R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

Create a Rmd file called StoreDataAnalysis.Rmd

Run Rstudio File-> New File-> R MarkDown-> HTML Document

2.Read the data in the file StoreData.CSV into a dataframe called store.df

store.df <- read.csv(paste("StoreData.csv", sep=""))

3.Output the summary statistics of sales of Product2- Pepsi

summary(store.df$p2sales)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    51.0    84.0    96.0   100.2   113.0   225.0

4.Table aggregating the sales of Product 2 (Pepsi) in the presence / absence of promotions.

Aggregate sales in presence of promotions

promotion <- store.df[store.df$p2prom == 1 ,]
Totalsales2 <- sum(promotion$p2sales)
Totalsales2

## [1] 38667

Aggregate sales in absence of promotions

promotion <- store.df[store.df$p2prom == 0 ,]
Totalsales2 <- sum(promotion$p2sales)
Totalsales2

## [1] 169659

Sample questions:

1a. The total number of rows and columns present in the data frame store.df.

nrow(store.df)

## [1] 2080

ncol(store.df)

## [1] 10

1b. List the names of the column present in the data frame store.df.

colnames(store.df)

##  [1] "storeNum" "Year"     "Week"     "p1sales"  "p2sales"  "p1price" 
##  [7] "p2price"  "p1prom"   "p2prom"   "country"

2a. 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. Convert the data types of the columns {Year, p1prom, p2prom, country} into factor variables.

For Column Year

years=store.df$Year
years.f <- factor(years, labels = c("First year", "Second Year"))
years.f

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## [1891] First year  First year  First year  First year  First year 
## [1896] First year  First year  First year  First year  First year 
## [1901] First year  First year  First year  First year  First year 
## [1906] First year  First year  First year  First year  First year 
## [1911] First year  First year  First year  First year  First year 
## [1916] First year  First year  First year  First year  First year 
## [1921] First year  First year  First year  First year  Second Year
## [1926] Second Year Second Year Second Year Second Year Second Year
## [1931] Second Year Second Year Second Year Second Year Second Year
## [1936] Second Year Second Year Second Year Second Year Second Year
## [1941] Second Year Second Year Second Year Second Year Second Year
## [1946] Second Year Second Year Second Year Second Year Second Year
## [1951] Second Year Second Year Second Year Second Year Second Year
## [1956] Second Year Second Year Second Year Second Year Second Year
## [1961] Second Year Second Year Second Year Second Year Second Year
## [1966] Second Year Second Year Second Year Second Year Second Year
## [1971] Second Year Second Year Second Year Second Year Second Year
## [1976] Second Year First year  First year  First year  First year 
## [1981] First year  First year  First year  First year  First year 
## [1986] First year  First year  First year  First year  First year 
## [1991] First year  First year  First year  First year  First year 
## [1996] First year  First year  First year  First year  First year 
## [2001] First year  First year  First year  First year  First year 
## [2006] First year  First year  First year  First year  First year 
## [2011] First year  First year  First year  First year  First year 
## [2016] First year  First year  First year  First year  First year 
## [2021] First year  First year  First year  First year  First year 
## [2026] First year  First year  First year  Second Year Second Year
## [2031] Second Year Second Year Second Year Second Year Second Year
## [2036] Second Year Second Year Second Year Second Year Second Year
## [2041] Second Year Second Year Second Year Second Year Second Year
## [2046] Second Year Second Year Second Year Second Year Second Year
## [2051] Second Year Second Year Second Year Second Year Second Year
## [2056] Second Year Second Year Second Year Second Year Second Year
## [2061] Second Year Second Year Second Year Second Year Second Year
## [2066] Second Year Second Year Second Year Second Year Second Year
## [2071] Second Year Second Year Second Year Second Year Second Year
## [2076] Second Year Second Year Second Year Second Year Second Year
## Levels: First year Second Year

For Column p1prom

p1proms=store.df$p1prom
p1proms.f <- factor(p1proms, labels = c("No", "Yes"))
p1proms.f

##    [1] No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##   [18] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  Yes
##   [35] No  Yes No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No 
##   [52] No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No 
##   [69] No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No 
##   [86] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [103] Yes No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  Yes
##  [120] No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No 
##  [137] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [154] No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No 
##  [171] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No 
##  [188] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  Yes No 
##  [205] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No 
##  [222] No  Yes No  No  No  Yes No  No  No  No  Yes No  No  No  No  No  Yes
##  [239] No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No 
##  [256] No  No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No 
##  [273] No  No  Yes No  No  No  No  No  No  No  No  No  No  No  Yes No  Yes
##  [290] No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  Yes
##  [307] No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No 
##  [324] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [341] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [358] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [375] No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No 
##  [392] No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No 
##  [409] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No 
##  [426] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  Yes No 
##  [443] No  No  Yes No  Yes No  No  No  Yes No  No  No  No  No  No  No  No 
##  [460] Yes No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No 
##  [477] No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No 
##  [494] No  No  Yes No  No  No  No  Yes No  No  No  No  Yes No  No  No  No 
##  [511] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No 
##  [528] No  No  Yes No  No  No  No  Yes No  No  Yes No  No  No  No  No  No 
##  [545] No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [562] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [579] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [596] No  No  No  No  No  No  Yes No  No  No  No  No  No  Yes No  No  No 
##  [613] No  No  Yes No  No  No  Yes No  No  No  No  No  Yes No  No  No  No 
##  [630] No  Yes No  No  No  No  No  No  Yes No  No  No  No  No  No  No  Yes
##  [647] No  No  No  No  No  No  Yes No  No  No  No  No  Yes No  No  No  No 
##  [664] No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No 
##  [681] No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No 
##  [698] No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No 
##  [715] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [732] No  Yes No  No  No  No  No  No  Yes Yes No  No  No  Yes No  No  No 
##  [749] No  No  No  No  No  No  No  Yes Yes No  No  No  No  No  No  Yes No 
##  [766] No  No  No  No  No  No  No  No  No  Yes No  Yes No  No  No  No  No 
##  [783] No  No  Yes No  No  No  No  No  Yes No  No  No  No  No  No  No  No 
##  [800] No  No  No  No  No  Yes No  No  Yes Yes No  No  No  No  No  No  No 
##  [817] No  No  No  Yes No  No  No  No  Yes No  No  No  No  No  Yes No  No 
##  [834] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [851] No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No 
##  [868] No  No  Yes No  No  No  No  No  No  Yes No  No  No  No  No  No  No 
##  [885] No  No  No  No  No  No  Yes No  No  No  No  No  No  Yes No  No  No 
##  [902] Yes No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No 
##  [919] No  No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No 
##  [936] No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No 
##  [953] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [970] No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No 
##  [987] Yes No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1004] No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No 
## [1021] No  No  No  No  Yes No  No  Yes No  No  No  No  No  No  No  Yes No 
## [1038] No  No  No  No  No  No  No  No  Yes Yes No  No  No  No  No  No  No 
## [1055] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1072] No  No  No  No  No  No  No  No  No  No  No  Yes Yes No  No  No  No 
## [1089] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1106] No  No  No  Yes No  No  No  No  No  No  No  No  Yes No  No  No  No 
## [1123] No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No 
## [1140] No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  Yes No 
## [1157] No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1174] No  No  No  No  No  No  No  No  No  Yes No  Yes Yes No  No  No  No 
## [1191] No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1208] No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1225] No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No 
## [1242] No  No  Yes No  No  No  No  No  No  No  No  No  No  No  Yes No  No 
## [1259] No  Yes No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No 
## [1276] No  No  No  No  Yes No  No  No  No  Yes Yes Yes No  No  No  Yes No 
## [1293] No  No  No  No  No  No  Yes No  No  No  No  Yes No  No  No  Yes No 
## [1310] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1327] No  No  No  Yes No  No  No  No  No  No  Yes No  No  Yes No  No  No 
## [1344] No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No 
## [1361] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1378] No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No 
## [1395] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No 
## [1412] No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No  Yes
## [1429] No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No 
## [1446] No  No  No  No  Yes Yes No  No  No  No  Yes No  Yes Yes No  Yes No 
## [1463] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  Yes
## [1480] No  No  No  No  No  Yes No  No  No  No  No  No  No  No  Yes No  No 
## [1497] No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1514] Yes No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No 
## [1531] No  No  No  Yes No  No  No  Yes No  No  No  No  No  Yes No  No  No 
## [1548] No  No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No 
## [1565] No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No 
## [1582] No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No 
## [1599] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1616] Yes No  No  No  No  No  No  No  No  No  No  No  No  Yes Yes No  No 
## [1633] No  Yes No  No  No  No  No  No  No  No  No  No  No  No  Yes Yes No 
## [1650] No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No 
## [1667] Yes No  No  Yes No  No  No  No  No  No  No  No  Yes No  No  No  No 
## [1684] No  No  Yes No  No  Yes No  No  No  No  No  No  No  No  No  No  No 
## [1701] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1718] No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No 
## [1735] No  No  No  No  No  No  No  Yes No  No  No  No  No  Yes No  No  No 
## [1752] No  No  No  No  Yes No  No  No  No  Yes No  No  No  No  No  No  Yes
## [1769] No  No  No  Yes No  No  No  No  No  No  No  Yes Yes No  No  No  No 
## [1786] No  No  No  No  No  No  No  No  No  No  No  Yes No  Yes No  Yes No 
## [1803] Yes No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  Yes
## [1820] No  No  No  No  No  No  Yes No  No  No  No  No  No  No  Yes No  No 
## [1837] No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No 
## [1854] No  No  No  No  Yes Yes Yes No  No  No  No  No  No  Yes No  No  No 
## [1871] Yes Yes No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No 
## [1888] No  No  No  No  No  No  No  No  No  Yes No  No  No  Yes No  No  No 
## [1905] No  No  No  No  No  No  No  No  No  No  Yes Yes No  No  No  No  Yes
## [1922] No  No  Yes No  No  No  No  No  No  No  No  No  No  No  Yes No  No 
## [1939] No  No  No  Yes No  No  No  No  No  No  Yes No  No  Yes No  No  Yes
## [1956] No  Yes No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No 
## [1973] No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No 
## [1990] No  No  No  No  No  No  No  No  No  No  No  No  No  Yes Yes No  No 
## [2007] No  No  No  No  Yes Yes No  No  No  No  No  No  Yes No  No  No  No 
## [2024] No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No 
## [2041] No  No  No  No  Yes No  No  No  No  Yes No  No  No  No  Yes No  No 
## [2058] No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No 
## [2075] No  No  No  No  No  No 
## Levels: No Yes

For Column p2prom

p2proms=store.df$p1prom
p2proms.f <- factor(p1proms, labels = c("No", "Yes"))
p2proms.f

##    [1] No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##   [18] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  Yes
##   [35] No  Yes No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No 
##   [52] No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No 
##   [69] No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No 
##   [86] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [103] Yes No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  Yes
##  [120] No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No 
##  [137] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [154] No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No 
##  [171] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No 
##  [188] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  Yes No 
##  [205] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No 
##  [222] No  Yes No  No  No  Yes No  No  No  No  Yes No  No  No  No  No  Yes
##  [239] No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No 
##  [256] No  No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No 
##  [273] No  No  Yes No  No  No  No  No  No  No  No  No  No  No  Yes No  Yes
##  [290] No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  Yes
##  [307] No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No 
##  [324] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [341] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [358] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [375] No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No 
##  [392] No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No 
##  [409] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No 
##  [426] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  Yes No 
##  [443] No  No  Yes No  Yes No  No  No  Yes No  No  No  No  No  No  No  No 
##  [460] Yes No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No 
##  [477] No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No 
##  [494] No  No  Yes No  No  No  No  Yes No  No  No  No  Yes No  No  No  No 
##  [511] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No 
##  [528] No  No  Yes No  No  No  No  Yes No  No  Yes No  No  No  No  No  No 
##  [545] No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [562] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [579] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [596] No  No  No  No  No  No  Yes No  No  No  No  No  No  Yes No  No  No 
##  [613] No  No  Yes No  No  No  Yes No  No  No  No  No  Yes No  No  No  No 
##  [630] No  Yes No  No  No  No  No  No  Yes No  No  No  No  No  No  No  Yes
##  [647] No  No  No  No  No  No  Yes No  No  No  No  No  Yes No  No  No  No 
##  [664] No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No 
##  [681] No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No 
##  [698] No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No 
##  [715] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [732] No  Yes No  No  No  No  No  No  Yes Yes No  No  No  Yes No  No  No 
##  [749] No  No  No  No  No  No  No  Yes Yes No  No  No  No  No  No  Yes No 
##  [766] No  No  No  No  No  No  No  No  No  Yes No  Yes No  No  No  No  No 
##  [783] No  No  Yes No  No  No  No  No  Yes No  No  No  No  No  No  No  No 
##  [800] No  No  No  No  No  Yes No  No  Yes Yes No  No  No  No  No  No  No 
##  [817] No  No  No  Yes No  No  No  No  Yes No  No  No  No  No  Yes No  No 
##  [834] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [851] No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No 
##  [868] No  No  Yes No  No  No  No  No  No  Yes No  No  No  No  No  No  No 
##  [885] No  No  No  No  No  No  Yes No  No  No  No  No  No  Yes No  No  No 
##  [902] Yes No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No 
##  [919] No  No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No 
##  [936] No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No 
##  [953] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
##  [970] No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No 
##  [987] Yes No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1004] No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No 
## [1021] No  No  No  No  Yes No  No  Yes No  No  No  No  No  No  No  Yes No 
## [1038] No  No  No  No  No  No  No  No  Yes Yes No  No  No  No  No  No  No 
## [1055] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1072] No  No  No  No  No  No  No  No  No  No  No  Yes Yes No  No  No  No 
## [1089] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1106] No  No  No  Yes No  No  No  No  No  No  No  No  Yes No  No  No  No 
## [1123] No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No 
## [1140] No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  Yes No 
## [1157] No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1174] No  No  No  No  No  No  No  No  No  Yes No  Yes Yes No  No  No  No 
## [1191] No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1208] No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1225] No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No 
## [1242] No  No  Yes No  No  No  No  No  No  No  No  No  No  No  Yes No  No 
## [1259] No  Yes No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No 
## [1276] No  No  No  No  Yes No  No  No  No  Yes Yes Yes No  No  No  Yes No 
## [1293] No  No  No  No  No  No  Yes No  No  No  No  Yes No  No  No  Yes No 
## [1310] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1327] No  No  No  Yes No  No  No  No  No  No  Yes No  No  Yes No  No  No 
## [1344] No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No 
## [1361] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1378] No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No 
## [1395] No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No 
## [1412] No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No  Yes
## [1429] No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No 
## [1446] No  No  No  No  Yes Yes No  No  No  No  Yes No  Yes Yes No  Yes No 
## [1463] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  Yes
## [1480] No  No  No  No  No  Yes No  No  No  No  No  No  No  No  Yes No  No 
## [1497] No  Yes No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1514] Yes No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No 
## [1531] No  No  No  Yes No  No  No  Yes No  No  No  No  No  Yes No  No  No 
## [1548] No  No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No 
## [1565] No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No 
## [1582] No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No 
## [1599] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1616] Yes No  No  No  No  No  No  No  No  No  No  No  No  Yes Yes No  No 
## [1633] No  Yes No  No  No  No  No  No  No  No  No  No  No  No  Yes Yes No 
## [1650] No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No 
## [1667] Yes No  No  Yes No  No  No  No  No  No  No  No  Yes No  No  No  No 
## [1684] No  No  Yes No  No  Yes No  No  No  No  No  No  No  No  No  No  No 
## [1701] No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No  No 
## [1718] No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No 
## [1735] No  No  No  No  No  No  No  Yes No  No  No  No  No  Yes No  No  No 
## [1752] No  No  No  No  Yes No  No  No  No  Yes No  No  No  No  No  No  Yes
## [1769] No  No  No  Yes No  No  No  No  No  No  No  Yes Yes No  No  No  No 
## [1786] No  No  No  No  No  No  No  No  No  No  No  Yes No  Yes No  Yes No 
## [1803] Yes No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  Yes
## [1820] No  No  No  No  No  No  Yes No  No  No  No  No  No  No  Yes No  No 
## [1837] No  No  No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No 
## [1854] No  No  No  No  Yes Yes Yes No  No  No  No  No  No  Yes No  No  No 
## [1871] Yes Yes No  No  No  No  No  No  No  No  No  Yes No  No  No  No  No 
## [1888] No  No  No  No  No  No  No  No  No  Yes No  No  No  Yes No  No  No 
## [1905] No  No  No  No  No  No  No  No  No  No  Yes Yes No  No  No  No  Yes
## [1922] No  No  Yes No  No  No  No  No  No  No  No  No  No  No  Yes No  No 
## [1939] No  No  No  Yes No  No  No  No  No  No  Yes No  No  Yes No  No  Yes
## [1956] No  Yes No  No  No  No  No  No  No  No  No  No  No  Yes No  No  No 
## [1973] No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No 
## [1990] No  No  No  No  No  No  No  No  No  No  No  No  No  Yes Yes No  No 
## [2007] No  No  No  No  Yes Yes No  No  No  No  No  No  Yes No  No  No  No 
## [2024] No  No  No  No  No  No  No  No  Yes No  No  No  No  No  No  No  No 
## [2041] No  No  No  No  Yes No  No  No  No  Yes No  No  No  No  Yes No  No 
## [2058] No  No  No  No  No  Yes No  No  No  No  No  No  No  No  No  No  No 
## [2075] No  No  No  No  No  No 
## Levels: No Yes

For Column country - it is already a factor variable

countries = store.df$country
is.factor(countries)

## [1] TRUE

2c. Write R code to verify the conversions done in Q2b.

is.factor(years.f)

## [1] TRUE

is.factor(p1proms.f)

## [1] TRUE

is.factor(p2proms.f)

## [1] TRUE

3a. Generate the summary statistics of the different variables present in the data frame store.df.

summary(store.df)

##     storeNum          Year          Week          p1sales   
##  Min.   :101.0   Min.   :1.0   Min.   : 1.00   Min.   : 73  
##  1st Qu.:105.8   1st Qu.:1.0   1st Qu.:13.75   1st Qu.:113  
##  Median :110.5   Median :1.5   Median :26.50   Median :129  
##  Mean   :110.5   Mean   :1.5   Mean   :26.50   Mean   :133  
##  3rd Qu.:115.2   3rd Qu.:2.0   3rd Qu.:39.25   3rd Qu.:150  
##  Max.   :120.0   Max.   :2.0   Max.   :52.00   Max.   :263  
##                                                             
##     p2sales         p1price         p2price         p1prom   
##  Min.   : 51.0   Min.   :2.190   Min.   :2.29   Min.   :0.0  
##  1st Qu.: 84.0   1st Qu.:2.290   1st Qu.:2.49   1st Qu.:0.0  
##  Median : 96.0   Median :2.490   Median :2.59   Median :0.0  
##  Mean   :100.2   Mean   :2.544   Mean   :2.70   Mean   :0.1  
##  3rd Qu.:113.0   3rd Qu.:2.790   3rd Qu.:2.99   3rd Qu.:0.0  
##  Max.   :225.0   Max.   :2.990   Max.   :3.19   Max.   :1.0  
##                                                              
##      p2prom       country 
##  Min.   :0.0000   AU:104  
##  1st Qu.:0.0000   BR:208  
##  Median :0.0000   CN:208  
##  Mean   :0.1385   DE:520  
##  3rd Qu.:0.0000   GB:312  
##  Max.   :1.0000   JP:416  
##                   US:312

3b (Bonus). 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   0.10  0.30   0.00    0.00  0.00   0.00   1.00   1.0
## p2prom      9 2080   0.14  0.35   0.00    0.05  0.00   0.00   1.00   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

4a. Break-up store.df by country.

table <- with(store.df, table(country))
table

## country
##  AU  BR  CN  DE  GB  JP  US 
## 104 208 208 520 312 416 312

4b. Express Q4a numbers in terms of percentages.

prop.table(table)

## country
##   AU   BR   CN   DE   GB   JP   US 
## 0.05 0.10 0.10 0.25 0.15 0.20 0.15

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,

When both products were under promotion?
When product 1 (Coke) was under promotion but product 2 (Pepsi) was not under promotion?
When product 1 (Coke) was not under promotion but product 2 (Pepsi) was under promotion?
When neither product was under promotion?

mytable <- xtabs(~ p1prom+p2prom, data=store.df)
addmargins(mytable)

##       p2prom
## p1prom    0    1  Sum
##    0   1616  256 1872
##    1    176   32  208
##    Sum 1792  288 2080

p1prom =0 implies no promotion on product 1; p1prom =1 implies promotion on product 1; p2prom =0 implies no promotion on product 2; p2prom =1 implies promotion on product 2.

5b. Express Q5a as in percentages.

mytable <- xtabs(~ p1prom+p2prom, data=store.df)
addmargins(prop.table(mytable))

##       p2prom
## p1prom          0          1        Sum
##    0   0.77692308 0.12307692 0.90000000
##    1   0.08461538 0.01538462 0.10000000
##    Sum 0.86153846 0.13846154 1.00000000

5c. Write R code to calculate the average price of product 1 (Coke) and product 2 (Pepsi) when both were under promotion.

promotion <- store.df[store.df$p1prom == 1 & store.df$p2prom == 1,]
Avgprice1 <- mean(promotion$p1price)
Avgprice2 <- mean(promotion$p2price)
Avgprice1

## [1] 2.568125

Avgprice2

## [1] 2.67125

5d. Write R code to calculate the average price of product 1 (Coke) and product 2 (Pepsi) when neither of them was under promotion.

promotion <- store.df[store.df$p1prom == 0 & store.df$p2prom == 0,]
Avgprice1 <- mean(promotion$p1price)
Avgprice2 <- mean(promotion$p2price)
Avgprice1

## [1] 2.543342

Avgprice2

## [1] 2.700891

5e. 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 product1 is under promotion

promotion <- store.df[store.df$p1prom == 1 & store.df$p2prom == 0,]
Avgprice1 <- mean(promotion$p1price)
Avgprice2 <- mean(promotion$p2price)
Avgprice1

## [1] 2.524659

Avgprice2

## [1] 2.703068

when product2 is under promotion

promotion <- store.df[store.df$p1prom == 0 & store.df$p2prom == 1,]
Avgprice1 <- mean(promotion$p1price)
Avgprice2 <- mean(promotion$p2price)
Avgprice1

## [1] 2.561484

Avgprice2

## [1] 2.691953

Question 6

6a. Total sales of product 1 (Coke) and product 2 (Pepsi) when both were under promotion.

promotion <- store.df[store.df$p1prom == 1 & store.df$p2prom == 1,]
Totalsales1 <- sum(promotion$p1sales)
Totalsales2 <- sum(promotion$p2sales)
Totalsales1

## [1] 5323

Totalsales2

## [1] 4272

6b. Total sales of product 1 (Coke) and product 2 (Pepsi) when neither were under promotion.

promotion <- store.df[store.df$p1prom == 0 & store.df$p2prom == 0,]
Totalsales1 <- sum(promotion$p1sales)
Totalsales2 <- sum(promotion$p2sales)
Totalsales1

## [1] 208889

Totalsales2

## [1] 153311

6c. Total sales of product 1 (Coke) and product 2 (Pepsi) when product1 (coke) was under promotion.

promotion <- store.df[store.df$p1prom == 1 & store.df$p2prom == 0,]
Totalsales1 <- sum(promotion$p1sales)
Totalsales2 <- sum(promotion$p2sales)
Totalsales1

## [1] 29806

Totalsales2

## [1] 16348

6c. Total sales of product 1 (Coke) and product 2 (Pepsi) when product2 (pepsi) was under promotion.

promotion <- store.df[store.df$p1prom == 0 & store.df$p2prom == 1,]
Totalsales1 <- sum(promotion$p1sales)
Totalsales2 <- sum(promotion$p2sales)
Totalsales1

## [1] 32723

Totalsales2

## [1] 34395

StoreDataAnalysis

K Chandirasekaran

15 September 2018