Data Visualization of a Pharmaceutical Company

The objective is to understand the sales trends for one of the leading pharmaceutical company. The company want to explore the past data in a better mannerand give a good picture of the progress and the faliure.

Import the required libraries

dplyr, tidyr, ggplot2, plotrix, magrittr

Import the Data

sales = read.csv("D:\\Business Analytics\\R\\CLASS - 6\\R case study 3 (Visualization)\\SalesData.csv")

1 - Comparison of Sales by Region for 2016 and 2015

options(scipen = 999)
s1 = sales%>%
      group_by(Region)%>%
        summarise(TotalSales2015 = sum(Sales2015),TotalSales2016 = sum(Sales2016))

data_long = gather(s1,key = Year, value = Sales,-Region )
data_long$Sales = round(data_long$Sales,1)

ggplot(data_long,aes(Region,Sales, fill = Year,label = Sales)) + geom_bar(stat = "identity",position = "dodge") + 
  geom_text(size = 4) + xlab('Region') +ylab('Sales') + ggtitle("Comparision of Sales by Region")

From the above bar chart we can observe that the sales in 2016 is more than 2015 across all the regions and East region has the higest sales.

2 - Sales for each Region in 2016

s2 = sales%>%
     group_by(Region)%>%
     summarise(TotalSales2016 = sum(Sales2016))

piepercent = round(s2$TotalSales2016/sum(s2$TotalSales2016)*100,1)
lbls= s2$Region
lbls = paste(lbls,":",piepercent)
lbls = paste(lbls,"%",sep = "")

pie(s2$TotalSales2016,labels = lbls , col = c("lightskyblue","royalblue3","turquoise4"),main = "2D Pie Chart of Sales 2016",radius = 1,border = "black")

pie3D(s2$TotalSales2016,labels = lbls ,explode = 0.1, col = c("lightskyblue","royalblue3","turquoise4")) + title('3D Pie Chart of Sales 2016')

## numeric(0)

From the above pie chart we can see that East region has the largest contributes in sales where as West contributes the lowest

3 - Sales comparision of 2015 and 2016 by region and tiers

s3 = sales%>%
     group_by(Region,Tier)%>%
     summarise(TotalSales2015 = sum(Sales2015),TotalSales2016 = sum(Sales2016))

data_long1 = gather(s3,key = Year,value = Sales,-c(Region,Tier))
ggplot(data_long1,aes(Tier,Sales,fill = Year)) + geom_bar(stat = "identity", position = "dodge") + facet_wrap(~ Region) +ggtitle("Comparision of Sales by Region and Tiers")

From the above bar chat we can conclude that the high tier contributes largest across all regions.

4 - In East region, which state registered a decline in 2016 as compared to 2015

s4 = sales%>%
     group_by(State)%>%
     filter(Region =="East")%>%
     summarise(TotalSales2015 = sum(Sales2015),TotalSales2016 = sum(Sales2016))
     
data_long2 = gather(s4,key = Year, value = Sales,-State)
ggplot(data_long2,aes(State,Sales,fill = Year)) + geom_bar(stat = "identity",position = "dodge") +ggtitle("Comparision of Sales by State")

From the above chart we can observe that NY is the only state whose sale dip in 2016 as compared to 2015

5 - In all the high tier, which division saw a decline in the number of unit sold in 2016 as compared to 2015

s5 = sales%>%
     filter(Tier == "High")%>%
     group_by(Division)%>%
     summarise(TotalUnit2015 = sum(Units2015),TotalUnit2016 = sum(Units2016))

data_long3 = gather(s5,key = Year, value = Sales,-Division)
gg1 = ggplot(data_long3,aes(Division,Sales, fill = Year)) + geom_bar(stat = "identity",position = "dodge")
gg1 + theme(axis.text.x = element_text(angle = 90)) + ggtitle("Comparision of Sales by Division")

6 - Compare quarter wise sale in 2015 and 2016 in a bar plot

sales$Qtr = if_else(sales$Month == "Jan"|sales$Month == "Feb"|sales$Month == "Mar","Q1",
              if_else(sales$Month == "Apr"|sales$Month == "May"|sales$Month == "Jun","Q2",
                if_else(sales$Month == "Jul"|sales$Month == "Aug"|sales$Month == "Sep","Q3","Q4")))


s7 = sales%>%
     group_by(Qtr)%>%
     summarise(TotalSum2015 = sum(Sales2015),TotalSum2016 = sum(Sales2016))

data_long4 = gather(s7,key = Year, value = Sales,-Qtr)
ggplot(data_long4,aes(Qtr,Sales,fill = Year)) + geom_bar(stat = "identity",position = "dodge") + ggtitle("Comparision of Sales by Quarter")

7 - Determine the composition of quarter wise sale in 2015 and 2016

s8_q1 = sales%>%group_by(Qtr,Tier)%>%filter(Qtr=="Q1")%>%summarise(TotalSales2015 = sum(Sales2015))
s8_q2 = sales%>%group_by(Qtr,Tier)%>%filter(Qtr=="Q2")%>%summarise(TotalSales2015 = sum(Sales2015))
s8_q3 = sales%>%group_by(Qtr,Tier)%>%filter(Qtr=="Q3")%>%summarise(TotalSales2015 = sum(Sales2015))
s8_q4 = sales%>%group_by(Qtr,Tier)%>%filter(Qtr=="Q4")%>%summarise(TotalSales2015 = sum(Sales2015))

piepercent1 = round(s8_q1$TotalSales2015/sum(s8_q1$TotalSales2015)*100,1)
piepercent2 = round(s8_q2$TotalSales2015/sum(s8_q2$TotalSales2015)*100,1)
piepercent3 = round(s8_q3$TotalSales2015/sum(s8_q3$TotalSales2015)*100,1)
piepercent4 = round(s8_q4$TotalSales2015/sum(s8_q4$TotalSales2015)*100,1)


lbls1 = s8_q1$Tier%>%paste(":",piepercent1)%>%paste("%",sep = "")         
lbls2 = s8_q2$Tier%>%paste(":",piepercent2)%>%paste("%",sep = "")         
lbls3 = s8_q3$Tier%>%paste(":",piepercent3)%>%paste("%",sep = "")         
lbls4 = s8_q4$Tier%>%paste(":",piepercent4)%>%paste("%",sep = "")         

par(mfrow = c(2,2)) 
pie(s8_q1$TotalSales2015,labels = lbls2 ,col = c("lightskyblue","royalblue3","turquoise4"),radius = 1,main = "Qtr 1")
pie(s8_q2$TotalSales2015,labels = lbls2 ,col = c("lightskyblue","royalblue3","turquoise4"),radius = 1,main = "Qtr 2")
pie(s8_q3$TotalSales2015,labels = lbls3 ,col = c("lightskyblue","royalblue3","turquoise4"),radius = 1,main = "Qtr 3")
pie(s8_q4$TotalSales2015,labels = lbls4 ,col = c("lightskyblue","royalblue3","turquoise4"),radius = 1,main = "Qtr 4")