Sales_Analysis

R Markdown

Exploratory Data Analysis with R

Introduction

In this analysis, we’ll explore a fictional sales dataset using R. We’ll load the necessary libraries, examine the dataset, calculate total sales for each product, and visualize the results using bar plots. Additionally, we’ll calculate average sales per month and visualize them using line plots.

Load Libraries and Data

```R library(dplyr) library(ggplot2)

Create a fictional dataset

sales_data <- data.frame( Product = c(“A”, “B”, “C”, “A”, “B”, “C”, “A”, “B”, “C”), Month = rep(c(“Jan”, “Feb”, “Mar”), times = 3), Sales = c(100, 150, 80, 120, 180, 90, 110, 160, 85) )

Explore the dataset

head(sales_data) summary(sales_data) # Calculate total sales for each product total_sales <- sales_data %>% group_by(Product) %>% summarise(Total_Sales = sum(Sales))

Visualize total sales using a bar plot

ggplot(data = total_sales, aes(x = Product, y = Total_Sales, fill = Product)) + geom_bar(stat = “identity”) + labs(title = “Total Sales by Product”, x = “Product”, y = “Total Sales”) + theme_minimal() # Calculate average sales per month average_sales <- sales_data %>% group_by(Month) %>% summarise(Average_Sales = mean(Sales))

Visualize average sales per month using a line plot

ggplot(data = average_sales, aes(x = Month, y = Average_Sales, group = 1)) + geom_line(color = “blue”) + geom_point(color = “blue”) + labs(title = “Average Sales per Month”, x = “Month”, y = “Average Sales”) + theme_minimal()