bussiness analytics assignment

Below is the analytic data for Reliance Industries.

source:https://www.moneyworks4me.com/indianstocks/large-cap/oil-and-gas/refineries/reliance-industries/company-info#

vector: 
year<-c(2014,2016,2017,2018,2019)
Netsales<-c(434460,375435,273999,305382,391677)
Bookvaluepershare<-c(336.41,369.77,391.27,444.13,495.59)
Netopsalesflow<-c(43261,34374,38134,49550,71459)

Dataframe:

df<-data.frame(year,Netsales,Bookvaluepershare,Netopsalesflow)

df
result  
   year   Net sales  Bookvaluepershare  Netopsalesflow

1 2014   434460            336.41                  43261

2 2016   375435            369.77                 34374

3 2017   273999            391.27                 38134

4 2018   305382            444.13                 49550

5 2019   391677            495.59                 71459

MATRIX:

matrix<-cbind(c(year),c(Netsales),c(Bookvaluepershare),c(Netopsalesflow))

matrix

result

> matrix

     [,1]   [,2]   [,3]  [,4]

[1,] 2014 434460 336.41 43261

[2,] 2016 375435 369.77 34374

[3,] 2017 273999 391.27 38134

[4,] 2018 305382 444.13 49550

[5,] 2019 391677 495.59 71459

ARRAY:

>row.names<-c("row1","row2","row3","row4","row5")

>column.names<-c("col1","col2","col3","col4")

>matrix.names<-c("matrix1")

>result <- array(c(year,Netsales,Bookvaluepershare,Netopsalesflow),dim = c(5,4,1),dimnames =          list(row.names,column.names, matrix.names))

>result

>, , matrix1

          col1   col2       col3  col4

row1 2014 434460 336.41 43261

row2 2016 375435 369.77 34374

row3 2017 273999 391.27 38134

row4 2018 305382 444.13 49550

row5 2019 391677 495.59 71459

GGPLOT:

ggplot(data=df,aes(x=year,y=Netsales))+geom_line(colour="green")