Module # 9 Visualization in R

 





Basic Histogram: Distribution of Cigarette Prices
Our journey begins with a basic histogram, shedding light on the distribution of cigarette prices in the dataset.


# Basic Histogram
hist(CigarettesB$price, main = "Distribution of Cigarette Prices", xlab = "Price")

The histogram vividly illustrates the spread of prices, giving us a glimpse into the variability and concentration within the dataset. Peaks and troughs in the histogram reveal potential clusters or outliers, setting the stage for further exploration.

Lattice Scatterplot Matrix: Unveiling Multivariate Relationships
Next, we employ a lattice scatterplot matrix, a powerful tool for understanding relationships between multiple variables simultaneously.


# Lattice Scatterplot Matrix
library(lattice)
splom(~CigarettesB[, c("packs", "price", "income")], main = "Scatterplot Matrix")

The scatterplot matrix allows us to identify patterns and correlations between "packs," "price," and "income." Each subplot provides a visual link between two variables, offering a comprehensive view of the dataset's multivariate structure.



ggplot2 Bar Chart: Average Income by Observation
Lastly, we delve into ggplot2 to create a bar chart showcasing the average income by observation. Despite the initial error in the code, we can refine it to accurately reflect the variable used.

# ggplot2 Bar Chart
library(ggplot2)
ggplot(CigarettesB, aes(x = rownames(CigarettesB), y = income)) +
  geom_bar(stat = "summary", fun = "mean", fill = "skyblue") +
  labs(title = "Average Income by Observation", x = "Observation", y = "Average Income")

This bar chart gives us insights into the average income across observations, highlighting any variations or trends present in the data.

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