Objective

The objective of this project is to explore Coca-Cola’s monthly revenue data from 2007 to 2020, identify patterns, and analyze correlations between time 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':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     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

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
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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.
Cleaned and organized the dataset.
Created new features such as Date for time series analysis.
Conducted descriptive and correlation analysis with 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

We first perform descriptive analysis to understand trends, distributions, and variations in Coca-Cola’s monthly revenues.

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()

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))

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()

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()

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()

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()

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))

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()

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'

2. Correlation Matrix

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

3. Heatmap of Correlations

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

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'

Coca-Cola Revenue Data Analysis (2007-2020)