Project On Financial Statements Analysis Of TVS Motors(2020 - 2024)

#R codes ##Data Loading

library(readxl)

## Warning: package 'readxl' was built under R version 4.4.3

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.4.3

library(gridExtra)

## Warning: package 'gridExtra' was built under R version 4.4.3

Tvs_bs <- read_excel("E:/R studio/tvsbs.xlsx")
Tvs_bs<-as.data.frame(Tvs_bs)
colnames(Tvs_bs) <- make.names(colnames(Tvs_bs))
colnames(Tvs_bs) <- trimws(colnames(Tvs_bs))
Tvs_bs_long <- reshape(Tvs_bs, 
                       varying = c("X2024", "X2023", "X2022", "X2021", "X2020"), 
                       v.names = "Amount", 
                       timevar = "Year",
                       times = c("2024","2023","2022","2021", "2020"),
                       direction = "long")
rownames(Tvs_bs_long) <- NULL

Balance Sheet Analysis of TVS Motors

The following graphs shows the Total Reserves,Total Current Liabilities,Total Current Assets,Cash And Cash Equivalents,Trade Receivables & Trade Payables trends of TVS Motors through out the years of 2020-2024

#codes by SHRAVAN SHANKAR.J
#Total Reserves and Surplus plot
b1_tvs=as.data.frame(Tvs_bs_long[Tvs_bs_long$Items=="TotalReservesandSurplus",])

p1_Tvs=ggplot(b1_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  labs(x="Year",y="Amount in Crores",
       title="Total Reserves and Surplus",
       caption = "B/S DATA",
       subtitle="TVS Motors (2020 - 2024)")+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  scale_y_continuous(
    limits = c(3000, 8000),  # Set y-axis limits
    breaks = seq(3000, 8000, by = 1000),  # Set the breaks on the y-axis
    labels = seq(30, 80, by = 10))  # Label the y-axis with values from 30 to 80
grid.arrange(p1_Tvs)

#codes by SHRAVAN SHANKAR.J
#Total Current Liabilities plot
b2_tvs=as.data.frame(Tvs_bs_long[Tvs_bs_long$Items == "TotalCurrentLiabilities",])


p2_Tvs=ggplot(b2_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  labs(x="Year",y="Amount in Crores",
       title="Total Current Liabilities",
       caption = "B/S DATA",
       subtitle="TVS Motors (2020 - 2024)")+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  scale_y_continuous(
    limits = c(4000, 8000),  # Set y-axis limits
    breaks = seq(4000, 8000, by = 1000),  # Set the breaks on the y-axis
    labels = seq(40, 80, by = 10))  # Label the y-axis with values from 40 to 80
grid.arrange(p2_Tvs)

#codes by SHRAVAN SHANKAR.J
#Total Current Assets plot
b3_tvs=as.data.frame(Tvs_bs_long[Tvs_bs_long$Items == "TotalCurrentAssets",])

p3_Tvs=ggplot(b3_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=3)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  labs(x="Year",y="Amount in Crores",
       title="Total Current Assets",
       subtitle="TVS Motors(2020 - 2024)",
       caption = "B/S DATA")+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  scale_y_continuous(
    limits = c(3000, 5000),  # Set y-axis limits
    breaks = seq(3000, 5000, by = 500),  # Set the breaks on the y-axis
    labels = seq(30, 50, by = 5))

grid.arrange(p3_Tvs)

#codes by SHRAVAN SHANKAR.J
#Cash And Cash Equivalents plot
b4_tvs=as.data.frame(Tvs_bs_long[Tvs_bs_long$Items == "CashAndCashEquivalents",])

p4_Tvs=ggplot(b4_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  labs(x="Year",y="Amount in Crores",
       title="Cash And CashEquivalents",
       subtitle="TVS Motors (2020 - 2024)",
       caption = "B/S DATA")+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  scale_y_continuous(
    limits = c(200, 1000),  # Set y-axis limits
    breaks = seq(200, 1000, by = 200),  # Set the breaks on the y-axis
    labels = seq(2, 10, by = 2))
grid.arrange(p4_Tvs)

#codes by SHRAVAN SHANKAR.J
#Trade Receivables plot
b5_tvs=as.data.frame(Tvs_bs_long[Tvs_bs_long$Items == "TradeReceivables",])

p5_Tvs=ggplot(b5_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  labs(x="Year",y="Amount in crores",
       title="Trade Receivables",
       subtitle="TVS Motors (2020 - 2024)",
       caption = "B/S DATA")+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  scale_y_continuous(
    limits = c(500, 1500),  # Set y-axis limits
    breaks = seq(500, 1500, by = 500),  # Set the breaks on the y-axis
    labels = seq(5, 15, by = 5))  
grid.arrange(p5_Tvs)

#codes by SHRAVAN SHANKAR.J
#Trade Payables plot
b6_tvs=as.data.frame(Tvs_bs_long[Tvs_bs_long$Items == "TradePayables",])             

p6_Tvs=ggplot(b6_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  labs(x="Year",y="Amount in Crores",
       title="Trade Payables",
       subtitle="TVS Motors (2020 - 2024)",
       caption = "B/S DATA")+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  scale_y_continuous(
    limits = c(2500, 5500),  # Set y-axis limits
    breaks = seq(2500, 5500, by = 500),  # Set the breaks on the y-axis
    labels = seq(25, 55, by = 5))
grid.arrange(p6_Tvs)

Profit & Loss statement Analysis Of TVS Motors (2020 - 2024)

The Following Graphs Represents the trends of important p&l values of TVS Motors for the the years 2020-2024

#codes by GANESH
#Profit or Loss for the Period plot
tvs_pl=read_excel("E:/R studio/tvspl.xlsx")
tvs_pl<-as.data.frame(tvs_pl)
# Clean column names (if needed)
colnames(tvs_pl) <- make.names(colnames(tvs_pl))
colnames(tvs_pl) <- trimws(colnames(tvs_pl))
#-----------------------------------

tvspllong <- reshape(tvs_pl, 
                       varying = c("X2024", "X2023", "X2022", "X2021", "X2020"), 
                       v.names = "Amount", 
                       timevar = "Year",
                       times = c("2024","2023","2022","2021", "2020"),
                       direction = "long")

b1_tvs=as.data.frame(tvspllong[tvspllong$Items=="Profit_orLossForThePeriod",])
p1_tvspl=ggplot(b1_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
   labs(x="Year",y="Amount in Crores",
       title="Profit or loss for the period",
       caption = "P&L DATA",
       subtitle="TVS (2020 - 2024)")+
  scale_y_continuous(
    limits = c(500, 2500),  
    breaks = seq(500, 2500, by = 500),  
    labels = seq(50, 250, by = 50))
   print(p1_tvspl)

#codes by GANESH
#Revenue  from operations (Gross) plot
b2_tvs=as.data.frame(tvspllong[tvspllong$Items == "RevenueFromOperations_Gross",])

p2_tvspl=ggplot(b2_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
     labs(x="Year",y="Amount in Crores",
       title="Revenue from operations (Gross)",
       caption = "P&L DATA",
       subtitle="TVS (2020 - 2024)")+
  scale_y_continuous(
    limits = c(15000, 35000),  # Set y-axis limits
    breaks = seq(15000, 35000, by = 5000),  # Set the breaks on the y-axis
    labels = seq(15, 35, by = 5))  # Label the y-axis with values from 50 to 80
     print (p2_tvspl)

#codes by GANESH
# EBT plot
b3_tvs=as.data.frame(tvspllong[tvspllong$Items == "Profit_orLossBeforeTax",])

p3_tvspl=ggplot(b3_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
  labs(x="Year",y="Amount in Crores",
       title="EBT (Earnings Before Tax)",
       subtitle="TVS (2020 - 2024)",
       caption = "P&L DATA")+
  scale_y_continuous(
    limits = c(750, 3100),  # Set y-axis limits
    breaks = seq(700, 3100, by = 300 ),  # Set the breaks on the y-axis
    labels = seq(70, 310, by = 30 ))
print(p1_tvspl)

#codes by GANESH
#Total Revenue plot
b4_tvs=as.data.frame(tvspllong[tvspllong$Items == "TotalRevenue",])

p4_tvspl=ggplot(b4_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
labs(x="Year",y="Amount in Crores",
       title="TotalRevenue",
       subtitle="TVS (2020 - 2024)",
       caption = "P&L DATA")+
  scale_y_continuous(
    limits = c(15000, 35000),  # Set y-axis limits
    breaks = seq(15000, 35000, by = 5000 ),  # Set the breaks on the y-axis
    labels = seq(150, 350, by = 50 ))
print(p4_tvspl)  

#codes by GANESH
# Other Income plot
b5_tvs=as.data.frame(tvspllong[tvspllong$Items == "OtherIncome",])

p5_tvspl=ggplot(b5_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=2),
            col="green",
            linewidth=2)+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
labs(x="Year",y="Amount in Crores",
       title="Other Income",
       subtitle="TVS (2020 - 2024)",
       caption = "P&L DATA")+
  scale_y_continuous(
    limits = c(15, 150),  # Set y-axis limits
    breaks = seq(15, 150, by = 15 ),  # Set the breaks on the y-axis
    labels = seq(15, 150, by = 15 ))
  print(p5_tvspl)

#codes by GANESH
#Other operating revenues
  b6_tvs=as.data.frame(tvspllong[tvspllong$Items == "OtherOperatingRevenues",])
  
  p6_tvspl=ggplot(b6_tvs,aes(x=Year,y=Amount))+
    geom_point(col="blue",size=4)+
    geom_line(aes(x=Year,Amount, group=2),
              col="green",
              linewidth=2)+
    geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
    theme_minimal() +
    labs(x="Year",y="Amount in Crores",
         title="Other operating Revenues",
         subtitle="TVS (2020 - 2024)",
         caption = "P&L DATA")+
  scale_y_continuous(
    limits = c(100, 400),  # Set y-axis limits
    breaks = seq(100, 400, by = 100 ),  # Set the breaks on the y-axis
    labels = seq(100, 400, by = 100 ))
  print(p6_tvspl)

#codes by GANESH
#Total  expenses
b7_tvs=as.data.frame(tvspllong[tvspllong$Items == "TotalExpenses",])

p7_tvspl=ggplot(b7_tvs,aes(x=Year,y=Amount))+
               geom_point(col="blue",size=4)+
                geom_line(aes(x=Year,Amount, group=1),
                          col= "green",
                          linewidth=2)+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
labs(x="Year",y="Amount in Crores",
 title= "Total Expenses",
subtitle = "TVS (2020-2024)",
caption = "P&L DATA")+
  scale_y_continuous(
    limits = c(15000,30000),
    breaks = seq(15000,30000, by= 5000),
    labels = seq(15000,30000, by = 5000))
print(p7_tvspl)

#codes by GANESH
#Finance Costs
b8_tvs=as.data.frame(tvspllong[tvspllong$Items == "FinanceCosts",])

p8_tvspl=ggplot(b8_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col= "green",
            linewidth=2)+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
 labs(x="Year",y="Amount in Crores",
       title= "finance costs",
       subtitle = "TVS (2020-2024)",
       caption = "P&L DATA")+
  scale_y_continuous(
    limits = c(100,190),
    breaks = seq(100,190, by= 10),
    labels = seq(100,190, by = 10))
print(p8_tvspl)

Cash Flow Statement Analysis Of TVS Motors (2020 - 2024)

The Following Plots Show the Trends of Important Items Of cash Flow Statement Of TVS Motors For The Years of 2020 - 2024

# codes by GIRISH.M
#Net Profit Before Tax PLOT
tvs_cf=read_excel("E:/R studio/tvscf.xlsx")
tvs_cf<-as.data.frame(tvs_cf)
colnames(tvs_cf) <- make.names(colnames(tvs_cf))
colnames(tvs_cf) <- trimws(colnames(tvs_cf))
tvs_cf_long <- reshape(tvs_cf, 
                     varying = c("X2020","X2021","X2022","X2023","X2024"), 
                     v.names = "Amount", 
                     timevar = "Year",
                     times = c("2020","2021","2022","2023", "2024"),
                     direction = "long")
rownames(tvs_cf_long) <- NULL

b1_tvs=as.data.frame(tvs_cf_long[tvs_cf_long$Items=="NetProfitBeforeTax",])

p1_tvscf=ggplot(b1_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
  labs(x="Year",y="Amount in crores",
       title="Net Profit Before Tax",
       caption = "C/F DATA",
       subtitle="TVS Motors (2020 - 2024)")+
  scale_y_continuous(
    limits = c(500, 3000),  # Set y-axis limits
    breaks = seq(500, 3000, by = 500),  # Set the breaks on the y-axis
    labels = seq(500, 3000, by = 500))  # Label the y-axis with values 
print(p1_tvscf)

#codes by GIRISH.M
#Net Cash From Operating Activities PLOT

b2_tvs=as.data.frame(tvs_cf_long[tvs_cf_long$Items == "NetCashFromOperatingActivities",])

p2_tvscf=ggplot(b2_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
  labs(x="Year",y="Amount in Crores",
       title="Net Cash From Operating Activities",
       subtitle="TVS Motors (2020 - 2024)",
       caption = "C/F DATA")+
  scale_y_continuous(
    limits = c(1000, 4000),  # Set y-axis limits
    breaks = seq(1000, 4000, by = 500),  # Set the breaks on the y-axis
    labels = seq(10, 40, by = 5)) 
print(p2_tvscf)

#Codes by GIRISH.M
#Net Dec on In Cash and Cash Equivalents

b3_tvs=as.data.frame(tvs_cf_long[tvs_cf_long$Items == "NetDecreaseOrIncreaseInCashandCashEquivalents",])

p3_tvscf=ggplot(b3_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
  labs(x="Year",y="Amount crores",
       title="Net Dec Or Inc in Cash and Cash Equivalents",
       subtitle="TVS Motors (2020 - 2024)",
       caption = "C/F DATA")+
  scale_y_continuous(
    limits = c(-600, 500),  # Set y-axis limits
    breaks = seq(-600, 500, by = 100),  # Set the breaks on the y-axis
    labels = seq(-60, 50, by = 10))
print(p3_tvscf)

#codes by GIRISH.M
#Opening Cash & Cash Equivalents

b4_tvs=as.data.frame(tvs_cf_long[tvs_cf_long$Items == "OpeningCash&CashEquivalents",])

p4_tvscf=ggplot(b4_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
   geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
  labs(x="Year",y="Amount in crores",
       title="Opening Cash & Cash Equivalents",
       subtitle="TVS Motors (2020 - 2024)",
       caption = "C/F DATA")+
  scale_y_continuous(
    limits = c(30, 900),  # Set y-axis limits
    breaks = seq(30, 900, by = 30),  # Set the breaks on the y-axis
    labels = seq(3, 90, by = 3)) 

print(p4_tvscf)

#codes by GIRISH.M
#Closing Cash & Cash Equivalents Plot

b5_tvs=as.data.frame(tvs_cf_long[tvs_cf_long$Items == "ClosingCash&CashEquivalents",])

p5_tvscf=ggplot(b5_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  labs(x="Year",y="Amount in crores",
       title="Closing Cash & Cash Equivalents",
       subtitle="TVS Motors (2020 - 2024)",
       caption = "C/F DATA")+
  geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
  scale_y_continuous(
    limits = c(200, 900),  # Set y-axis limits
    breaks = seq(200, 900, by = 50),  # Set the breaks on the y-axis
    labels = seq(20, 90, by = 5))
print(p5_tvscf)

#codes by GIRISH.M
#Net Cash used from Financing Activities plot

b6_tvs=as.data.frame(tvs_cf_long[tvs_cf_long$Items == "NetCashusedin_orfromFinancingActivities",])

p6_tvscf=ggplot(b6_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  labs(x="Year",y="Amount in crores",
       title="Net Cash used in or from Financing Activities",
       subtitle="TVS Motors (2020 - 2024)",
       caption = "C/F DATA")+
   geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
  scale_y_continuous(
    limits = c(-2000, 1000),  # Set y-axis limits
    breaks = seq(-2000, 1000, by = 1000),  # Set the breaks on the y-axis
    labels = seq(-2000, 1000, by = 1000)) 

print(p6_tvscf)

#codes by GIRISH.M
#Net Cash used in or from Plot

b7_tvs=as.data.frame(tvs_cf_long[tvs_cf_long$Items == "NetCashusedin_orfrom",])

p7_tvscf=ggplot(b7_tvs,aes(x=Year,y=Amount))+
  geom_point(col="blue",size=4)+
  geom_line(aes(x=Year,Amount, group=1),
            col="green",
            linewidth=2)+
  labs(x="Year",y="Amount in crores",
       title="Net Cash used in or from",
       subtitle="TVS Motors (2020 - 2024)",
       caption = "C/F DATA")+
   geom_text(aes(label = Amount), vjust = -1, color = "black") +  # Display numbers above points
  theme_minimal() +
  scale_y_continuous(
    limits = c(-2500, -500),  # Set y-axis limits
    breaks = seq(-2500, -500, by = 500),  # Set the breaks on the y-axis
    labels = seq(-2500, -500, by = 500))
print(p7_tvscf)

The Above Plots Show The Trends of Various Items Taken From The Financial Statements Of TVS Motors For The Years of 2020 - 2024 coded in R studios Using (ggplot2).

# codes by GANESH

# Define groups from the items
tvs_cms_grp <- "CostOfMaterialsConsumed"
tvs_rev_grp <- "TotalRevenue"

# Calculate Gross Profit (Revenue - COGS) for each year
tvs_gpr_grp = tapply(tvspllong$Amount[tvspllong$Item %in% tvs_cms_grp], 
                     tvspllong$Year[tvspllong$Item %in% tvs_cms_grp], sum)

tvs_revr_grp = tapply(tvspllong$Amount[tvspllong$Item %in% tvs_rev_grp], 
                      tvspllong$Year[tvspllong$Item %in% tvs_rev_grp], sum)

# Compute Gross Profit Ratio
tvs_gp_ratio <- ((tvs_revr_grp - tvs_gpr_grp) / tvs_revr_grp) * 100

# Create a dataframe with Year and GP Ratio
gpdf=data.frame(
  Year = names(tvs_revr_grp),
  TVS_GP_Ratio = unname(tvs_gp_ratio))


#net profit ratio
tvs_np_grp <- "Profit_orLossForThePeriod"

tvs_rev_grp <- "TotalRevenue"

tvs_npr_grp = tapply(tvspllong$Amount[tvspllong$Item %in% tvs_np_grp], tvspllong$Year[tvspllong$Item %in% tvs_np_grp ], sum)
tvs_revr_grp = tapply(tvspllong$Amount[tvspllong$Item %in% tvs_rev_grp], tvspllong$Year[tvspllong$Item %in% tvs_rev_grp], sum)

tvs_np_ratio <-  (tvs_npr_grp/tvs_revr_grp)*100 

#creating dataframe
npdf=data.frame(Year=names(tvs_revr_grp),
           TVS_NP_Ratio=unname(tvs_np_ratio))

#ROA
# Define groups from the items for ROA
tvs_np_grp <- "Profit_orLossForThePeriod"
tvs_ta_grp= "TotalAssets"


#Finding the sum of items in each group year wise
tvs_npr_grp = tapply(tvspllong$Amount[tvspllong$Item %in% tvs_np_grp], tvspllong$Year[tvspllong$Item %in% tvs_np_grp ], sum)
tvs_tar_grp = tapply(Tvs_bs_long$Amount[Tvs_bs_long$Item %in% tvs_ta_grp], Tvs_bs_long$Year[Tvs_bs_long$Item %in% tvs_ta_grp], sum)

#Finding the ratio of sum in each group - yearwise
tvs_roa_ratio <-  (tvs_npr_grp/tvs_tar_grp)*100 

#creating dataframe
roa_df=data.frame(Year=names(tvs_revr_grp),
           TVS_ROA_Ratio=unname(tvs_roa_ratio))
#ROE
tvs_np_grp <- "Profit_orLossForThePeriod"
tvs_esc_grp="TotalShareholdersFunds"

#Finding the sum of items in each group year wise
tvs_npr_grp = tapply(tvspllong$Amount[tvspllong$Item %in% tvs_np_grp], tvspllong$Year[tvspllong$Item %in% tvs_np_grp ], sum)
tvs_escr_grp = tapply(Tvs_bs_long$Amount[Tvs_bs_long$Item %in% tvs_esc_grp], Tvs_bs_long$Year[Tvs_bs_long$Item %in% tvs_esc_grp], sum)

#Finding the ratio of sum in each group - yearwise
tvs_roe_ratio <-  (tvs_npr_grp/tvs_escr_grp)*100 


#creating dataframe
roe_df=data.frame(Year=names(tvs_revr_grp),
           TVS_ROE_Ratio=unname(tvs_roe_ratio))
           

#--------------------------------------------------------------------------------------------------------------------------
#bajaj and tvs
#Gross profit ratio
tvs_cms_grp <- "CostOfMaterialsConsumed"
tvs_rev_grp <- "TotalRevenue"
bajaj_cms_grp <- "CostOfMaterials_Consumed"
bajaj_rev_grp <- "Total_Revenue"

tvs_gpr_grp = tapply(tvspllong$Amount[tvspllong$Item %in% tvs_cms_grp], 
                     tvspllong$Year[tvspllong$Item %in% tvs_cms_grp], sum)

tvs_revr_grp = tapply(tvspllong$Amount[tvspllong$Item %in% tvs_rev_grp], 
                      tvspllong$Year[tvspllong$Item %in% tvs_rev_grp], sum)

bajaj_gpr_grp = tapply(bajajpllong$Amount[bajajpllong$Items %in% bajaj_cms_grp], 
                     bajajpllong$Year[bajajpllong$Items %in% bajaj_cms_grp], sum)

bajaj_revr_grp = tapply(bajajpllong$Amount[bajajpllong$Items %in% bajaj_rev_grp], 
                      bajajpllong$Year[bajajpllong$Items %in% bajaj_rev_grp], sum)

tvs_gp_ratio <- ((tvs_revr_grp - tvs_gpr_grp) / tvs_revr_grp) * 100
bajaj_gp_ratio <- ((bajaj_revr_grp - bajaj_gpr_grp) / bajaj_revr_grp) * 100

#creating dataframe
gpsdf=data.frame(
  Year = names(tvs_revr_grp),
 TVS_GP_Ratio = unname(tvs_gp_ratio),
 BAJAJ_GP_Ratio = unname(bajaj_gp_ratio))


#Net profit
tvs_np_grp <- "Profit_orLossForThePeriod"
tvs_rev_grp <- "TotalRevenue"
bajaj_np_grp <- "ProfitLossForThePeriod_"
bajaj_rev_grp <- "Total_Revenue"

tvs_npr_grp = tapply(tvspllong$Amount[tvspllong$Item %in% tvs_np_grp], 
                     tvspllong$Year[tvspllong$Item %in% tvs_np_grp], sum)

tvs_revr_grp = tapply(tvspllong$Amount[tvspllong$Item %in% tvs_rev_grp], 
                      tvspllong$Year[tvspllong$Item %in% tvs_rev_grp], sum)

bajaj_npr_grp = tapply(bajajpllong$Amount[bajajpllong$Items %in% bajaj_np_grp], 
                       bajajpllong$Year[bajajpllong$Items %in% bajaj_np_grp], sum)

bajaj_revr_grp = tapply(bajajpllong$Amount[bajajpllong$Items %in% bajaj_rev_grp], 
                        bajajpllong$Year[bajajpllong$Items %in% bajaj_rev_grp], sum)

tvs_np_ratio <-  (tvs_npr_grp / tvs_revr_grp) * 100
bajaj_np_ratio <- (bajaj_npr_grp / bajaj_revr_grp) * 100

#creating dataframe
npsdf=data.frame(
  Year = names(tvs_revr_grp),
  TVS_NP_Ratio = unname(tvs_np_ratio),
  BAJAJ_NP_Ratio = unname(bajaj_np_ratio))

#ROA
tvs_np_grp <- "Profit_orLossForThePeriod"
tvs_ta_grp= "TotalAssets"
bajaj_np_grp = "ProfitLossForThePeriod_"
bajaj_ta_grp = "TotalAssets"


#Finding the sum of items in each group year wise
tvs_npr_grp = tapply(tvspllong$Amount[tvspllong$Item %in% tvs_np_grp], tvspllong$Year[tvspllong$Item %in% tvs_np_grp ], sum)
tvs_tar_grp = tapply(Tvs_bs_long$Amount[Tvs_bs_long$Item %in% tvs_ta_grp], Tvs_bs_long$Year[Tvs_bs_long$Item %in% tvs_ta_grp], sum)
bajaj_npr_grp = tapply(bajajpllong$Amount[bajajpllong$Items %in% bajaj_np_grp], bajajpllong$Year[bajajpllong$Items %in% bajaj_np_grp ], sum)
bajaj_tar_grp = tapply(bajajbslong$Amount[bajajbslong$Items %in% bajaj_ta_grp], bajajbslong$Year[bajajbslong$Items %in% bajaj_ta_grp], sum)

#Finding the ratio of sum in each group - yearwise
tvs_roa_ratio <-  (tvs_npr_grp/tvs_tar_grp)*100 
bajaj_roa_ratio <- (bajaj_npr_grp/bajaj_tar_grp)*100 
#creating dataframe
roasdf=data.frame(Year=names(tvs_revr_grp),
                  TVS_ROA_Ratio=unname(tvs_roa_ratio),
                  BAJAJ_ROA_Ratio=unname(bajaj_roa_ratio))

#ROE
tvs_np_grp <- "Profit_orLossForThePeriod"
tvs_esc_grp="TotalShareholdersFunds"
bajaj_np_grp = "ProfitLossForThePeriod_"
bajaj_esc_grp = "TotalShareholders_Funds"

#Finding the sum of items in each group year wise
tvs_npr_grp = tapply(tvspllong$Amount[tvspllong$Item %in% tvs_np_grp], tvspllong$Year[tvspllong$Item %in% tvs_np_grp ], sum)
tvs_escr_grp = tapply(Tvs_bs_long$Amount[Tvs_bs_long$Item %in% tvs_esc_grp], Tvs_bs_long$Year[Tvs_bs_long$Item %in% tvs_esc_grp], sum)
bajaj_npr_grp = tapply(bajajpllong$Amount[bajajpllong$Items %in% bajaj_np_grp], bajajpllong$Year[bajajpllong$Item %in% bajaj_np_grp ], sum)
bajaj_escr_grp = tapply(bajajbslong$Amount[bajajbslong$Item %in% bajaj_esc_grp], bajajbslong$Year[bajajbslong$Item %in% bajaj_esc_grp], sum)
#Finding the ratio of sum in each group - yearwise
tvs_roe_ratio <-  (tvs_npr_grp/tvs_escr_grp)*100
bajaj_roe_ratio <- (bajaj_npr_grp/bajaj_escr_grp)*100


#creating dataframe
roesdf=data.frame(Year=names(tvs_revr_grp),
                  ROE_TVS_Ratio=unname(tvs_roe_ratio),
                  ROE_BAJAJ_Ratio=unname(tvs_roe_ratio))

The Following Are The Important Ratios Of TVS Motors & BAJAJ AUTO For The Years Of 2020 - 2024

This Ratios Shows The Performance of Both TVS Motors & BAJAJ AUTO In Comparison With Each Other

Ratios Compared (Gross profit,Net Profit,Return On Asset,Return On Equity Ratios)

print(roesdf)

##   Year ROE_TVS_Ratio ROE_BAJAJ_Ratio
## 1 2020      16.36913        16.36913
## 2 2021      14.67388        14.67388
## 3 2022      18.53075        18.53075
## 4 2023      24.65389        24.65389
## 5 2024      26.94333        26.94333

print(roasdf)

##   Year TVS_ROA_Ratio BAJAJ_ROA_Ratio
## 1 2020      6.331976        20.58660
## 2 2021      6.001893        14.44517
## 3 2022      7.542035        15.72232
## 4 2023     10.656007        18.07908
## 5 2024     12.968303        21.83547

print(npsdf)

##   Year TVS_NP_Ratio BAJAJ_NP_Ratio
## 1 2020     3.599114       16.11256
## 2 2021     3.646675       15.69599
## 3 2022     4.294000       14.60930
## 4 2023     5.631063       14.96343
## 5 2024     6.524688       16.22731

print(gpsdf)

##   Year TVS_GP_Ratio BAJAJ_GP_Ratio
## 1 2020     27.95817       38.44152
## 2 2021     25.48108       36.90682
## 3 2022     25.29061       35.46629
## 4 2023     25.94183       36.16156
## 5 2024     27.19113       36.49368

The Following is The Comparision Of Ratios Of TVS Motors With Industrial Average Performance

Ratios Compared (Gross profit,Net Profit,Return On Asset,Return On Equity Ratios)

GP_IND_AVG= c("33.1045","30.8926","30.4240","30.7397","33.0330")
gpdf=data.frame(
  Year = names(tvs_revr_grp),
  TVS_GP_Ratio = unname(tvs_gp_ratio),
  GP_IND_AVG = unname(GP_IND_AVG))
print(gpdf)

##   Year TVS_GP_Ratio GP_IND_AVG
## 1 2020     27.95817    33.1045
## 2 2021     25.48108    30.8926
## 3 2022     25.29061    30.4240
## 4 2023     25.94183    30.7397
## 5 2024     27.19113    33.0330

NP_IND_AVG= c("10.6600","9.5962","9.0671",  "9.6875",   "11.0330")
npdf=data.frame(Year=names(tvs_revr_grp),
           TVS_NP_Ratio=unname(tvs_np_ratio),
           NP_IND_AVG = unname(NP_IND_AVG))
print(npdf)

##   Year TVS_NP_Ratio NP_IND_AVG
## 1 2020     3.599114    10.6600
## 2 2021     3.646675     9.5962
## 3 2022     4.294000     9.0671
## 4 2023     5.631063     9.6875
## 5 2024     6.524688    11.0330

ROA_IND_AVG= c("15.4322","11.2742", "11.5511",  "13.7488","16.7736")
roa_df=data.frame(Year=names(tvs_revr_grp),
           TVS_ROA_Ratio=unname(tvs_roa_ratio),
           ROA_IND_AVG=unname(ROA_IND_AVG))
print(roa_df)

##   Year TVS_ROA_Ratio ROA_IND_AVG
## 1 2020      6.331976     15.4322
## 2 2021      6.001893     11.2742
## 3 2022      7.542035     11.5511
## 4 2023     10.656007     13.7488
## 5 2024     12.968303     16.7736

ROE_IND_AVG=c("22.5552","17.4164",  "17.6729",  "21.4035",  "26.3625")
roe_df=data.frame(Year=names(tvs_revr_grp),
           TVS_ROE_Ratio=unname(tvs_roe_ratio),
           ROE_IND_AVG=unname(ROE_IND_AVG))
print(roe_df)

##   Year TVS_ROE_Ratio ROE_IND_AVG
## 1 2020      16.36913     22.5552
## 2 2021      14.67388     17.4164
## 3 2022      18.53075     17.6729
## 4 2023      24.65389     21.4035
## 5 2024      26.94333     26.3625