Biocon Competitor Analysis

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library(readxl)
library(ggplot2)
competitors <- read_excel("C:/My Work/Data Analytics/Data sets/Biocom_Comp_Ana.xlsx")
names(competitors)<-c("company_name","year","sales","sales_per_change","opm","net_profit",
                  "net_profit_per_change","npm","eps","ceps")
head(competitors)

 ggplot(competitors, aes(x=competitors$sales, y=competitors$company_name)) + geom_boxplot()

competitors<-na.omit(competitors)
competitors$sales_per_change<- substr(competitors$sales_per_change, start = 1, stop = 4)
class(competitors$sales_per_change)<-"numeric"
summary(competitors)

 company_name            year          sales          sales_per_change       opm         
 Length:193         Min.   :2013   Min.   :   14.37   Min.   :-9.8000   Min.   :-0.1143  
 Class :character   1st Qu.:2014   1st Qu.:  273.26   1st Qu.: 0.1300   1st Qu.: 0.1073  
 Mode  :character   Median :2014   Median : 1003.76   Median : 0.2100   Median : 0.1395  
                    Mean   :2015   Mean   : 2702.35   Mean   : 0.9921   Mean   : 0.1506  
                    3rd Qu.:2015   3rd Qu.: 2862.19   3rd Qu.: 0.5400   3rd Qu.: 0.1862  
                    Max.   :2016   Max.   :28487.03   Max.   :12.4000   Max.   : 0.4220  
   net_profit      net_profit_per_change      npm              eps              ceps       
 Min.   :   0.24   Min.   : -0.9896      Min.   :0.0008   Min.   : -4.95   Min.   :-20.19  
 1st Qu.:  15.93   1st Qu.: -0.0325      1st Qu.:0.0508   1st Qu.:  5.34   1st Qu.: 10.14  
 Median :  77.25   Median :  0.2113      Median :0.0905   Median : 14.21   Median : 22.48  
 Mean   : 397.94   Mean   :  0.9805      Mean   :0.1030   Mean   : 22.33   Mean   : 31.10  
 3rd Qu.: 376.15   3rd Qu.:  0.5264      3rd Qu.:0.1443   3rd Qu.: 32.16   3rd Qu.: 44.71  
 Max.   :5656.86   Max.   :100.8496      Max.   :0.3092   Max.   :145.61   Max.   :180.58  

data<-data.frame(aggregate(. ~ year, data=competitors[competitors$company_name!='Biocon',][,2:10], FUN=sum))

data[data$year=='2013',][,2:9]<-data[data$year=='2013',][,2:9]/(nrow(competitors[competitors$year=='2013',]))

data[data$year=='2014',][,2:9]<-data[data$year=='2014',][,2:9]/(nrow(competitors[competitors$year=='2014',]))
data[data$year=='2015',][,2:9]<-data[data$year=='2015',][,2:9]/(nrow(competitors[competitors$year=='2015',]))
data[data$year=='2016',][,2:9]<-data[data$year=='2016',][,2:9]/(nrow(competitors[competitors$year=='2016',]))

data<-cbind('TOTAL',data)
names(data)[1]<-"company_name"
data<-rbind(data
,competitors[competitors$company_name=="Biocon",])

data

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