Final Project: Heidelberg Cement Bangladesh Stock Price Analysis

Load Required Libraries

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
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##     filter, lag

## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union

library(zoo)

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
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##     as.Date, as.Date.numeric

library(scales)
library(plotly)

## Loading required package: ggplot2

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## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
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library(ggplot2)
library(gganimate)
library(gifski)
library(av)

Load the Dataset

df <- read.csv("Heidelberg Cement Bangladesh Stock Price History.csv", stringsAsFactors = FALSE)

head(df$Date)  

## [1] "3/27/2025" "3/25/2025" "3/24/2025" "3/23/2025" "3/20/2025" "3/19/2025"

str(df$Date)

##  chr [1:3578] "3/27/2025" "3/25/2025" "3/24/2025" "3/23/2025" "3/20/2025" ...

Dataset Overview: Heidelberg Cement Bangladesh Stock Price History

The dataset contains 3,578 rows and 7 columns, tracking daily stock price movements.

Variables:

1.Date – Trading date

2.Price – Closing stock price

3.Open – Opening stock price

4.High – Highest stock price of the day

5.Low – Lowest stock price of the day

6.Vol. – Trading volume

7.Change % – Daily percentage change

Data Cleaning

df$Date <- as.Date(df$Date, format = "%m/%d/%Y")


colnames(df)[colnames(df) == "Change.."] <- "ChangePerc"

df$ChangePerc <- gsub("%", "", df$ChangePerc) 
df$ChangePerc <- as.numeric(df$ChangePerc) / 100

summary(df$ChangePerc)

##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -0.1646000 -0.0085000 -0.0005500  0.0002196  0.0067000  0.2260000

df$Vol. <- gsub("K", "", df$Vol.)  
df$Vol. <- gsub(",", "", df$Vol.)  
df$Vol. <- as.numeric(df$Vol.) * 1000

## Warning: NAs introduced by coercion

sum(is.na(df$Vol.))

## [1] 2

df <- df[!is.na(df$Vol.), ]

df <- na.omit(df)

Summary Statistics of Heidelberg Cement Bangladesh Stock Prices

# 1. Summary of Stock Prices
summary(df$Price)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   125.3   234.4   300.9   332.2   427.1   675.1

# 2. Stock Price Volatility
range(df$Price)

## [1] 125.3 675.1

sd(df$Price)

## [1] 130.8648

# 3. Daily Percentage Change
summary(df$ChangePerc)

##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -0.1646000 -0.0085000 -0.0006000  0.0001802  0.0066250  0.2260000

# 4. Trading Volume Analysis
summary(df$Vol.)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       0    6948   18130   54814   52413  974650

# 5. Correlation Between Stock Prices & Volume
cor(df$Price, df$Vol., use = "complete.obs")

## [1] 0.1666656

# 6. Rolling 30-day Moving Average for Trends
df$MovingAvg <- rollmean(df$Price, k = 30, fill = NA, align = "right")

Static Visualization

ggplot(df, aes(x = Date, y = Price)) +
  geom_line(color = "blue", alpha = 0.7, size = 1.5) +
  geom_smooth(method = "lm", color = "red") +
  scale_x_date(date_labels = "%Y", date_breaks = "2 years") +
  theme_minimal() +
  ggtitle("Stock Price Trend of Heidelberg Cement Bangladesh")

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## `geom_smooth()` using formula = 'y ~ x'

Description if Visualization

This plot shows the trend of Heidelberg Cement Bangladesh’s stock price over time. The blue line represents the daily closing prices, while the red regression line shows the overall trend of stock price movement over the period. The x-axis is labeled with years, and the transparency of the blue line (alpha = 0.7) helps reduce clutter and make the plot clearer.

Interactive Visualization

ggplotly()

## `geom_smooth()` using formula = 'y ~ x'

Animated Visualization

p_anim <- ggplot(df, aes(x = Date, y = Price)) +
  geom_line(color = "blue") +
  transition_reveal(Date) +
  ggtitle("Stock Price Movement Over Time")

animate(p_anim, fps = 10, duration = 10)

# Save the animation

anim_save("Stock_Price_Animation.gif", animation = p_anim)