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.