Objective

The objective of this project is to explore Coca-Cola’s monthly revenue data from 2007 to 2020, identify patterns, analyze seasonal trends, and study the correlation between year and revenue using R and various data visualization techniques.

Load Libraries

library(readxl)
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(ggplot2)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.4     ✔ tibble    3.2.1
## ✔ purrr     1.0.4     ✔ tidyr     1.3.1
## ✔ readr     2.1.5

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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(corrplot)

## corrplot 0.95 loaded

Read the Data

data <- read_excel("C:/Users/nishant/Desktop/New folder (3)/CocaCola_Full_Monthly_Revenue_2007_2020.xlsx")

Project Overview

Imported data from Excel into R.
Cleaned and prepared the dataset by ordering months and creating a proper date column.
Conducted descriptive analysis to understand revenue patterns.
Conducted correlation analysis to study relationships between revenue and time.
Visualized the insights through graphs.

Data Preparation

data$Month <- factor(data$Month, levels = month.name)
data <- data %>%
  mutate(Date = as.Date(paste(Year, Month, "1", sep = "-"), format="%Y-%B-%d"))

Descriptive Visualizations

1. Line Chart - Revenue Over Time

ggplot(data, aes(x = Date, y = `Net Operating Revenues ($M)`)) +
  geom_line(color = "blue") +
  labs(title = "Revenue Over Time", x = "Date", y = "Revenue ($M)") +
  theme_minimal()

Interpretation:
The line chart shows a general increase in Coca-Cola’s revenue from 2007 to 2019, with some visible seasonal fluctuations. Revenue declines slightly towards 2020.

2. Bar Plot - Average Revenue by Month

data %>%
  group_by(Month) %>%
  summarize(Average_Revenue = mean(`Net Operating Revenues ($M)`)) %>%
  ggplot(aes(x = Month, y = Average_Revenue)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  labs(title = "Average Revenue by Month", x = "Month", y = "Average Revenue ($M)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Interpretation:
August and December appear to be the highest revenue months, possibly due to holiday seasons and increased beverage consumption.

3. Histogram - Revenue Distribution

ggplot(data, aes(x = `Net Operating Revenues ($M)`)) +
  geom_histogram(binwidth = 100, fill = "orange", color = "black") +
  labs(title = "Revenue Distribution", x = "Revenue ($M)", y = "Count") +
  theme_minimal()

Interpretation:
Most of the monthly revenues fall within the range of 3000-5000 million dollars.

4. Boxplot - Revenue by Year

ggplot(data, aes(x = factor(Year), y = `Net Operating Revenues ($M)`)) +
  geom_boxplot(fill = "lightgreen") +
  labs(title = "Revenue Spread by Year", x = "Year", y = "Revenue ($M)") +
  theme_minimal()

Interpretation:
Revenue spread has remained fairly consistent each year, with some occasional outliers suggesting months of exceptionally high or low revenue.

5. Pie Chart - Revenue Contribution by Year

yearly_sum <- data %>%
  group_by(Year) %>%
  summarize(Total_Revenue = sum(`Net Operating Revenues ($M)`))

ggplot(yearly_sum, aes(x = "", y = Total_Revenue, fill = factor(Year))) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0) +
  labs(title = "Revenue Share by Year") +
  theme_void()

Interpretation:
The years closer to 2019 contributed the highest share to overall revenue during this period.

6. Area Chart - Cumulative Revenue

data <- data %>%
  arrange(Date) %>%
  mutate(Cumulative_Revenue = cumsum(`Net Operating Revenues ($M)`))

ggplot(data, aes(x = Date, y = Cumulative_Revenue)) +
  geom_area(fill = "lightblue") +
  labs(title = "Cumulative Revenue Over Time", x = "Date", y = "Cumulative Revenue ($M)") +
  theme_minimal()

Interpretation:
The cumulative revenue steadily increased over time, showing Coca-Cola’s strong overall growth trajectory during these years.

7. Violin Plot - Revenue by Month

ggplot(data, aes(x = Month, y = `Net Operating Revenues ($M)`)) +
  geom_violin(fill = "violet") +
  labs(title = "Revenue Distribution by Month", x = "Month", y = "Revenue ($M)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Interpretation:
Summer and winter months show a wider spread in revenue values, suggesting stronger seasonal impacts.

8. Dot Plot - Revenue

ggplot(data, aes(x = Date, y = `Net Operating Revenues ($M)`)) +
  geom_point(color = "red") +
  labs(title = "Revenue Dots Over Time", x = "Date", y = "Revenue ($M)") +
  theme_minimal()

Interpretation:
Dot plot reveals some extreme low and high points, highlighting monthly variations clearly over the years.

Correlation Analysis

Now we study how variables are correlated, particularly how revenue varies with year.

Prepare Data for Correlation

cor_data <- data %>%
  select(Year, `Net Operating Revenues ($M)`)

1. Scatter Plot - Year vs Revenue

ggplot(cor_data, aes(x = Year, y = `Net Operating Revenues ($M)`)) +
  geom_point(color = "darkgreen") +
  geom_smooth(method = "lm", se = FALSE, color = "black") +
  labs(title = "Year vs Revenue", x = "Year", y = "Revenue ($M)") +
  theme_minimal()

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

Interpretation:
The scatter plot indicates a generally increasing trend in revenue over years, with some variability.

2. Correlation Matrix

corr_matrix <- cor(cor_data)
corrplot(corr_matrix, method = "circle", type = "upper")

Interpretation:
A positive correlation is observed between Year and Revenue, suggesting revenue growth over time.

3. Heatmap of Correlations

corrplot(corr_matrix, method = "color", addCoef.col = "black", number.cex = 0.7)

Interpretation:
The heatmap reaffirms the positive correlation strength between the year and revenue.

4. Jitter Plot - Revenue Trend Over Years

ggplot(cor_data, aes(x = Year, y = `Net Operating Revenues ($M)`)) +
  geom_jitter(width = 0.3, height = 0, color = "purple") +
  geom_smooth(method = "lm", color = "red") +
  labs(title = "Revenue Trend Over Years (Jitter Plot)", x = "Year", y = "Revenue ($M)") +
  theme_minimal()

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

Interpretation:
The jitter plot with trend line shows slight fluctuations but a clear overall increase in revenue over years.

Conclusion

The analysis revealed that Coca-Cola’s revenue exhibited a positive growth trend from 2007 to 2019 with minor seasonal fluctuations. The company showed strong cumulative revenue growth, with higher revenues in specific months like August and December. Correlation analysis confirmed that revenue and time are positively related.

Coca-Cola Revenue Data Analysis (2007-2020)