Factor in analysis and plotting in R Programming - Mini Project: Build & Apply

Factor in analysis and plotting

📖 Scenario: You are working with a small dataset of fruit preferences collected from a group of people. Each person chose their favorite fruit from a list. You want to analyze how many people chose each fruit and then create a simple bar plot to visualize the counts.

🎯 Goal: Build a program that creates a factor variable for fruit preferences, counts how many times each fruit was chosen, and then plots these counts in a bar chart.

📋 What You'll Learn

Create a vector called fruits with the exact values: "apple", "banana", "apple", "orange", "banana", "banana", "apple".

Convert the fruits vector into a factor called fruit_factor.

Use the table() function on fruit_factor to count the number of each fruit and store it in fruit_counts.

Create a bar plot of fruit_counts with the title "Fruit Preferences".

💡 Why This Matters

🌍 Real World

Analyzing survey data where answers are categories, like favorite fruits, colors, or brands.

💼 Career

Data analysts and scientists often work with categorical data and need to summarize and visualize it clearly.

1

Create the fruit vector

Create a character vector called fruits with these exact values: "apple", "banana", "apple", "orange", "banana", "banana", "apple".

R Programming

# Create the fruits vector with the exact values
# Your code here

Hint

Use the c() function to combine the fruit names into a vector.

2

Convert to a factor

Convert the fruits vector into a factor called fruit_factor using the factor() function.

R Programming

fruits <- c("apple", "banana", "apple", "orange", "banana", "banana", "apple")
# Convert fruits to a factor called fruit_factor
# Your code here

Hint

Use fruit_factor <- factor(fruits) to convert the vector to a factor.

3

Count the factor levels

Use the table() function on fruit_factor to count how many times each fruit appears. Store the result in a variable called fruit_counts.

R Programming

fruits <- c("apple", "banana", "apple", "orange", "banana", "banana", "apple")
fruit_factor <- factor(fruits)
# Count the number of each fruit and store in fruit_counts
# Your code here

Hint

Use fruit_counts <- table(fruit_factor) to get counts of each fruit.

4

Plot the fruit counts

Create a bar plot of fruit_counts using the barplot() function. Add the title "Fruit Preferences" using the main argument.

R Programming

fruits <- c("apple", "banana", "apple", "orange", "banana", "banana", "apple")
fruit_factor <- factor(fruits)
fruit_counts <- table(fruit_factor)
# Create a bar plot of fruit_counts with title "Fruit Preferences"
# Your code here

Hint

Use barplot(fruit_counts, main = "Fruit Preferences") to create the plot with a title.

Practice

1. What is the main purpose of using a factor in R when analyzing data?

easy

A. To convert text data into categories for easier analysis and plotting

B. To perform mathematical calculations on numeric data

C. To store large datasets efficiently

D. To create complex machine learning models

Solution

1. Step 1: Understand what factors represent

   Factors in R are used to represent categorical data, turning text into categories.

2. Step 2: Recognize the benefit of factors

   Using factors helps in organizing data for analysis and plotting by grouping similar values.

3. Final Answer:

   To convert text data into categories for easier analysis and plotting -> Option A

4. Quick Check:

   Factors = Categories [OK]

Hint: Factors group text into categories for analysis and plots [OK]

Common Mistakes:

• Thinking factors perform calculations
• Confusing factors with numeric vectors
• Assuming factors store data efficiently

2. Which of the following is the correct way to create a factor with levels ordered as "Low", "Medium", "High" in R?

easy

A. factor(x, order = c("Low", "Medium", "High"))

B. factor(x, levels = c("Low", "Medium", "High"))

C. factor(x, labels = c("Low", "Medium", "High"))

D. factor(x, sort = c("Low", "Medium", "High"))

Solution

1. Step 1: Recall factor syntax for setting levels

   The correct argument to set the order of categories is levels.

2. Step 2: Check the options

   Only factor(x, levels = c("Low", "Medium", "High")) uses levels correctly to order the factor levels.

3. Final Answer:

   factor(x, levels = c("Low", "Medium", "High")) -> Option B

4. Quick Check:

   Use levels= to order categories [OK]

Hint: Use levels= to set factor order, not order= or sort= [OK]

Common Mistakes:

• Using order= instead of levels=
• Confusing labels= with levels=
• Trying to sort levels with sort= argument

3. Consider the following R code:

x <- c("apple", "banana", "apple", "cherry")
f <- factor(x, levels = c("banana", "apple", "cherry"))
summary(f)

What will be the output of summary(f)?

medium

A. banana: 1apple: 2cherry: 1

B. apple: 2banana: 1cherry: 1

C. apple: 2cherry: 1banana: 1

D. cherry: 1banana: 1apple: 2

Solution

1. Step 1: Understand factor levels order

   The factor levels are set as "banana", "apple", "cherry" which determines the order in summary output.

2. Step 2: Count occurrences of each level

   In x, "banana" appears once, "apple" twice, and "cherry" once.

3. Final Answer:

   banana: 1
   apple: 2
   cherry: 1 -> Option A

4. Quick Check:

   Summary shows counts in levels order [OK]

Hint: Summary counts follow factor levels order [OK]

Common Mistakes:

• Assuming summary sorts alphabetically
• Ignoring levels order in summary
• Counting values incorrectly

4. The following R code is intended to create a factor with levels "Small", "Medium", "Large" and plot a bar chart. However, the plot shows the levels in alphabetical order instead of the specified order.

sizes <- c("Medium", "Large", "Small", "Medium")
f <- factor(sizes, levels = c("Small", "Medium", "Large"))
barplot(table(f))

What is the likely cause of this issue?

medium

A. The factor levels are correct, but barplot plots in alphabetical order by default

B. The barplot function ignores factor levels and sorts alphabetically

C. The factor was not created with the correct levels argument

D. The table function converts factor to character and loses order

Solution

1. Step 1: Check factor creation syntax

   The factor is created with levels specified correctly, so this is not the issue.

2. Step 2: Understand barplot and table behavior

   The table() function converts the factor to a named vector which may lose the factor level order, causing barplot to plot in alphabetical order.

3. Step 3: Identify common mistake

   Using table() directly on factor can lose order; using barplot with factor levels preserved requires careful handling.

4. Final Answer:

   The table function converts factor to character and loses order -> Option D

5. Quick Check:

   Use methods preserving factor order for plotting [OK]

Hint: Always set levels when creating factor to control plot order [OK]

Common Mistakes:

• Assuming barplot ignores factor levels
• Not setting levels argument in factor()
• Using character vector instead of factor in table()

5. You have a dataset with a column rating containing values "Poor", "Average", "Good", and "Excellent". You want to create a factor that orders these levels from worst to best and then plot a bar chart showing the counts in this order. Which R code correctly achieves this?

hard

A. rating_factor <- factor(rating, levels = sort(unique(rating)))
barplot(table(rating_factor))

B. rating_factor <- factor(rating)
barplot(table(rating_factor))

C. rating_factor <- factor(rating, levels = c("Poor", "Average", "Good", "Excellent"))
barplot(table(rating_factor))

D. rating_factor <- factor(rating, labels = c("Poor", "Average", "Good", "Excellent"))
barplot(table(rating_factor))

Solution

1. Step 1: Set factor levels in desired order

   To order from worst to best, levels must be explicitly set as "Poor", "Average", "Good", "Excellent".

2. Step 2: Plot counts respecting factor order

   Using table() on the factor and plotting with barplot() will show bars in factor level order.

3. Final Answer:

   rating_factor <- factor(rating, levels = c("Poor", "Average", "Good", "Excellent")) barplot(table(rating_factor)) -> Option C

4. Quick Check:

   Explicit levels order controls plot order [OK]

Hint: Set levels explicitly to control order in plots [OK]

Common Mistakes:

• Not setting levels, so order is alphabetical
• Using labels= instead of levels= to order
• Sorting unique values instead of custom order