商業數據分析 - 期中競賽 - 圖表二

組員名單

M064020023 李劭竑

B034020022 吳明倫

M064111038 林嘉羽

M064111040 吳欣容

M064111045 周俊德

M054112060 蔡佩紜

Tony Chou 指導

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對於Ｘ表的變數解釋以及圖表

1.poorbitch(每筆消費毛利為負的產品有幾項)

.從圖中可以發現poorbitch的佔比其實很低

#poorbitch the count of who is poorbitch. it shows that there are very few poorbitch
ggplot(X,aes(x=poorbitch))+
  xlab("poorbitch")+
  ylab("count")+
  geom_histogram(fill="lightblue",color="black")+
  ggtitle("Histogram of poorbitch ")

There were 50 or more warnings (use warnings() to see the first 50)

wday the distribution of people come from Sunday to Monday

2.週日到週六的來客數

.可以發現週日的人數最多，六日一的人數為一週裡較高的

ggplot(X,aes(x=as.numeric(X$wday)))+
    xlab("weekday")+
    ylab("count")+
  geom_histogram(fill="lightblue",color="black",binwidth = 0.5)+
  ggtitle("Histogram of day")+
  scale_x_continuous(breaks=seq(0,6,1))+
    scale_x_continuous(breaks=c(0:6),labels=c("Sunday","Monday",                                                     "Tuesday","Wenseday","Thursday","Friday","Saturday"))

Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.

3.顧客的投機程度

.每筆消費產品所有毛利相加除以總數量

ggplot(X, aes(x=spec))+
  xlab("spec")+
  ylab("count")+
  geom_histogram(fill="lightblue",color = "black")+
  ggtitle("Histogram of spec")+
  xlim(-1,1)

4.顧客的消費金額

.大部分的顧客消費金額在1000以下

ggplot(data=X,aes(x=total))+
       xlab("total")+                      
       ylab("count")+
geom_histogram(fill="pink",col="steelblue")+
scale_x_continuous(breaks=seq(0,10000,1000))+ggtitle("Histogram of total")

5.商品的毛利

.大部份商品毛利都在150以下

ggplot(data=X,aes(x=gross))+
       xlab("total")+                     
       ylab("count")+
  geom_histogram(fill="pink",col="steelblue")+
       ggtitle("Histogram of gross")+
       xlim(0,1000)

6.顧客購買數量

.大部份的顧客購買產品在5樣以下

ggplot(data=X,aes(x=items))+
       xlab("total")+                      
       ylab("count")+
  geom_histogram(fill="pink",col="steelblue")+
  ggtitle("Histogram of items")+
  xlim(0,40)

7.poorbitch的出現時機

.poorbitch出現最多在禮拜一

ggplot(data=X[X$poorbitch==1,],aes(x=as.numeric(wday)))+
  xlab("poorbitch")+
  ylab("day")+
  geom_histogram(fill="pink",col="steelblue",binwidth = 0.5)+ 
  scale_x_continuous(breaks=seq(0,6,1))+
  scale_x_continuous(breaks=c(0:6),labels=c("Sunday","Monday",                                                     "Tuesday","Wenseday","Thursday","Friday","Saturday"))

Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.

8.每個地區投機人數的多寡

.EF地區的投機人數較多

ggplot(data=X[X$spec <0.5,],
      aes(as.numeric(x=area)))+
       xlab("area")+
       ylab("spec")+
  geom_histogram(fill="blue",col="steelblue",binwidth=0.5)+
  scale_x_continuous(breaks=c(1:8),labels=c("A","B","C","D","E","F","G","H"))

9.每個筆交易的金額

.大部份的金額在1000元以下

ggplot(data=X,aes(x=total))+
       xlab("total")+                      
       ylab("money")+geom_histogram(fill="pink",col="steelblue")+
  ggtitle("Histogram of total")+
  scale_x_continuous(breaks=seq(0,6000,1000))+
  xlim(0,6000)

Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.