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##Reading data into R
library(readxl)
store<-read_excel("d:/Superstore.xlsx")
View(store)

Including Plots

Extracting year from given data and adding a column in data

displaying barplot

  1. Genrate a barplot to display Yearwise sales count
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
salescount<-store %>% group_by(year) %>% summarise(count=sum(Sales))
library(ggplot2)
ggplot(salescount,aes(x=year))+geom_bar(aes(fill=count))

## histogram plot 2. Generate a histogram to display total order quantity and thier frequenies.

hist(store$`Order Quantity`)

## summarised table

summarized table to display year wise sales with order prioirty.

z<-store %>% group_by(year,`Order Priority`) %>% summarise(total_sales=sum(Sales))
View(z)

summarised table of profits

summarized table and display order priority wise profits.

p<-store %>% group_by(`Order Priority`) %>% summarise(profits=sum(Profit))
View(p)

picking 2 highest values of profits in order priority

prof<-store %>% group_by(`Order Priority`) %>% top_n(2,Profit) %>% select(`Order Priority`,Profit)
View(prof)

Display barplot

barplot to display ship mode totals year wise.

ggplot(store,aes(x=year))+geom_bar(aes(fill=`Ship Mode`))

summarised table

To display year wise ship mode count

shipmode<-store %>% group_by(year,`Ship Mode`) %>% summarise(count=n())
View(shipmode)

discount and profits with five number summary

fivenum(store$Profit)
## [1] -14140.702    -83.315     -1.500    162.748  27220.690
fivenum(store$Discount)
## [1] 0.00 0.02 0.05 0.08 0.25

if unit price and discount have left skewed or right skewed distrbution

summary(store$`Unit Price`)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.99    6.48   20.99   89.35   85.99 6783.02
summary(store$Discount)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.02000 0.05000 0.04967 0.08000 0.25000

conclusion

In summary of unit prices mean > median ,we can conclude that unit prices follows right skewed distribution. In summary of Discount mean < median ,we can conclude that Discount follows left skewed distribution.

Which prodcuts are more profitable?

profitable<-store %>% group_by(`Product Category`) %>% summarise(total_profit=sum(Profit))
View(profitable)

conclusion

From summarised table we can get the maximum of profits and that is TECHNOLOGIES. Technology product category products are earning maximum profits out of all.

What is the average shipping cost the company is spending on through different ship modes?

avg_shippingcost<-store %>% group_by(`Ship Mode`) %>% summarise(avg_cost=mean(`Shipping Cost`))
View(avg_shippingcost)

conclusion

From above table, we can say average shipping cost of delivery truck is more than other shipping modes.It spends 45 rs on an average for one product shipment.

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.