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Case Study 1: Data Analysis

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Introduction

In this case study, we will apply the data analysis techniques learned throughout the course to a real-world dataset. The goal is to perform a comprehensive analysis, including data cleaning, manipulation, visualization, and statistical analysis. By the end of this case study, you should be able to:

  1. Import and clean a dataset.
  2. Perform exploratory data analysis (EDA).
  3. Visualize data using various plotting techniques.
  4. Conduct statistical tests to derive insights.

Dataset Description

For this case study, we will use the "Iris" dataset, which is a classic dataset in the field of data analysis and machine learning. The dataset contains 150 observations of iris flowers, with the following features:

  • Sepal.Length: Length of the sepal (in cm).
  • Sepal.Width: Width of the sepal (in cm).
  • Petal.Length: Length of the petal (in cm).
  • Petal.Width: Width of the petal (in cm).
  • Species: Species of the iris flower (setosa, versicolor, virginica).

Step 1: Importing the Dataset

First, we need to import the dataset into R.

# Load necessary libraries
library(dplyr)
library(ggplot2)

# Load the dataset
data(iris)

# Display the first few rows of the dataset
head(iris)

Explanation

  • We load the dplyr and ggplot2 libraries for data manipulation and visualization.
  • The data(iris) function loads the Iris dataset.
  • The head(iris) function displays the first few rows of the dataset.

Step 2: Data Cleaning

Next, we will check for any missing values and clean the dataset if necessary.

# Check for missing values
sum(is.na(iris))

# Summary of the dataset
summary(iris)

Explanation

  • sum(is.na(iris)) checks for any missing values in the dataset.
  • summary(iris) provides a summary of the dataset, including the minimum, maximum, mean, and quartiles for each numeric variable.

Step 3: Exploratory Data Analysis (EDA)

Descriptive Statistics

We will start by calculating some basic descriptive statistics.

# Calculate mean, median, and standard deviation for each numeric variable
iris %>%
  summarise(
    Sepal.Length.Mean = mean(Sepal.Length),
    Sepal.Width.Mean = mean(Sepal.Width),
    Petal.Length.Mean = mean(Petal.Length),
    Petal.Width.Mean = mean(Petal.Width),
    Sepal.Length.Median = median(Sepal.Length),
    Sepal.Width.Median = median(Sepal.Width),
    Petal.Length.Median = median(Petal.Length),
    Petal.Width.Median = median(Petal.Width),
    Sepal.Length.SD = sd(Sepal.Length),
    Sepal.Width.SD = sd(Sepal.Width),
    Petal.Length.SD = sd(Petal.Length),
    Petal.Width.SD = sd(Petal.Width)
  )

Explanation

  • We use the summarise function from dplyr to calculate the mean, median, and standard deviation for each numeric variable in the dataset.

Data Visualization

Histogram

We will create histograms to visualize the distribution of each numeric variable.

# Histogram for Sepal Length
ggplot(iris, aes(x = Sepal.Length)) +
  geom_histogram(binwidth = 0.3, fill = "blue", color = "black") +
  labs(title = "Histogram of Sepal Length", x = "Sepal Length", y = "Frequency")

# Histogram for Sepal Width
ggplot(iris, aes(x = Sepal.Width)) +
  geom_histogram(binwidth = 0.3, fill = "green", color = "black") +
  labs(title = "Histogram of Sepal Width", x = "Sepal Width", y = "Frequency")

# Histogram for Petal Length
ggplot(iris, aes(x = Petal.Length)) +
  geom_histogram(binwidth = 0.3, fill = "red", color = "black") +
  labs(title = "Histogram of Petal Length", x = "Petal Length", y = "Frequency")

# Histogram for Petal Width
ggplot(iris, aes(x = Petal.Width)) +
  geom_histogram(binwidth = 0.3, fill = "purple", color = "black") +
  labs(title = "Histogram of Petal Width", x = "Petal Width", y = "Frequency")

Explanation

  • We use ggplot2 to create histograms for each numeric variable. The geom_histogram function is used to create the histograms, and the labs function is used to add titles and labels.

Boxplot

We will create boxplots to visualize the distribution of each numeric variable by species.

# Boxplot for Sepal Length by Species
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  labs(title = "Boxplot of Sepal Length by Species", x = "Species", y = "Sepal Length")

# Boxplot for Sepal Width by Species
ggplot(iris, aes(x = Species, y = Sepal.Width, fill = Species)) +
  geom_boxplot() +
  labs(title = "Boxplot of Sepal Width by Species", x = "Species", y = "Sepal Width")

# Boxplot for Petal Length by Species
ggplot(iris, aes(x = Species, y = Petal.Length, fill = Species)) +
  geom_boxplot() +
  labs(title = "Boxplot of Petal Length by Species", x = "Species", y = "Petal Length")

# Boxplot for Petal Width by Species
ggplot(iris, aes(x = Species, y = Petal.Width, fill = Species)) +
  geom_boxplot() +
  labs(title = "Boxplot of Petal Width by Species", x = "Species", y = "Petal Width")

Explanation

  • We use ggplot2 to create boxplots for each numeric variable by species. The geom_boxplot function is used to create the boxplots, and the labs function is used to add titles and labels.

Step 4: Statistical Analysis

ANOVA Test

We will perform an ANOVA test to determine if there are significant differences in the means of the numeric variables across the different species.

# ANOVA for Sepal Length
anova_sepal_length <- aov(Sepal.Length ~ Species, data = iris)
summary(anova_sepal_length)

# ANOVA for Sepal Width
anova_sepal_width <- aov(Sepal.Width ~ Species, data = iris)
summary(anova_sepal_width)

# ANOVA for Petal Length
anova_petal_length <- aov(Petal.Length ~ Species, data = iris)
summary(anova_petal_length)

# ANOVA for Petal Width
anova_petal_width <- aov(Petal.Width ~ Species, data = iris)
summary(anova_petal_width)

Explanation

  • We use the aov function to perform ANOVA tests for each numeric variable. The summary function is used to display the results of the ANOVA tests.

Conclusion

In this case study, we have successfully:

  1. Imported and cleaned the Iris dataset.
  2. Performed exploratory data analysis (EDA) using descriptive statistics and visualizations.
  3. Conducted statistical tests to derive insights.

By completing this case study, you have applied various data analysis techniques to a real-world dataset, reinforcing your understanding and skills in R programming. In the next case study, we will focus on machine learning techniques.

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