ANOVA in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a one-way ANOVA test using the built-in aov function.

R Programming

result <- aov([1] ~ group, data = data_frame)

Drag options to blanks, or click blank then click option'

Agroup

Bvalue

Cdata_frame

Danova

Attempts:

3 left

💡 Hint

Common Mistakes

Using the group variable as the dependent variable.

Using the data frame name instead of the variable.

The aov function requires a formula where the dependent variable is on the left side of ~. Here, value is the dependent variable.

2fill in blank

medium

Complete the code to display the summary of the ANOVA test results.

R Programming

summary([1])

Drag options to blanks, or click blank then click option'

Agroup

Bdata_frame

Cresult

Daov

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the data frame instead of the ANOVA result.

Passing the group variable instead of the model.

The summary function is used to show the ANOVA table from the aov result stored in result.

3fill in blank

hard

Fix the error in the code to correctly perform ANOVA on the variable score grouped by treatment.

R Programming

anova_result <- aov(score [1] treatment, data = df)

Drag options to blanks, or click blank then click option'

A*

B-

C+

D~

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' or '-' instead of '~' in the formula.

Omitting the formula operator.

The formula in aov requires the tilde ~ to separate the dependent and independent variables.

4fill in blank

hard

Fill both blanks to create a dictionary of means for each group and filter groups with mean greater than 5.

R Programming

group_means <- tapply(df$[1], df$[2], mean)
filtered_means <- group_means[group_means > 5]

Drag options to blanks, or click blank then click option'

Ascore

Btreatment

Cgroup

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the variable and group names.

Using incorrect variable names.

tapply applies the mean function to score grouped by treatment. Then we filter means greater than 5.

5fill in blank

hard

Fill all three blanks to create a boxplot of response by condition with custom colors and title.

R Programming

boxplot([1] ~ [2], data = experiment_data, col = [3], main = "Response by Condition")

Drag options to blanks, or click blank then click option'

Aresponse

Bcondition

Cc("red", "blue", "green")

Dc("green", "yellow", "purple")

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping response and condition in the formula.

Using invalid color vectors.

The boxplot formula is response ~ condition. The colors are set with a vector of color names.

Practice

1. What is the main purpose of using aov() function in R?

easy

A. To create a scatter plot of data

B. To calculate correlation between two variables

C. To perform ANOVA and test if group means differ

D. To compute the median of a numeric vector

Solution

1. Step 1: Understand the function purpose

   The aov() function in R is designed to perform Analysis of Variance (ANOVA).

2. Step 2: Identify what ANOVA tests

   ANOVA tests if the means of different groups are significantly different from each other.

3. Final Answer:

   To perform ANOVA and test if group means differ -> Option C

4. Quick Check:

   aov() = test group means difference [OK]

Hint: Remember: aov() is for comparing group averages [OK]

Common Mistakes:

• Confusing aov() with correlation functions
• Thinking aov() creates plots
• Using aov() to calculate medians

2. Which of the following is the correct syntax to run a one-way ANOVA in R on variable score grouped by group in data frame df?

easy

A. anova(score, group, data = df)

B. aov(score ~ group, data = df)

C. aov(data = df, score ~ group)

D. anova(df$score ~ df$group)

Solution

1. Step 1: Recall correct aov() syntax

   The correct syntax is aov(response ~ factor, data = dataframe).

2. Step 2: Match syntax to options

   aov(score ~ group, data = df) matches this exactly: aov(score ~ group, data = df).

3. Final Answer:

   aov(score ~ group, data = df) -> Option B

4. Quick Check:

   Correct aov() syntax = aov(score ~ group, data = df) [OK]

Hint: Use formula: response ~ group, with data= argument [OK]

Common Mistakes:

• Swapping formula and data arguments
• Using anova() instead of aov() for model fitting
• Incorrect formula order or missing data argument

3. What will be the output of the following R code?

df <- data.frame(group = rep(c('A', 'B'), each=3), score = c(5,6,7,8,9,10))
model <- aov(score ~ group, data = df)
summary(model)

medium

A. Shows significant difference between groups with low p-value

B. Shows no significant difference with high p-value

C. Returns an error due to incorrect data frame

D. Prints the raw data frame without analysis

Solution

1. Step 1: Understand the data and groups

   Group A has scores 5,6,7; Group B has 8,9,10. Group B scores are clearly higher.

2. Step 2: Interpret ANOVA summary output

   Since group means differ noticeably, summary(model) will show a low p-value indicating significant difference.

3. Final Answer:

   Shows significant difference between groups with low p-value -> Option A

4. Quick Check:

   Different group means = significant ANOVA result [OK]

Hint: If group means differ clearly, expect low p-value [OK]

Common Mistakes:

• Assuming no difference despite clear mean gap
• Expecting error from correct code
• Confusing summary output with raw data print

4. Identify the error in this R code snippet for ANOVA:

data <- data.frame(treatment = c('X','Y','X','Y'), value = c(10, 15, 12, 14))
result <- aov(value, treatment, data=data)
summary(result)

medium

A. Variable names are not quoted properly

B. Data frame is missing required columns

C. summary() cannot be used on aov objects

D. Incorrect argument order in aov() function

Solution

1. Step 1: Check aov() function usage

   aov() requires a formula as first argument, not separate variables.

2. Step 2: Identify correct syntax

   The correct call is aov(value ~ treatment, data=data), not aov(value, treatment, data=data).

3. Final Answer:

   Incorrect argument order in aov() function -> Option D

4. Quick Check:

   aov() needs formula first [OK]

Hint: Always use formula: response ~ group in aov() [OK]

Common Mistakes:

• Passing variables separately instead of formula
• Misusing summary() on wrong object types
• Incorrect data frame structure assumptions

5. You have a data frame df with three groups in column treatment and numeric values in response. You want to check if the group means differ significantly and then perform a post-hoc test to find which groups differ. Which sequence of R commands is correct?

hard

A. model <- aov(response ~ treatment, data=df)
summary(model)
TukeyHSD(model)

B. summary(aov(response, treatment, data=df))
posthoc <- TukeyHSD(response)

C. anova(response ~ treatment, data=df)
posthoc <- aov(response ~ treatment, data=df)

D. model <- aov(treatment ~ response, data=df)
summary(model)
posthoc <- TukeyHSD(model)

Solution

1. Step 1: Fit ANOVA model correctly

   Use aov(response ~ treatment, data=df) to fit the model.

2. Step 2: Summarize and perform post-hoc test

   Use summary(model) to check significance, then TukeyHSD(model) for pairwise group comparisons.

3. Final Answer:

   model <- aov(response ~ treatment, data=df) summary(model) TukeyHSD(model) -> Option A

4. Quick Check:

   ANOVA then TukeyHSD = model <- aov(response ~ treatment, data=df) summary(model) TukeyHSD(model) [OK]

Hint: Run aov(), then summary(), then TukeyHSD() for post-hoc [OK]

Common Mistakes:

• Using wrong formula order in aov()
• Trying TukeyHSD on raw data instead of model
• Confusing anova() with aov() usage