ggplot(df, aes(x = body_mass_g)) +
geom_histogram(bins = 12) +
labs(title = "Distribution of Body Mass")
4 Exploratory Data Analysis
In this section, we begin exploring the cleaned dataset. Exploratory Data Analysis (EDA) helps us understand the structure of the data, identify patterns, and guide our modeling decisions.
EDA is an important step because it allows us to: - understand distributions of variables
- identify relationships between variables
- detect potential outliers or unusual values
- build intuition before fitting models
4.1 Distribution of Body Mass
We start by examining the distribution of body mass to understand its overall shape.
4.2 Body Mass vs Flipper Length
Next, we explore the relationship between flipper length and body mass.
ggplot(df, aes(x = flipper_length_mm, y = body_mass_g)) +
geom_point() +
labs(title = "Body Mass vs Flipper Length")
4.3 Body Mass vs Bill Length
We also examine how bill length relates to body mass.
ggplot(df, aes(x = bill_length_mm, y = body_mass_g)) +
geom_point() +
labs(title = "Body Mass vs Bill Length")
4.4 Body Mass by Species
Finally, we compare body mass across different penguin species.
ggplot(df, aes(x = species, y = body_mass_g)) +
geom_boxplot() +
labs(title = "Body Mass by Species")
4.5 Key Takeaways
From these plots, we can begin to see patterns in the data:
- body mass has a clear distribution
- some variables appear to have relationships with body mass
- differences between species may be important
These insights will guide how we build and interpret our models in the next section.