ANOVA in R Programming - Mini Project: Build & Apply

Performing ANOVA Analysis in R

📖 Scenario: You are a data analyst working with a company that wants to understand if different diets affect weight loss differently. You have collected weight loss data from three groups following different diets.

🎯 Goal: You will create a dataset, set up the factor variable, perform an ANOVA test to compare the groups, and finally display the ANOVA summary results.

📋 What You'll Learn

Create a data frame called weight_loss with two columns: Diet and Loss with exact values

Create a factor variable diet_factor from the Diet column

Perform an ANOVA test using aov() with Loss as response and diet_factor as predictor, store in anova_result

Print the summary of anova_result to display the ANOVA table

💡 Why This Matters

🌍 Real World

ANOVA is used in many fields like medicine, agriculture, and marketing to compare multiple groups and see if their averages differ significantly.

💼 Career

Data analysts and scientists use ANOVA to make data-driven decisions and understand the impact of different treatments or conditions.

1

Create the dataset

Create a data frame called weight_loss with two columns: Diet and Loss. Use these exact values: Diet = c('A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C') and Loss = c(3.2, 2.8, 3.6, 4.1, 4.5, 4.0, 5.2, 5.5, 5.1).

R Programming

# Create the data frame weight_loss with Diet and Loss columns
# Your code here

Need a hint?

Use data.frame() to create a table with columns named Diet and Loss.

2

Create a factor variable for Diet

Create a factor variable called diet_factor from the Diet column of the weight_loss data frame.

R Programming

weight_loss <- data.frame(
  Diet = c('A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'),
  Loss = c(3.2, 2.8, 3.6, 4.1, 4.5, 4.0, 5.2, 5.5, 5.1)
)
# Create diet_factor as a factor from weight_loss$Diet
# Your code here

Need a hint?

Use the factor() function to convert the Diet column into a factor.

3

Perform ANOVA test

Use the aov() function to perform an ANOVA test with Loss as the response variable and diet_factor as the predictor. Store the result in a variable called anova_result.

R Programming

weight_loss <- data.frame(
  Diet = c('A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'),
  Loss = c(3.2, 2.8, 3.6, 4.1, 4.5, 4.0, 5.2, 5.5, 5.1)
)
diet_factor <- factor(weight_loss$Diet)
# Perform ANOVA and store in anova_result
# Your code here

Need a hint?

Use the formula Loss ~ diet_factor inside aov() and specify data = weight_loss.

4

Display ANOVA summary

Print the summary of the ANOVA result stored in anova_result using the summary() function.

R Programming

weight_loss <- data.frame(
  Diet = c('A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'),
  Loss = c(3.2, 2.8, 3.6, 4.1, 4.5, 4.0, 5.2, 5.5, 5.1)
)
diet_factor <- factor(weight_loss$Diet)
anova_result <- aov(Loss ~ diet_factor, data = weight_loss)
# Print the summary of anova_result
# Your code here

Need a hint?

Use print(summary(anova_result)) to show the ANOVA table.