WORKING DIRECTORY getwd()
LOADING THE DATA INTO store.df
store.df = read.csv("StoreData.csv")STRUCTURE OF THE DATA
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 ...QUESTION 1 1a.Write R code to show the total number of rows and columns present in the data frame store.df?
dim(store.df)## [1] 2080 10Number of rows 2080 & Number of columns 10 1b.Write R code to list the names of the column present in the data frame store.df?
names(store.df)## [1] "storeNum" "Year" "Week" "p1sales" "p2sales" "p1price"
## [7] "p2price" "p1prom" "p2prom" "country"QUESTION 2 2a.Write R code to output the data types of the different columns?
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 ...2b.Write R code to convert the data types of the columns {Year, p1prom, p2prom, country} into factor variables?
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$p2prom = as.factor(store.df$p2prom)2c. Write R code to verify the conversions done in Q2b?
is.factor(store.df$Year)## [1] TRUEis.factor(store.df$p1prom)## [1] TRUEis.factor(store.df$p2prom)## [1] TRUEis.factor(store.df$country)## [1] TRUEQUESTION 3 3a. Write R code to generate the summary statistics of the different variables present in the data frame store.df?
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:312Q3b. Write R code to generate the following table using the describe() function from the psych package in R?
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 0.04QUESTION 4 4a. Break-up store.df by country. Write R code to generate the following break-ups by country?
with(store.df,table(country))## country
## AU BR CN DE GB JP US
## 104 208 208 520 312 416 312QUESTION 5 5a.The dataset has 2080 rows of weekly data for 20 unique store branches for two different years. For product 1 (Coke) and product 2 (Pepsi), write R code to count the number of weeks, I.When both products were under promotion?
nrow(subset(store.df, store.df$p1prom==1 & store.df$p2prom==1))## [1] 32II.When product 1 (Coke) was under promotion but product 2 (Pepsi) was not under promotion?
nrow(subset(store.df, store.df$p1prom==1 & store.df$p2prom==0))## [1] 176III.When product 1 (Coke) was not under promotion but product 2 (Pepsi) was under promotion?
nrow(subset(store.df, store.df$p1prom==0 & store.df$p2prom==1))## [1] 256IV.When neither product was under promotion?
nrow(subset(store.df, store.df$p1prom==0 & store.df$p2prom==0))## [1] 16165b. Express Q5a as in percentages? I.When both products were under promotion?
nrow(subset(store.df, store.df$p1prom==1 & store.df$p2prom==1))*100/nrow(store.df)## [1] 1.538462II.When product 1 (Coke) was under promotion but product 2 (Pepsi) was not under promotion?
nrow(subset(store.df, store.df$p1prom==1 & store.df$p2prom==0))*100/nrow(store.df)## [1] 8.461538III.When product 1 (Coke) was not under promotion but product 2 (Pepsi) was under promotion?
nrow(subset(store.df, store.df$p1prom==0 & store.df$p2prom==1))*100/nrow(store.df)## [1] 12.30769IV.When neither product was under promotion?
nrow(subset(store.df, store.df$p1prom==0 & store.df$p2prom==0))*100/nrow(store.df)## [1] 77.692315c. Write R code to calculate the average price of product 1 (Coke) and product 2 (Pepsi) when both were under promotion?
a= (subset(store.df, store.df$p1prom==1 & store.df$p2prom==1))
mean(a$p1price)## [1] 2.568125mean(a$p2price)## [1] 2.671255d. Write R code to calculate the average price of product 1 (Coke) and product 2 (Pepsi) when neither of them was under promotion?
b= (subset(store.df, store.df$p1prom==0 & store.df$p2prom==0))
mean(b$p1price)## [1] 2.543342mean(b$p2price)## [1] 2.7008915e. Write R code to calculate the average price of product 1 (Coke) and product 2 (Pepsi) when only one of them was under promotion? When Product 1 was under promotion
c= (subset(store.df, store.df$p1prom==1 & store.df$p2prom==0))
mean(c$p1price)## [1] 2.524659mean(c$p2price)## [1] 2.703068When Product 2 was under promotion
d= (subset(store.df, store.df$p1prom==0 & store.df$p2prom==1))
mean(d$p1price)## [1] 2.561484mean(d$p2price)## [1] 2.691953