Correlation analysis in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the correlation between two numeric vectors x and y.

R Programming

correlation <- cor(x, y, method = [1])

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

A"pearson"

B"spearman"

C"kendall"

D"linear"

Attempts:

3 left

💡 Hint

Common Mistakes

Using an invalid method name like "linear" causes an error.

Confusing method names with other correlation types.

The default and most common method for correlation is "pearson" which measures linear correlation.

2fill in blank

medium

Complete the code to calculate the correlation matrix of the dataframe df.

R Programming

cor_matrix <- cor([1])

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

Adf

Bdata

Cmatrix

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list instead of a dataframe causes an error.

Passing an undefined variable name.

The function cor() expects a numeric matrix or dataframe. Here, df is the dataframe containing numeric columns.

3fill in blank

hard

Fix the error in the code to calculate the Spearman correlation between vectors a and b.

R Programming

correlation <- cor(a, b, method = [1])

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

ASpearman

Bspearman

C"spearman"

DSPEARMAN

Attempts:

3 left

💡 Hint

Common Mistakes

Not using quotes around the method name causes an error.

Using incorrect capitalization without quotes.

The method argument requires the method name as a string, so it must be in quotes.

4fill in blank

hard

Fill both blanks to calculate the correlation matrix of numeric columns only from df.

R Programming

numeric_df <- df[sapply(df, [1])]
cor_matrix <- cor(numeric_df, method = [2])

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

Ais.numeric

Bis.character

C"pearson"

D"kendall"

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting non-numeric columns causes errors in cor().

Using method names without quotes.

Use is.numeric to select numeric columns, and "pearson" as the correlation method for linear correlation.

5fill in blank

hard

Fill all three blanks to calculate the correlation between x and y using Spearman method and round the result to 3 decimals.

R Programming

result <- round(cor([1], [2], method = [3]), 3)

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

Ax

By

C"spearman"

D"pearson"

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect variable names.

Not quoting the method name causes errors.

Use variables x and y as inputs, and specify the method as "spearman" in quotes. Then round the result to 3 decimal places.

Practice

1. What does the cor() function in R calculate?

easy

A. The correlation coefficient between two numeric vectors

B. The sum of two numeric vectors

C. The difference between two numeric vectors

D. The product of two numeric vectors

Solution

1. Step 1: Understand the purpose of cor()

   The cor() function is designed to calculate how two numeric vectors move together, which is called correlation.

2. Step 2: Identify the correct description

   Correlation coefficient measures the strength and direction of a linear relationship between two numeric vectors.

3. Final Answer:

   The correlation coefficient between two numeric vectors -> Option A

4. Quick Check:

   cor() calculates correlation [OK]

Hint: Remember: cor() finds how two numbers move together [OK]

Common Mistakes:

• Confusing correlation with sum or difference
• Thinking cor() calculates causation
• Using cor() on non-numeric data

2. Which of the following is the correct syntax to calculate correlation between vectors x and y in R?

easy

A. correlation_coefficient(x, y)

B. cor(x, y)

C. correlate(x, y)

D. correlation(x, y)

Solution

1. Step 1: Recall the R function name

   The correct function to calculate correlation in R is cor().

2. Step 2: Check the syntax

   The function takes two numeric vectors as arguments: cor(x, y).

3. Final Answer:

   cor(x, y) -> Option B

4. Quick Check:

   Use cor() with two vectors [OK]

Hint: Use exactly cor(x, y) to get correlation [OK]

Common Mistakes:

• Using incorrect function names like correlation()
• Adding extra arguments unnecessarily
• Using wrong argument order

3. What is the output of the following R code?

x <- c(1, 2, 3, 4, 5)
y <- c(2, 4, 6, 8, 10)
cor(x, y)

medium

A. 1

B. -1

C. 0

D. NA

Solution

1. Step 1: Analyze the vectors

   Vector y is exactly twice x for each element, so they have a perfect positive linear relationship.

2. Step 2: Calculate correlation

   Perfect positive linear relationship means correlation coefficient is 1.

3. Final Answer:

   1 -> Option A

4. Quick Check:

   Perfect positive correlation = 1 [OK]

Hint: If one vector is a perfect multiple of another, cor = 1 [OK]

Common Mistakes:

• Confusing correlation with sum or mean
• Expecting 0 when vectors are related
• Misreading negative correlation

4. Identify the error in this R code for correlation:

x <- c(1, 2, 3)
y <- c(4, 5)
cor(x, y)

medium

A. cor() function is misspelled

B. Vectors contain non-numeric data

C. Vectors have different lengths causing an error

D. No error, code runs fine

Solution

1. Step 1: Check vector lengths

   Vector x has length 3, vector y has length 2, so lengths differ.

2. Step 2: Understand cor() requirements

   cor() requires vectors of equal length to compute correlation.

3. Final Answer:

   Vectors have different lengths causing an error -> Option C

4. Quick Check:

   cor() needs equal length vectors [OK]

Hint: Vectors must be same length for cor() [OK]

Common Mistakes:

• Ignoring vector length mismatch
• Assuming cor() auto-adjusts lengths
• Thinking cor() works with non-numeric data

5. You have two numeric vectors a and b with some missing values (NA). Which is the correct way to calculate correlation ignoring missing values in R?

hard

A. cor(a, b, removeNA = TRUE)

B. cor(a, b, na.rm = TRUE)

C. cor(a, b, method = "ignoreNA")

D. cor(a, b, use = "complete.obs")

Solution

1. Step 1: Understand handling of missing values in cor()

   The cor() function uses the use argument to specify how to handle missing values.

2. Step 2: Identify correct argument value

   Setting use = "complete.obs" tells R to use only pairs without any NA values.

3. Final Answer:

   cor(a, b, use = "complete.obs") -> Option D

4. Quick Check:

   Use use = "complete.obs" to ignore NA [OK]

Hint: Use use="complete.obs" to skip NA pairs [OK]

Common Mistakes:

• Using na.rm which is not an argument for cor()
• Using incorrect argument names like removeNA
• Assuming method argument handles NA