Question1
store.df=read.csv("StoreData.csv")
dim(store.df)
## [1] 2080 10
colnames(store.df)
## [1] "storeNum" "Year" "Week" "p1sales" "p2sales" "p1price"
## [7] "p2price" "p1prom" "p2prom" "country"
Question2
str(store.df)
## 'data.frame': 2080 obs. of 10 variables:
## $ storeNum: int 101 101 101 101 101 101 101 101 101 101 ...
## $ Year : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Week : int 1 2 3 4 5 6 7 8 9 10 ...
## $ p1sales : int 127 137 156 117 138 115 116 106 116 145 ...
## $ p2sales : int 106 105 97 106 100 127 90 126 94 91 ...
## $ p1price : num 2.29 2.49 2.99 2.99 2.49 2.79 2.99 2.99 2.29 2.49 ...
## $ p2price : num 2.29 2.49 2.99 3.19 2.59 2.49 3.19 2.29 2.29 2.99 ...
## $ p1prom : int 0 0 1 0 0 0 0 0 0 0 ...
## $ p2prom : int 0 0 0 0 1 0 0 0 0 0 ...
## $ country : Factor w/ 7 levels "AU","BR","CN",..: 7 7 7 7 7 7 7 7 7 7 ...
store.df$Year=as.factor(store.df$Year)
store.df$p1prom=as.factor(store.df$p1prom)
store.df$p2prom=as.factor(store.df$p2prom)
store.df$country=as.factor(store.df$country)
str(store.df)
## 'data.frame': 2080 obs. of 10 variables:
## $ storeNum: int 101 101 101 101 101 101 101 101 101 101 ...
## $ Year : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
## $ Week : int 1 2 3 4 5 6 7 8 9 10 ...
## $ p1sales : int 127 137 156 117 138 115 116 106 116 145 ...
## $ p2sales : int 106 105 97 106 100 127 90 126 94 91 ...
## $ p1price : num 2.29 2.49 2.99 2.99 2.49 2.79 2.99 2.99 2.29 2.49 ...
## $ p2price : num 2.29 2.49 2.99 3.19 2.59 2.49 3.19 2.29 2.29 2.99 ...
## $ p1prom : Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 1 1 1 ...
## $ p2prom : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ country : Factor w/ 7 levels "AU","BR","CN",..: 7 7 7 7 7 7 7 7 7 7 ...
Question3
summary(store.df)
## storeNum Year Week p1sales p2sales
## Min. :101.0 1:1040 Min. : 1.00 Min. : 73 Min. : 51.0
## 1st Qu.:105.8 2:1040 1st Qu.:13.75 1st Qu.:113 1st Qu.: 84.0
## Median :110.5 Median :26.50 Median :129 Median : 96.0
## Mean :110.5 Mean :26.50 Mean :133 Mean :100.2
## 3rd Qu.:115.2 3rd Qu.:39.25 3rd Qu.:150 3rd Qu.:113.0
## Max. :120.0 Max. :52.00 Max. :263 Max. :225.0
##
## p1price p2price p1prom p2prom country
## Min. :2.190 Min. :2.29 0:1872 0:1792 AU:104
## 1st Qu.:2.290 1st Qu.:2.49 1: 208 1: 288 BR:208
## Median :2.490 Median :2.59 CN:208
## Mean :2.544 Mean :2.70 DE:520
## 3rd Qu.:2.790 3rd Qu.:2.99 GB:312
## Max. :2.990 Max. :3.19 JP:416
## US:312
psych::describe(store.df)
## vars n mean sd median trimmed mad min max range
## storeNum 1 2080 110.50 5.77 110.50 110.50 7.41 101.00 120.00 19.0
## Year* 2 2080 1.50 0.50 1.50 1.50 0.74 1.00 2.00 1.0
## Week 3 2080 26.50 15.01 26.50 26.50 19.27 1.00 52.00 51.0
## p1sales 4 2080 133.05 28.37 129.00 131.08 26.69 73.00 263.00 190.0
## p2sales 5 2080 100.16 24.42 96.00 98.05 22.24 51.00 225.00 174.0
## p1price 6 2080 2.54 0.29 2.49 2.53 0.44 2.19 2.99 0.8
## p2price 7 2080 2.70 0.33 2.59 2.69 0.44 2.29 3.19 0.9
## p1prom* 8 2080 1.10 0.30 1.00 1.00 0.00 1.00 2.00 1.0
## p2prom* 9 2080 1.14 0.35 1.00 1.05 0.00 1.00 2.00 1.0
## country* 10 2080 4.55 1.72 4.50 4.62 2.22 1.00 7.00 6.0
## skew kurtosis se
## storeNum 0.00 -1.21 0.13
## Year* 0.00 -2.00 0.01
## Week 0.00 -1.20 0.33
## p1sales 0.74 0.66 0.62
## p2sales 0.99 1.51 0.54
## p1price 0.28 -1.44 0.01
## p2price 0.32 -1.40 0.01
## p1prom* 2.66 5.10 0.01
## p2prom* 2.09 2.38 0.01
## country* -0.29 -0.81 0.04
Question4
cntrfreq<-with(store.df,table(country))
cntrfreq
## country
## AU BR CN DE GB JP US
## 104 208 208 520 312 416 312
prop.table(cntrfreq)*100
## country
## AU BR CN DE GB JP US
## 5 10 10 25 15 20 15
Question5
mt<-xtabs(~p1prom+p2prom,data=store.df)
mt
## p2prom
## p1prom 0 1
## 0 1616 256
## 1 176 32
prop.table(mt)*100
## p2prom
## p1prom 0 1
## 0 77.692308 12.307692
## 1 8.461538 1.538462
aggregate(store.df$p1price~store.df$p1prom+store.df$p2prom,FUN=mean)
## store.df$p1prom store.df$p2prom store.df$p1price
## 1 0 0 2.543342
## 2 1 0 2.524659
## 3 0 1 2.561484
## 4 1 1 2.568125
aggregate(store.df$p2price~store.df$p1prom+store.df$p2prom,FUN=mean)
## store.df$p1prom store.df$p2prom store.df$p2price
## 1 0 0 2.700891
## 2 1 0 2.703068
## 3 0 1 2.691953
## 4 1 1 2.671250
For other insights
aggregate(store.df$p1price~store.df$p1prom,FUN=mean)
## store.df$p1prom store.df$p1price
## 1 0 2.545823
## 2 1 2.531346
aggregate(store.df$p2price~store.df$p2prom,FUN=mean)
## store.df$p2prom store.df$p2price
## 1 0 2.701105
## 2 1 2.689653
aggregate(store.df$p1sales~store.df$p1prom,FUN=mean)
## store.df$p1prom store.df$p1sales
## 1 0 129.0662
## 2 1 168.8894
aggregate(store.df$p2sales~store.df$p2prom,FUN=mean)
## store.df$p2prom store.df$p2sales
## 1 0 94.67578
## 2 1 134.26042
aggregate(store.df$p1sales~store.df$p1prom+store.df$p2prom,FUN=mean)
## store.df$p1prom store.df$p2prom store.df$p1sales
## 1 0 0 129.2630
## 2 1 0 169.3523
## 3 0 1 127.8242
## 4 1 1 166.3438
aggregate(store.df$p2sales~store.df$p1prom+store.df$p2prom,FUN=mean)
## store.df$p1prom store.df$p2prom store.df$p2sales
## 1 0 0 94.87067
## 2 1 0 92.88636
## 3 0 1 134.35547
## 4 1 1 133.50000
Qualitative insights
- Coke’s mean (133) as well median (129) sales were higher than that of Pepsi’s (100.2 and 96 respectively)
- This could probably be a result of its lower average price as well, 2.544 compared to Pepsi’s 2.7
- To describe the dataset, higher number of observations have been recorded in Germany (DE) - 25% of the observations followed by Japan (20%) and the US & the UK (10% each)
- As for the promotions, Coke was on promotion for 10% of the recorded weeks Pepsi for approximately 14% of the recorded weeks, giving higher
- During promotions, prices of both Coke and Pepsi dropped by ~ $0.011
- Comparing the sales during promotions vs that when there were none, both Coke’s and Pepsi’s sales increased by $40, however the % increase being much higher in the case of Pepsi
- Also, Coke suffered a drop of ~ $3 in sales when it had a promotion simultaneously with Pepsi, compared to when it had a promotion alone
- The effect was less pronounced on Pepsi, with it suffering just a drop of ~ $1 in sales when it had a promotion simultaneously with Coke, compared to when it had a promotion alone