20251MBI017_Divin_Claudel_Ishimwe
Ishimwe Divin Claudel
2026-05-23
Master of Science in Big Data Analytics
R for Data Science
Mid Term Exam
Stock Market Analysis and Prediction by Using a Regression Model
# Load all the datasets in R Stidio separately
# excluding NIFTY50_all.csv and stock_metadata.csv
# ================================================
ADANIPORTS <- read.csv("ADANIPORTS.csv")
ASIANPAINT <- read.csv("ASIANPAINT.csv")
AXISBANK <- read.csv("AXISBANK.csv")
BAJAJ_AUTO <- read.csv("BAJAJ-AUTO.csv")
BAJAJFINSV <- read.csv("BAJAJFINSV.csv")
BAJFINANCE <- read.csv("BAJFINANCE.csv")
BHARTIARTL <- read.csv("BHARTIARTL.csv")
BPCL <- read.csv("BPCL.csv")
BRITANNIA <- read.csv("BRITANNIA.csv")
CIPLA <- read.csv("CIPLA.csv")
COALINDIA <- read.csv("COALINDIA.csv")
DRREDDY <- read.csv("DRREDDY.csv")
EICHERMOT <- read.csv("EICHERMOT.csv")
GAIL <- read.csv("GAIL.csv")
GRASIM <- read.csv("GRASIM.csv")
HCLTECH <- read.csv("HCLTECH.csv")
HDFC <- read.csv("HDFC.csv")
HDFCBANK <- read.csv("HDFCBANK.csv")
HEROMOTOCO <- read.csv("HEROMOTOCO.csv")
HINDALCO <- read.csv("HINDALCO.csv")
HINDUNILVR <- read.csv("HINDUNILVR.csv")
ICICIBANK <- read.csv("ICICIBANK.csv")
INDUSINDBK <- read.csv("INDUSINDBK.csv")
INFRATEL <- read.csv("INFRATEL.csv")
INFY <- read.csv("INFY.csv")
IOC <- read.csv("IOC.csv")
ITC <- read.csv("ITC.csv")
JSWSTEEL <- read.csv("JSWSTEEL.csv")
KOTAKBANK <- read.csv("KOTAKBANK.csv")
LT <- read.csv("LT.csv")
MARUTI <- read.csv("MARUTI.csv")
MM <- read.csv("MM.csv")
NESTLEIND <- read.csv("NESTLEIND.csv")
NTPC <- read.csv("NTPC.csv")
ONGC <- read.csv("ONGC.csv")
POWERGRID <- read.csv("POWERGRID.csv")
RELIANCE <- read.csv("RELIANCE.csv")
SBIN <- read.csv("SBIN.csv")
SHREECEM <- read.csv("SHREECEM.csv")
SUNPHARMA <- read.csv("SUNPHARMA.csv")
TATAMOTORS <- read.csv("TATAMOTORS.csv")
TATASTEEL <- read.csv("TATASTEEL.csv")
TCS <- read.csv("TCS.csv")
TECHM <- read.csv("TECHM.csv")
TITAN <- read.csv("TITAN.csv")
ULTRACEMCO <- read.csv("ULTRACEMCO.csv")
UPL <- read.csv("UPL.csv")
VEDL <- read.csv("VEDL.csv")
WIPRO <- read.csv("WIPRO.csv")
ZEEL <- read.csv("ZEEL.csv")
# Display first 6 rows of Zee Entertainment Enterprises Ltd Vedanta Ltd datasets
# Checking that datasets are loaded
# ==============================================================================
#Zee Entertainment Enterprises Ltd
head(ZEEL)## Date Symbol Series Prev.Close Open High Low Last Close
## 1 2000-01-03 ZEETELE EQ 1092.55 1175.00 1179.95 1160.00 1179.95 1179.95
## 2 2000-01-04 ZEETELE EQ 1179.95 1220.00 1274.35 1183.10 1274.35 1260.65
## 3 2000-01-05 ZEETELE EQ 1260.65 1160.55 1317.70 1159.80 1190.95 1176.55
## 4 2000-01-06 ZEETELE EQ 1176.55 1195.00 1200.00 1095.00 1106.00 1115.45
## 5 2000-01-07 ZEETELE EQ 1115.45 1097.10 1097.10 1026.25 1026.25 1026.25
## 6 2000-01-10 ZEETELE EQ 1026.25 1026.25 1026.25 944.30 962.00 966.70
## VWAP Volume Turnover Trades Deliverable.Volume X.Deliverble
## 1 1177.03 1261391 1.484690e+14 NA NA NA
## 2 1228.02 4616547 5.669220e+14 NA NA NA
## 3 1238.35 8763127 1.085178e+15 NA NA NA
## 4 1135.04 5164020 5.861353e+14 NA NA NA
## 5 1029.94 755129 7.777374e+13 NA NA NA
## 6 980.49 3942813 3.865885e+14 NA NA NA#Vedanta Ltd
head(VEDL)## Date Symbol Series Prev.Close Open High Low Last Close
## 1 2000-01-03 SESAGOA EQ 107.70 111.00 116.35 108.00 116.35 116.35
## 2 2000-01-04 SESAGOA EQ 116.35 113.75 116.00 108.00 116.00 114.70
## 3 2000-01-05 SESAGOA EQ 114.70 107.55 115.85 107.55 114.50 114.00
## 4 2000-01-06 SESAGOA EQ 114.00 112.00 123.10 112.00 118.80 119.30
## 5 2000-01-07 SESAGOA EQ 119.30 119.85 120.00 114.05 116.50 116.50
## 6 2000-01-10 SESAGOA EQ 116.50 120.50 120.85 116.00 119.00 119.00
## VWAP Volume Turnover Trades Deliverable.Volume X.Deliverble
## 1 114.80 20371 233859660000 NA NA NA
## 2 113.34 22366 253499440000 NA NA NA
## 3 112.78 18305 206436075000 NA NA NA
## 4 119.89 25800 309313325000 NA NA NA
## 5 116.84 17361 202840260000 NA NA NA
## 6 118.69 20707 245767815000 NA NA NA4.1 Exploratory data analysis
Create a function to profile each stock by displaying the following information: trading date range, missing values, number of outliers, top 5 rows, data types, and summary statistics. Then, apply the function to the Zee Entertainment Enterprises Ltd dataset and display the results.
# =========================================================
# Load Zee Entertainment dataset
# =========================================================
ZEEL <- read.csv("ZEEL.csv")
# Convert Date column into proper date format
ZEEL$Date <- as.Date(ZEEL$Date)
# =========================================================
# Function to profile stock dataset
# =========================================================
stock_profile <- function(data) {
print("Trading Date Range")
print(range(data$Date))
print("Missing Values")
print(colSums(is.na(data)))
print("Number of Outliers in Close Price")
# Detect outliers using IQR method
Q1 <- quantile(data$Close, 0.25)
Q3 <- quantile(data$Close, 0.75)
IQR_value <- Q3 - Q1
lower_limit <- Q1 - 1.5 * IQR_value
upper_limit <- Q3 + 1.5 * IQR_value
outliers <- data$Close[
data$Close < lower_limit |
data$Close > upper_limit
]
print(length(outliers))
print("Top 5 Rows")
print(head(data, 5))
print("Data Types")
print(str(data))
print("Summary Statistics")
print(summary(data))
}
# =========================================================
# Apply function to ZEEL dataset
# =========================================================
stock_profile(ZEEL)## [1] "Trading Date Range"
## [1] "2000-01-03" "2021-04-30"
## [1] "Missing Values"
## Date Symbol Series Prev.Close
## 0 0 0 0
## Open High Low Last
## 0 0 0 0
## Close VWAP Volume Turnover
## 0 0 0 0
## Trades Deliverable.Volume X.Deliverble
## 2850 519 519
## [1] "Number of Outliers in Close Price"
## [1] 84
## [1] "Top 5 Rows"
## Date Symbol Series Prev.Close Open High Low Last Close
## 1 2000-01-03 ZEETELE EQ 1092.55 1175.00 1179.95 1160.00 1179.95 1179.95
## 2 2000-01-04 ZEETELE EQ 1179.95 1220.00 1274.35 1183.10 1274.35 1260.65
## 3 2000-01-05 ZEETELE EQ 1260.65 1160.55 1317.70 1159.80 1190.95 1176.55
## 4 2000-01-06 ZEETELE EQ 1176.55 1195.00 1200.00 1095.00 1106.00 1115.45
## 5 2000-01-07 ZEETELE EQ 1115.45 1097.10 1097.10 1026.25 1026.25 1026.25
## VWAP Volume Turnover Trades Deliverable.Volume X.Deliverble
## 1 1177.03 1261391 1.484690e+14 NA NA NA
## 2 1228.02 4616547 5.669220e+14 NA NA NA
## 3 1238.35 8763127 1.085178e+15 NA NA NA
## 4 1135.04 5164020 5.861353e+14 NA NA NA
## 5 1029.94 755129 7.777374e+13 NA NA NA
## [1] "Data Types"
## 'data.frame': 5306 obs. of 15 variables:
## $ Date : Date, format: "2000-01-03" "2000-01-04" ...
## $ Symbol : chr "ZEETELE" "ZEETELE" "ZEETELE" "ZEETELE" ...
## $ Series : chr "EQ" "EQ" "EQ" "EQ" ...
## $ Prev.Close : num 1093 1180 1261 1177 1115 ...
## $ Open : num 1175 1220 1161 1195 1097 ...
## $ High : num 1180 1274 1318 1200 1097 ...
## $ Low : num 1160 1183 1160 1095 1026 ...
## $ Last : num 1180 1274 1191 1106 1026 ...
## $ Close : num 1180 1261 1177 1115 1026 ...
## $ VWAP : num 1177 1228 1238 1135 1030 ...
## $ Volume : int 1261391 4616547 8763127 5164020 755129 3942813 6802005 2968833 2251046 2949092 ...
## $ Turnover : num 1.48e+14 5.67e+14 1.09e+15 5.86e+14 7.78e+13 ...
## $ Trades : num NA NA NA NA NA NA NA NA NA NA ...
## $ Deliverable.Volume: num NA NA NA NA NA NA NA NA NA NA ...
## $ X.Deliverble : num NA NA NA NA NA NA NA NA NA NA ...
## NULL
## [1] "Summary Statistics"
## Date Symbol Series Prev.Close
## Min. :2000-01-03 Length :5306 Length :5306 Min. : 62.3
## 1st Qu.:2005-04-13 N.unique : 2 N.unique : 1 1st Qu.: 143.2
## Median :2010-08-17 N.blank : 0 N.blank : 0 Median : 238.2
## Mean :2010-08-18 Min.nchar: 4 Min.nchar: 2 Mean : 273.4
## 3rd Qu.:2015-12-17 Max.nchar: 7 Max.nchar: 2 3rd Qu.: 345.6
## Max. :2021-04-30 Max. :1541.7
##
## Open High Low Last
## Min. : 62 Min. : 66.3 Min. : 60.1 Min. : 62.7
## 1st Qu.: 144 1st Qu.: 146.9 1st Qu.: 140.0 1st Qu.: 143.5
## Median : 238 Median : 244.0 Median : 231.4 Median : 237.7
## Mean : 274 Mean : 279.6 Mean : 267.6 Mean : 273.2
## 3rd Qu.: 346 3rd Qu.: 352.8 3rd Qu.: 338.4 3rd Qu.: 345.1
## Max. :1640 Max. :1645.0 Max. :1512.2 Max. :1564.0
##
## Close VWAP Volume Turnover
## Min. : 62.3 Min. : 63.08 Min. : 4415 Min. :7.021e+10
## 1st Qu.: 143.2 1st Qu.: 143.68 1st Qu.: 1218226 1st Qu.:2.595e+13
## Median : 238.1 Median : 238.90 Median : 2138807 Median :5.250e+13
## Mean : 273.2 Mean : 273.63 Mean : 4825422 Mean :1.249e+14
## 3rd Qu.: 345.6 3rd Qu.: 345.64 3rd Qu.: 4532904 3rd Qu.:1.137e+14
## Max. :1541.7 Max. :1578.11 Max. :165959680 Max. :4.286e+15
##
## Trades Deliverable.Volume X.Deliverble
## Min. : 296 Min. : 4415 Min. :0.0557
## 1st Qu.: 24579 1st Qu.: 513686 1st Qu.:0.3073
## Median : 41074 Median : 893532 Median :0.4635
## Mean : 62646 Mean : 1415718 Mean :0.4522
## 3rd Qu.: 71463 3rd Qu.: 1593444 3rd Qu.:0.5939
## Max. :1088460 Max. :42891428 Max. :1.0000
## NAs :2850 NAs :519 NAs :519Analyze trading volume trends and identify unusual volume spikes. Identify at least 3 significant market events visible in the data (e.g., COVID-19,…) and annotate them on chart.
# =========================================================
# Trading Volume Trend Analysis
# =========================================================
# Plot trading volume trend
plot(
ZEEL$Date,
ZEEL$Volume,
type = "l",
main = "ZEEL Trading Volume Trend",
xlab = "Date",
ylab = "Trading Volume"
)
# =========================================================
# Identify unusual volume spikes
# =========================================================
volume_threshold <- mean(ZEEL$Volume) + 2 * sd(ZEEL$Volume)
spikes <- ZEEL[ZEEL$Volume > volume_threshold, ]
# Display unusual spikes
head(spikes)## Date Symbol Series Prev.Close Open High Low Last Close
## 161 2000-08-23 ZEETELE EQ 492.75 495.2 510.95 483.3 498.55 505.15
## 235 2000-12-07 ZEETELE EQ 306.45 306.0 329.80 305.0 327.70 324.05
## 265 2001-01-19 ZEETELE EQ 227.95 229.0 263.90 228.6 262.00 258.10
## 267 2001-01-23 ZEETELE EQ 267.00 265.5 289.95 265.0 285.50 285.85
## 269 2001-01-25 ZEETELE EQ 278.15 280.0 284.00 263.1 277.60 276.35
## 288 2001-02-22 ZEETELE EQ 237.20 235.5 240.70 227.1 231.50 233.50
## VWAP Volume Turnover Trades Deliverable.Volume X.Deliverble
## 161 500.04 23396356 1.169918e+15 NA NA NA
## 235 318.36 22379180 7.124565e+14 NA NA NA
## 265 245.36 26527273 6.508762e+14 NA NA NA
## 267 281.88 23827589 6.716586e+14 NA NA NA
## 269 272.20 24840007 6.761374e+14 NA NA NA
## 288 233.76 23414924 5.473381e+14 NA NA NA# Highlight spikes on chart
points(
spikes$Date,
spikes$Volume,
col = "red",
pch = 19
)
# =========================================================
# Add significant market events
# =========================================================
# COVID-19 Market Crash
abline(v = as.Date("2020-03-01"), col = "blue", lty = 2)
text(
as.Date("2020-03-01"),
max(ZEEL$Volume),
"COVID-19",
pos = 4
)
# Market Recovery
abline(v = as.Date("2021-01-01"), col = "green", lty = 2)
text(
as.Date("2021-01-01"),
max(ZEEL$Volume) * 0.9,
"Market Recovery",
pos = 4
)
# Inflation / Global Uncertainty
abline(v = as.Date("2022-02-01"), col = "purple", lty = 2)
text(
as.Date("2022-02-01"),
max(ZEEL$Volume) * 0.8,
"Global Uncertainty",
pos = 4
)
4.3 Visualization
# ============================================
# Load packages
# ============================================
library(ggplot2)
library(zoo)##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric# ============================================
# Load dataset
# ============================================
ZEEL <- read.csv("ZEEL.csv")
# Convert Date column
ZEEL$Date <- as.Date(ZEEL$Date)
# ============================================
# Percentage Change
# ============================================
ZEEL$Percentage_Change <-
((ZEEL$Close - ZEEL$Prev.Close) /
ZEEL$Prev.Close) * 100
# ============================================
# Moving Averages
# ============================================
ZEEL$MA15 <- rollmean(ZEEL$Close, 15, fill = NA)
ZEEL$MA30 <- rollmean(ZEEL$Close, 30, fill = NA)
ZEEL$MA45 <- rollmean(ZEEL$Close, 45, fill = NA)
# ============================================
# Closing Price Trend
# ============================================
ggplot(ZEEL, aes(Date, Close)) +
geom_line() +
ggtitle("Closing Price Trend")
# ============================================
# Percentage Change Trend
# ============================================
ggplot(ZEEL, aes(Date, Percentage_Change)) +
geom_line() +
ggtitle("Percentage Change Trend")
# ============================================
# Sales Volume Trend
# ============================================
ggplot(ZEEL, aes(Date, Volume)) +
geom_line() +
ggtitle("Sales Volume Trend")
# ============================================
# Moving Average Trend
# ============================================
ggplot(ZEEL, aes(Date, Close)) +
geom_line() +
geom_line(aes(y = MA15), color = "blue") +
geom_line(aes(y = MA30), color = "red") +
geom_line(aes(y = MA45), color = "green") +
ggtitle("15, 30 and 45 Days Moving Average")## Warning: Removed 14 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 44 rows containing missing values or values outside the scale range
## (`geom_line()`).
# ============================================
# Histogram
# ============================================
ggplot(ZEEL, aes(Percentage_Change)) +
geom_histogram(bins = 30) +
ggtitle("Histogram of Percentage Change")
4.4 Correlation Analysis
# ============================================
# Load dataset
# ============================================
ZEEL <- read.csv("ZEEL.csv")
# ============================================
# Select numerical columns
# ============================================
data <- ZEEL[, c(
"Prev.Close",
"Open",
"High",
"Low",
"Close",
"Volume"
)]
# ============================================
# Correlation Matrix
# ============================================
cor(data)## Prev.Close Open High Low Close
## Prev.Close 1.00000000 0.99942268 0.99885060 0.99838428 0.99790147
## Open 0.99942268 1.00000000 0.99913584 0.99869374 0.99811311
## High 0.99885060 0.99913584 1.00000000 0.99814370 0.99880555
## Low 0.99838428 0.99869374 0.99814370 1.00000000 0.99911821
## Close 0.99790147 0.99811311 0.99880555 0.99911821 1.00000000
## Volume -0.04934908 -0.04832402 -0.03974465 -0.05694402 -0.04744036
## Volume
## Prev.Close -0.04934908
## Open -0.04832402
## High -0.03974465
## Low -0.05694402
## Close -0.04744036
## Volume 1.00000000# ============================================
# Scatter Plot Matrix
# ============================================
pairs(data)
# ============================================
# Heatmap of Correlation
# ============================================
heatmap(cor(data))
# ============================================
# Find highly correlated pairs
# ============================================
correlation <- cor(data)
print(correlation)## Prev.Close Open High Low Close
## Prev.Close 1.00000000 0.99942268 0.99885060 0.99838428 0.99790147
## Open 0.99942268 1.00000000 0.99913584 0.99869374 0.99811311
## High 0.99885060 0.99913584 1.00000000 0.99814370 0.99880555
## Low 0.99838428 0.99869374 0.99814370 1.00000000 0.99911821
## Close 0.99790147 0.99811311 0.99880555 0.99911821 1.00000000
## Volume -0.04934908 -0.04832402 -0.03974465 -0.05694402 -0.04744036
## Volume
## Prev.Close -0.04934908
## Open -0.04832402
## High -0.03974465
## Low -0.05694402
## Close -0.04744036
## Volume 1.000000004.5 Regression Analysis
# ============================================
# Load dataset
# ============================================
ZEEL <- read.csv("ZEEL.csv")
# ============================================
# Select variables
# ============================================
data <- ZEEL[, c(
"Prev.Close",
"Open",
"High",
"Low",
"Volume",
"Close"
)]
# Remove missing values
data <- na.omit(data)
# ============================================
# Split data into training and test sets
# ============================================
set.seed(123)
sample_size <- floor(0.8 * nrow(data))
train_index <- sample(
1:nrow(data),
sample_size
)
train_data <- data[train_index, ]
test_data <- data[-train_index, ]
# Print sizes
print(dim(train_data))## [1] 4244 6print(dim(test_data))## [1] 1062 6# ============================================
# Train regression model
# ============================================
model <- lm(
Close ~ Prev.Close + Open + High + Low + Volume,
data = train_data
)
# ============================================
# Predict test data
# ============================================
predicted <- predict(
model,
test_data
)
# ============================================
# Performance Metrics
# ============================================
# RMSE
rmse <- sqrt(
mean((test_data$Close - predicted)^2)
)
print(rmse)## [1] 4.847351# MAE
mae <- mean(
abs(test_data$Close - predicted)
)
print(mae)## [1] 2.403604# R2
r2 <- cor(
test_data$Close,
predicted
)^2
print(r2)## [1] 0.9992598# ============================================
# Scatter Plot
# ============================================
plot(
test_data$Close,
predicted,
main = "Actual vs Predicted Close Price",
xlab = "Actual Close Price",
ylab = "Predicted Close Price"
)
# 45-degree reference line
abline(0, 1, col = "red")
# ============================================
# Standardize independent variables
# ============================================
x <- data[, c(
"Prev.Close",
"Open",
"High",
"Low",
"Volume"
)]
scaled_x <- scale(x)
# Mean after scaling
print(colMeans(scaled_x))## Prev.Close Open High Low Volume
## 1.260117e-16 -1.713930e-17 3.974498e-17 -4.879196e-17 1.369993e-17# Standard deviation after scaling
print(apply(scaled_x, 2, sd))## Prev.Close Open High Low Volume
## 1 1 1 1 1# ============================================
# PCA
# ============================================
pca <- prcomp(
scaled_x,
center = TRUE,
scale. = TRUE
)
# Select first 2 components
pca_data <- pca$x[, 1:2]
# Split PCA data
train_pca <- pca_data[train_index, ]
test_pca <- pca_data[-train_index, ]
# Train PCA regression model
pca_model <- lm(
train_data$Close ~ .,
data = as.data.frame(train_pca)
)
# Predict PCA model
pca_pred <- predict(
pca_model,
as.data.frame(test_pca)
)
# PCA R2
pca_r2 <- cor(
test_data$Close,
pca_pred
)^2
print(pca_r2)## [1] 0.99765174.6 Sales volume comparisons
# ============================================
# Load datasets
# ============================================
ZEEL <- read.csv("ZEEL.csv")
VEDL <- read.csv("VEDL.csv")
# ============================================
# Convert Date column
# ============================================
ZEEL$Date <- as.Date(ZEEL$Date)
VEDL$Date <- as.Date(VEDL$Date)
# ============================================
# Extract Year
# ============================================
ZEEL$Year <- format(ZEEL$Date, "%Y")
VEDL$Year <- format(VEDL$Date, "%Y")
# ============================================
# Annual Sales Volume
# ============================================
zeel_volume <- aggregate(
Volume ~ Year,
data = ZEEL,
sum
)
vedl_volume <- aggregate(
Volume ~ Year,
data = VEDL,
sum
)
# ============================================
# Combine datasets
# ============================================
combined <- data.frame(
Year = zeel_volume$Year,
ZEEL = zeel_volume$Volume,
VEDL = vedl_volume$Volume
)
print(combined)## Year ZEEL VEDL
## 1 2000 2124967599 3859848
## 2 2001 2136706114 7426543
## 3 2002 1402554565 20751661
## 4 2003 1000731804 46142649
## 5 2004 569852081 100736719
## 6 2005 459053470 171258643
## 7 2006 611240479 45725647
## 8 2007 408755292 72642593
## 9 2008 307791908 641538432
## 10 2009 436102564 1888019256
## 11 2010 377601412 1266509516
## 12 2011 465542142 652829352
## 13 2012 477606441 664266631
## 14 2013 626168229 1064395380
## 15 2014 631955791 1516787175
## 16 2015 565559180 1896714942
## 17 2016 506730371 4038767678
## 18 2017 493681594 2598719114
## 19 2018 598775408 3276296751
## 20 2019 3696617196 2944363596
## 21 2020 6403204532 7377503381
## 22 2021 1302490317 1430971315# ============================================
# Clustered Bar Chart
# ============================================
barplot(
t(as.matrix(combined[, 2:3])),
beside = TRUE,
names.arg = combined$Year,
col = c("blue", "green"),
main = "Annual Sales Volume Comparison",
xlab = "Year",
ylab = "Total Volume"
)
legend(
"topright",
legend = c("ZEEL", "VEDL"),
fill = c("blue", "green")
)
# ============================================
# 2018 Highest Volume
# ============================================
volume_2018 <- combined[
combined$Year == "2018",
]
print(volume_2018)## Year ZEEL VEDL
## 19 2018 598775408 3276296751# ============================================
# 2021 Lowest Volume
# ============================================
volume_2021 <- combined[
combined$Year == "2021",
]
print(volume_2021)## Year ZEEL VEDL
## 22 2021 1302490317 1430971315# ============================================
# Ratio for 2021
# ============================================
ratio_2021 <- volume_2021$ZEEL /
volume_2021$VEDL
print(ratio_2021)## [1] 0.9102141