Numeric type in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of numeric division and type check

What is the output of this R code snippet?

R Programming

x <- 5 / 2
typeof(x)

A "double"

B "integer"

C "numeric"

D "complex"

💡 Hint

In R, division usually results in a floating-point number.

In R, the division operator '/' returns a double precision number, so typeof(x) returns "double".

❓ Predict Output

intermediate

Output of integer coercion with as.integer()

What is the output of this R code?

R Programming

y <- as.integer(3.7)
y

A 3

B 3.7

C 4

D NA

💡 Hint

as.integer() truncates the decimal part.

The as.integer() function truncates the decimal part and returns the integer portion, so 3.7 becomes 3.

🔧 Debug

advanced

Type coercion in numeric vector creation

What is the output of this R code?

R Programming

z <- c(1, 2, "3", 4)
typeof(z)

A "numeric"

B "character"

C Error: non-numeric argument to binary operator

D "integer"

💡 Hint

Mixing numbers and strings in a vector causes coercion.

When combining numbers and strings in a vector, R coerces all elements to character type, so typeof(z) returns "character".

❓ Predict Output

advanced

Result of numeric overflow in R

What is the output of this R code?

R Programming

big_num <- 1e308 * 10
big_num

A 1e309

B NaN

C 0

D Inf

💡 Hint

Multiplying very large numbers can exceed the maximum representable value.

Multiplying 1e308 by 10 exceeds the largest double precision number, resulting in Inf (infinity).

🧠 Conceptual

expert

Understanding numeric precision in R

Which statement best describes numeric precision in R for double type numbers?

A Double precision numbers in R are stored as integers internally.

B Double precision numbers in R can represent all decimal numbers exactly.

C Double precision numbers in R have about 15-16 decimal digits of precision.

D Double precision numbers in R have unlimited precision.

💡 Hint

Think about how computers store floating-point numbers.

Double precision floating-point numbers in R follow IEEE 754 standard and have about 15-16 digits of decimal precision, but cannot represent all decimals exactly.

Practice

1. Which of the following is TRUE about numeric types in R?

easy

A. Numeric and integer types are the same in R.

B. Integer type stores decimal numbers by default.

C. Numeric type stores decimal numbers by default.

D. You must always use quotes to create numeric values.

Solution

1. Step 1: Understand numeric and integer types in R

   In R, numeric type stores decimal numbers by default, while integer type stores whole numbers with an L suffix.

2. Step 2: Evaluate each option

   Numeric type stores decimal numbers by default correctly states that numeric stores decimals by default. Options A, B, and C are incorrect based on R's type system.

3. Final Answer:

   Numeric type stores decimal numbers by default. -> Option C

4. Quick Check:

   Numeric = decimals [OK]

Hint: Remember: numeric = decimals, integer = whole numbers [OK]

Common Mistakes:

• Confusing integer and numeric types
• Thinking quotes are needed for numbers
• Assuming numeric and integer are identical

2. Which of the following is the correct way to create an integer value 5 in R?

easy

A. x <- 5L

B. x <- "5"

C. x <- 5

D. x <- as.integer("5L")

Solution

1. Step 1: Recall integer creation syntax in R

   In R, to create an integer literal, you add L after the number, e.g., 5L.

2. Step 2: Check each option

   x <- 5L uses 5L which is correct. x <- 5 creates a numeric (decimal) 5, not integer. x <- "5" creates a string. x <- as.integer("5L") fails to convert the string "5L" to integer due to the L suffix, resulting in NA.

3. Final Answer:

   x <- 5L -> Option A

4. Quick Check:

   Integer literal = number + L [OK]

Hint: Use L suffix to make integers, else numeric by default [OK]

Common Mistakes:

• Using plain numbers for integers
• Confusing strings with numbers
• Using as.integer on decimals expecting exact integer

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

x <- as.numeric("3.14")
typeof(x)

medium

A. "double"

B. "numeric"

C. "integer"

D. "character"

Solution

1. Step 1: Understand as.numeric() conversion

   as.numeric() converts a string to a numeric type, which in R is stored as double precision floating point.

2. Step 2: Check typeof() result for numeric

   typeof() returns "double" for numeric values with decimals in R.

3. Final Answer:

   "double" -> Option A

4. Quick Check:

   as.numeric() returns double type [OK]

Hint: as.numeric() returns double type, not integer [OK]

Common Mistakes:

• Expecting integer type from as.numeric()
• Confusing character and numeric types
• Thinking numeric means integer

4. Identify the error in this R code:

y <- 10L
z <- "abc"
sum <- y + z

medium

A. Error because sum is a reserved word

B. No error, sum is 10

C. Error because 10L is not numeric

D. Error because "abc" cannot convert to numeric

Solution

1. Step 1: Check addition of integer and character

   z <- "abc" creates a character value. Adding integer y (10L) and character z causes an error because character cannot be converted to numeric for arithmetic operations.

2. Step 2: Confirm the error

   The error is 'non-numeric argument to binary operator' since "abc" cannot convert to numeric.

3. Final Answer:

   Error because "abc" cannot convert to numeric -> Option D

4. Quick Check:

   String in arithmetic causes error [OK]

Hint: Non-numeric strings to as.numeric() produce NA [OK]

Common Mistakes:

• Trying to perform arithmetic on strings
• Thinking 10L is not numeric
• Assuming sum is reserved and causes error

5. You have a character vector c("1", "2.5", "3L", "four"). Which R code correctly converts only the valid numeric values to numeric type, ignoring invalid ones?

hard

A. as.numeric(c("1", "2.5", "3L", "four"))

B. as.numeric(gsub("L", "", c("1", "2.5", "3L", "four")))

C. suppressWarnings(as.numeric(c("1", "2.5", "3L", "four")))

D. as.integer(c("1", "2.5", "3L", "four"))

Solution

1. Step 1: Understand the problem with "3L" and "four"

   "3L" is not directly numeric because of the L suffix as a string; "four" is invalid.

2. Step 2: Use gsub to remove "L" and convert

   Removing "L" from strings allows as.numeric() to convert "3" correctly; "four" becomes NA.

3. Step 3: Check options

   as.numeric(gsub("L", "", c("1", "2.5", "3L", "four"))) removes "L" before conversion, correctly converting valid numbers and producing NA for "four". Others either do not handle "3L" or convert incorrectly.

4. Final Answer:

   as.numeric(gsub("L", "", c("1", "2.5", "3L", "four"))) -> Option B

5. Quick Check:

   Remove non-numeric chars before conversion [OK]

Hint: Remove non-numeric chars before as.numeric() [OK]

Common Mistakes:

• Trying to convert strings with letters directly
• Using as.integer on decimals
• Ignoring warnings from invalid conversions