs(color = NULL)
```
4-2. 10개씩 (40-49, 50-59, 60-69, 70-79)
```{r}
final %>%
ggplot(aes(color = factor(store_id %% 10))) +
facet_grid(rows = vars(store_id %/% 10)) +
geom_point(aes(x = part, y = times)) +
geom_vline(xintercept = c("12:00", "15:00", "18:00", "21:00")) +
labs(color = NULL)
```
```{r eval = F}
for (i in 4:7) {
start = i * 10
finish = start + 9
name = paste("test_point", start, "-", finish, ".jpg", sep = "")
# name = paste("test_part_line_", start, "-", finish, ".jpg", sep = "")
jpeg(name, width = 800, height = 600)
plotting = final %>%
filter(between(store_id, start, finish)) %>%
ggplot(aes(color = factor(store_id))) +
geom_point(aes(x = part, y = times)) +
# geom_line(aes(x = part, y = times, group = store_id %% 10)) +
geom_vline(xintercept = "12:00") + #, color = "orange") +
geom_vline(xintercept = "15:00") + #, color = "green") +
geom_vline(xintercept = "18:00") + #, color = "blue") +
geom_vline(xintercept = "21:00") + #, color = "purple") +
labs(color = NULL)
print(plotting)
dev.off()
}
```
번외: 선으로 표현
```{r}
final %>%
ggplot(aes(color = factor(store_id %% 10))) +
facet_grid(rows = vars(store_id %/% 10)) +
geom_line(aes(x = part, y = times, group = store_id %% 10)) +
geom_vline(xintercept = c("9:00", "12:00", "15:00", "18:00", "21:00")) +
labs(x = NULL, y = NULL, color = NULL)
```
번외: colormap 이용
```{r eval = F}
library(RColorBrewer)
final %>%
ggplot(aes(color = factor(store_id %% 10))) +
facet_grid(rows = vars(store_id %/% 10)) +
geom_point(aes(x = part, y = times)) +
scale_color_brewer(palette = "Paired") +
geom_vline(xintercept = c("3:00", "6:00", "9:00", "12:00", "15:00", "18:00", "21:00")) +
# ggtitle("40~49, 50~59, 60~69, 70~79") +
labs(color = NULL)
```
4-3. 5개씩 (40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79)
```{r}
final %>%
ggplot(aes(color = factor(store_id %% 5))) +
facet_grid(rows = vars(store_id %/% 5)) +
geom_point(aes(x = part, y = times)) +
geom_vline(xintercept = c("12:00", "15:00", "18:00", "21:00")) +
labs(color = NULL)
```
한 블럭을 통해 확인해보자.
```{r}
i = 4
```
```{r}
i = i + 1
final %>%
filter(between(store_id, i * 10, i * 10 + 9)) %>%
ggplot(aes(color = factor(store_id %% 5))) +
facet_grid(rows = vars(store_id %/% 5)) +
geom_point(aes(x = part, y = times)) +
geom_vline(xintercept = c("12:00", "15:00", "18:00", "21:00")) +
ylim(0, 85) +
labs(color = NULL)
```
4-4. 1개씩 확인해보자.
```{r}
nn = 40
```
```{r}
nn = nn + 1
final %>% filter(store_id == nn) %>%
ggplot(aes(color = factor(store_id))) +
geom_point(aes(x = part, y = times)) +
geom_vline(xintercept = "12:00") + #, color = "black") +
geom_vline(xintercept = "15:00") + #, color = "grey15") +
geom_vline(xintercept = "18:00") + #, color = "yellow") +
geom_vline(xintercept = "21:00") #+ #, color = "orange") # +
# scale_x_continuous(breaks = c()) +
# scale_x_datetime(breaks = date_breaks("1 min"), labels = date_format("%H:%M"))
```
내꺼 부분 하나씩 확인
```{r}
test_tt = read.csv(file.choose(), header = T, stringsAsFactors = F)
part = test_tt %>% filter(between(store_id, 40, 79))
final = part %>%
mutate(part = as.POSIXct(time, format = "%H:%M")) %>%
group_by(store_id, part) %>%
summarise(times = length(amount))
```
```{r}
for (i in 40:79) {
name = paste(i, "point.jpg", sep = "")
jpeg(name, width = 1200, height = 600)
plotting = final %>%
filter(store_id == i) %>%
ggplot() +
geom_point(aes(x = part, y = times)) +
ggtitle(i) +
scale_x_datetime(date_labels = "%H:%M")
print(plotting)
dev.off()
}
```
```{r}
test_tt %>%
group_by(store_id) %>%
summarise(times = length(amount))
test_tt %>%
mutate(part = as.POSIXct(time, format = "%H:%M")) %>%
group_by(store_id, part = cut(part, "10 min")) %>%
summarise(times = length(amount))
ggplot(aes(color = factor(store_id))) +
geom_point(aes(x = part, y = times)) +
guides(color = F)
```
저장에 용이하도록
```{r}
name = paste("test.jpg", sep = "")
jpeg(name, width = 1200, height = 600)
plotting = test_tt %>%
mutate(part = as.POSIXct(time, format = "%H")) %>%
group_by(store_id, part) %>%
summarise(times = length(amount)) %>%
ggplot(aes(color = factor(store_id))) +
geom_point(aes(x = part, y = times)) +
scale_x_datetime(date_labels = "%H:%M") +
guides(color = F)
print(plotting)
dev.off()
```
```{r}
part = test_tt %>% filter(store_id == present)
final = part %>%
mutate(part = as.POSIXct(time, format = "%H:%M")) %>%
group_by(store_id, part) %>%
summarise(times = length(amount))
name = paste(present, "point.jpg", sep = "")
jpeg(name, width = 1200, height = 600)
plotting = final %>%
ggplot() +
geom_point(aes(x = part, y = times)) +
ggtitle(i) +
scale_x_datetime(date_labels = "%H:%M")
print(plotting)
dev.off()
```
두번째 저장을 위한
```{r}
name = paste("total_a_week_line2.jpg", sep = "")
jpeg(name, width = 1200, height = 600)
plotting = total %>%
filter(cate != "일반 음식점") %>%
mutate(part = wday(transacted_date)) %>%
group_by(cate, part) %>%
summarise(times = length(amount)) %>%
ggplot(aes(color = cate)) +
geom_line(aes(x = part, y = times)) +
# scale_x_discrete(label = c("일", "월", "화", "수", "목", "금", "토")) +
labs(y = NULL, color = NULL)
print(plotting)
dev.off()
```
-->
4. test 처음부터
- 전체 판매 건수가 300 이하인 store_id는 판별 불가로 제외하기로 하였다.
- 횟수가 적어서 패턴을 보기 어려우니까 10분 단위로 묶자.
- 알아보기 쉽게 시간으로, 시간선 그리자.
데이터 불러오기
```{r}
test_tt = read.csv(file.choose(), header = T, stringsAsFactors = F)
test_tt %>% head
```
store_id 당 전체 판매 건수 확인 (맡은 거 확인)
```{r}
removed = test_tt %>%
group_by(store_id) %>%
summarise(times = length(amount)) %>%
filter(times >= 300) %>%
select(store_id)
colnames(removed) = NULL
removed = removed %>% unlist
removed[65:96]
```
10분 단위로 판매 횟수를 묶기
```{r}
# test_part %>% head
test_part = test_tt %>%
mutate(part = as.POSIXct(time, format = "%H:%M")) %>%
group_by(store_id, part = cut(part, "10 min")) %>%
summarise(times = length(amount))
```
저장) boxplot 확인하고자
```{r}
for (i in 34) {
name = paste(i, ".jpg", sep = "")
jpeg(name, width = 600, height = 600)
plotting = test_part %>%
filter(store_id == i) %>%
mutate(part = as.POSIXct(part)) %>%
ggplot(aes(x = part, y = times, color = factor(store_id))) +
geom_boxplot(outlier.color = "lightblue") +
geom_text(aes(label = times), hjust = -0.3)
print(plotting)
dev.off()
}
```
저장) 내 파트 저장하려고.
```{r}
limit = as.POSIXct(c("00:00", "24:00"), format = "%H:%M")
time_line = as.POSIXct(c("9:00", "12:00", "15:00", "18:00", "21:00", "24:00"), format = "%H:%M")
for (i in removed[65:96]) {
name = paste(i, ".jpg", sep = "")
jpeg(name, width = 1200, height = 600)
plotting = test_part %>%
filter(store_id == i) %>%
mutate(part = as.POSIXct(part)) %>%
# 그림 그리기 시작
ggplot(aes(x = part, y = times, color = factor(store_id))) +
geom_point(size = 5) +
# 축 설정 및 선 그리기
scale_x_datetime(date_labels = "%H:%M", limits = limit) +
geom_vline(xintercept = time_line, linetype = 2) +
# label, legend, title
labs(x = NULL, y = NULL, color = NULL) +
ggtitle(i) +
# theme으로 legend 설정하기 싫은데, 다른 방법은 힘들다.
theme(legend.position = "none")
print(plotting)
dev.off()
}
```
저장) 뭉텅이로 요일
```{r}
# removed[65:96]
for (i in 0:7) {
start = 65 + i * 4
finish = start + 3
name = paste0(removed[start], "~", removed[finish], ".jpg")
jpeg(name, width = 800, height = 600)
plotting = test_tt %>%
filter(store_id %in% removed[start:finish]) %>%
mutate(part = factor(wday(date))) %>%
group_by(store_id, part) %>%
summarise(times = length(amount)) %>%
ggplot(aes(color = factor(store_id))) +
geom_line(aes(x = part, y = times, group = store_id)) +
scale_x_discrete(label = c("일", "월", "화", "수", "목", "금", "토")) +
labs(x = NULL, y = NULL, color = NULL)
print(plotting)
dev.off()
}
```
이러다 좀 더 포괄적인 저장 함수 구현에 대해 신경 쓸 것만 같은 그런 느낌
4-1. 다양하게 그림을 그려보자.
```{r}
total = read.csv(file.choose(), row.names = NULL, stringsAsFactors = F)
total %>% colnames
```
date: transacted_date, transacted_time
factor: (store_id, card_id,) card_company, installment_term(, region)
continous: amount
output: (type_of_business,) cate
```{r}
total %>%
group_by(transacted_time, card_company, cate) %>%
summarise(amount, refund = amount < 0) %>%
ggplot(aes(x = card_company, y = amount, color = cate, shape = refund)) +
geom_point() +
labs(x = NULL, y = NULL, color = NULL)
```
```{r}
tt = total %>%
group_by(cate) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(refund = refund / (times - refund))
# name = paste0("total_refund.jpg")
# jpeg(name, width = 800, height = 600)
# plotting =
tt %>%
ggplot(aes(x = cate, y = refund)) +
geom_bar(stat = "identity", fill = "lightblue") +
labs(x = NULL, y = NULL, color = NULL) +
geom_text(aes(label = round(refund, 5)))
# print(plotting)
# dev.off()
```
시간별로 amount(or 카드 긁은 횟수)를 보는데, card_company는 모양으로, cate는 색으로?
```{r}
total %>%
group_by(card_company, cate, transacted_time) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(part = as.POSIXct(transacted_time, format = "%H:%M")) %>%
ggplot(aes(x = part, y = times, color = cate, shape = card_company)) +
geom_point() +
scale_x_datetime(date_labels = "%H:%M") +
labs(x = NULL, y = NULL, color = NULL)
```
카드 회사별로 소비자의 소비 category를 시각화한 건데, 실패
업종 -> (어떤 과정에 쓰일 특징 증거들)
store_id -> (어떤 과정) -> 업종
```{r}
total %>%
group_by(card_company, cate) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
ggplot(aes(x = card_company, y = times, fill = cate)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(x = NULL, y = NULL, color = NULL)
```
4-2. 유형 확인
```{r}
test = read.csv(file.choose(), row.names = NULL, stringsAsFactors = F)
test %>% colnames
```
```{r}
test_part = test %>%
mutate(part = as.POSIXct(time, format = "%H:%M")) %>%
group_by(store_id, part = cut(part, "10 min")) %>%
summarise(times = length(amount)) %>% head
```
4-3. 나눈 유형에 업종을 붙여보자.
```{r}
betweens1 = c(4, 6, 8, 13, 16, 19, 22, 23, 31, 35, 38, 40, 54, 55, 56, 58, 62, 72, 74, 81, 85, 101, 117, 118, 120, 135, 137, 139, 143, 148, 149, 154, 155, 156, 158, 163, 177, 179, 180, 182, 184, 190, 193, 194, 78, 106, 151, 166, 176)
betweens2 = c(79, 100, 116, 142, 152, 160)
betweens3 = c(0, 2, 3, 12, 14, 15, 17, 18, 24, 30, 37, 39, 42, 48, 49, 50, 53, 59, 60, 61, 65, 66, 68, 73, 86, 90, 91, 96, 98, 99, 102, 104, 109, 112, 115, 119, 123, 125, 128, 132, 134, 159, 161, 169, 174, 178, 185, 186, 188, 191, 195, 10, 20, 25, 45) # 88
betweens4 = c(172)
betweens5 = c(11, 69, 80, 82, 87, 103, 105, 107, 110, 114, 122, 131, 140, 144, 153, 162, 165, 167)
betweens6 = c(34)
```
```{r}
name = paste0("betweens3_without_88.jpg")
jpeg(name, width = 800, height = 600)
plotting = test_part %>%
filter(store_id %in% betweens3) %>%
mutate(part = as.POSIXct(part)) %>%
ggplot(aes(color = factor(store_id))) +
geom_point(aes(x = part, y = times)) +
scale_x_datetime(date_labels = "%H:%M") +
ggtitle("3유형") +
labs(x = NULL, y = NULL, color = NULL) #+
#guides(color = F)
print(plotting)
dev.off()
```
4-4. 모비율 검정
```{r}
temp = read.xlsx(file.choose(), stringsAsFactors = F, header = T, sheetIndex = 2, encoding = "UTF-8")
temp %>% head
```
```{r eval = F}
tt = total %>%
group_by(cate) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(refund = refund / (times - refund))
colnames(tt) = NULL
tt %>% .[1, 2:3]
tt %>% .[, 1] %>% unlist
tt
```
4-4-1.여기서부터는 유형별로 다릅니다.
```{r}
x_part = 0; n = 0
for (i in c(6, 9)) {
x_part = x_part + unlist(tt[i, 2]) * unlist(tt[i, 3])
n = n + unlist(tt[i, 2])
}
x_part; n
```
나눈 유형별로 비율 따오자.
```{r}
tt1 = test %>%
filter(store_id %in% betweens1) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(refund = refund / (times - refund))
colnames(tt1) = NULL
tt1
```
```{r}
prop2 = unlist(tt1[2])
n = c(n, unlist(tt1[1]))
x = c(x_part, prop2 * unlist(tt1[1]))
prop.test(x = x, n = n, alternative = c("two.sided"), conf.level = 0.95)
```
<!-- 출처: https://rfriend.tistory.com/129 [R, Python 분석과 프로그래밍의 친구 (by R Friend)]
prop <- c(0.33, 0.41) # proportion of events
n <- c(500, 600) # number of trials
x <- prop*n # number of events
prop.test(x = x, # number of events
+ n = n, # number of trials
+ alternative = c("two.sided"), # alternative = c("two.sided", "less", "greater")
+ conf.level = 0.95) # confidence level (= 1- significance level 'alpha') -->
4-4-2. 표로 이쁘게 뽑아보자.
```{r}
tt = total %>%
group_by(cate) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
colnames(tt) = NULL
tt
```
```{r}
tt1[1, ] = test %>%
filter(store_id %in% betweens1) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt1[2, ] = test %>%
filter(store_id %in% betweens2) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt1[3, ] = test %>%
filter(store_id %in% betweens3) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt1[4, ] = test %>%
filter(store_id %in% betweens4) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt1[5, ] = test %>%
filter(store_id %in% betweens5) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
# write.csv(tt, "total_refund_ratio.csv")
# write.csv(tt1, "test_refund_ration.csv")
tt1
```
6, 9 / 11 / 4 / 8, 3 / 2 / 1
```{r}
x = 0
n = 0
for (i in c(6, 9)) {
x = x + unlist(tt[i, 3])
n = n + unlist(tt[i, 2])
}
prop.test(x = c(unlist(tt1[1, 2]), x), n = c(unlist(tt1[1, 1]), n),
alternative = c("two.sided"), conf.level = 0.95)
```
```{r eval = F}
p = x / n
p
prop.test(x = unlist(tt1[1, 2]), n = unlist(tt1[1, 1]), p = p,
alternative = c("two.sided"), conf.level = 0.95)
```
---
4-4-3. 다시 확인
test data 정제
```{r}
# test data
test = read.csv(file.choose(), stringsAsFactors = F, header = T)
test %>% head
```
```{r}
dd = test %>% filter(업종번호 == 6) %>% select(store_id) %>% unique
colnames(dd) = NULL
dd %>% unlist
```
```{r}
# test %>% filter(업종번호 == 6) %>% select(store_id) %>% unique %>% as.list %>% .[[1]] %>% length
tt1[1, ] = test %>%
filter(업종번호 == 1) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt1[2, ] = test %>%
filter(업종번호 == 2) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt1[3, ] = test %>%
filter(업종번호 == 3) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt1[4, ] = test %>%
filter(업종번호 == 4) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt1[5, ] = test %>%
filter(업종번호 == 5) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt1[6, ] = test %>%
filter(업종번호 == 6) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt1
```
train data 정제
```{r}
total = read.csv(file.choose(), stringsAsFactors = F, header = T)
total %>% head
total %>% colnames
```
```{r}
total %>% select(cate) %>% unique %>% .[1] %>% unlist
```
```{r}
tt = total %>%
filter(cate %in% c("일반 음식점", "음식소매업")) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
colnames(tt) = NULL
tt[2, ] = total %>%
filter(cate %in% c("휴게 음식점")) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt[3, ] = total %>%
filter(cate %in% c("오락 및 여가")) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt[4, ] = total %>%
filter(cate %in% c("의류", "서비스업")) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt[5, ] = total %>%
filter(cate %in% c("미용")) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt[6, ] = total %>%
filter(cate %in% c("교육")) %>%
summarise(times = length(amount), refund = sum(amount < 0)) %>%
mutate(times = times - refund)
tt
```
```{r}
tt1
```
- 6, 9 / 11 / 4 / 8, 3 / 2 / 1
하나씩 확인해보자.
```{r}
index = 5
x = c(unlist(tt[index, 2]), unlist(tt1[index, 2]))
n = c(unlist(tt[index, 1]), unlist(tt1[index, 1]))
prop.test(x = x, n = n, alternative = c("two.sided"), conf.level = 0.95)
```
```{r}
to_csv = c()
for (index in 1:6) {
x = c(unlist(tt[index, 2]), unlist(tt1[index, 2]))
n = c(unlist(tt[index, 1]), unlist(tt1[index, 1]))
# prop.test(x = x, n = n, alternative = c("two.sided"), conf.level = 0.95)
summ = prop.test(x = x, n = n, alternative = c("two.sided"), conf.level = 0.95)
temp = bind_cols(index, x[1], n[1], x[2], n[2], summ$conf.int[1], summ$conf.int[2], summ$p.value)
to_csv = bind_rows(to_csv, temp)
}
colnames(to_csv) = c("index", "total_events", "total_times", "test_events", "test_times", "conf_low", "conf_high", "p_value")
to_csv
```
```{r}
write.csv(to_csv, "prop_hypothesis.csv", sep = ",")
```
---
#### 추가) 그림 뽑아주려고.
특정 카테고리 그림 뽑기
```{r}
# name = paste("total_미용.jpg", sep = "")
# jpeg(name, width = 1000, height = 600)
# plotting =
total %>%
filter(cate %in% c("의류")) %>%
group_by(transacted_time) %>%
summarise(times = length(amount)) %>%
mutate(part = as.POSIXct(transacted_time, format = "%H:%M")) %>%
ggplot(aes(color = "red")) +
geom_point(aes(x = part, y = times)) +
scale_x_datetime(date_labels = "%H:%M") +
scale_y_continuous(limits = c(0, 1000)) +
labs(x = NULL, y = NULL, color = NULL) +
guides(color = F)
# print(plotting)
# dev.off()
```
업종번호에 따라 다르게
```{r}
name = paste0("test_custom2.jpg")
jpeg(name, width = 1200, height = 600)
plotting = test %>%
group_by(업종번호, time) %>%
summarise(times = length(amount)) %>%
filter(업종번호 != "") %>%
mutate(part = as.POSIXct(time, format = "%H:%M")) %>%
ggplot(aes(color = factor(업종번호))) +
geom_point(aes(x = part, y = times)) +
scale_x_datetime(date_labels = "%H:%M") +
labs(x = NULL, y = NULL, color = NULL)
print(plotting)
dev.off()
```
특정 store_id 그림
```{r}
for (present in c(34)) {
name = paste0(present, "_test.jpg")
jpeg(name, width = 800, height = 600)
plotting = test %>%
filter(store_id == present) %>%
group_by(time) %>%
summarise(times = length(amount)) %>%
mutate(part = as.POSIXct(time, format = "%H:%M")) %>%
ggplot() +
geom_point(aes(x = part, y = times), color = "forestgreen") +
scale_x_datetime(date_labels = "%H:%M") +