How to Use sum() Function in R

The sum() function in R is used to calculate the sum of all the values present in its arguments.

Syntax

sum(x, na.rm = FALSE)

Parameters

1. x: It is a numeric vector or data frame.
2. na.rm: whether NA should be removed; if not, NA will be returned.

Example 1: Using sum() with vector

rv <- c(11, 19, 21, 18, 46)

# Calculates the sum of the values 
sum(rv)

Output

[1] 115

You can see that we calculated the sum of all vector elements using the sum() function.

Example 2: Passing the NA value

You can’t get the desired output if NA is present in the vector elements.

vec <- c(11, 19, 21, NA, 46)

# Calculates the sum of rv vector
sum(vec)

Output

[1] NA

Well, we did not expect NA output. However, sometimes your dataset may contain ‘NA” values, i.e., ‘Not Available’. But we can handle this issue by bypassing na.rm = TRUE.

vec <- c(11, 19, 21, NA, 46)

# Calculates the sum of the values 
sum(vec, na.rm = TRUE)

Output

[1] 97

Example 3: Using sum() function with complete data frame

df <- data.frame(
  col1 = c(1, 2, 3),
  col2 = c(4, 5, 6),
  col3 = c(7, 8, 9)
)

sum(df)

Output

[1] 45

We calculated the sum of all the elements in the data frame using the sum(df) function, adding the values in all three columns.

Example 4: Adding values of a specific column

df <- data.frame(
  col1 = c(1, 2, 3),
  col2 = c(4, 5, 6),
  col3 = c(7, 8, 9)
)

sum(df$col2)

To access the column, use $ and then the column name. Here we are finding the sum of enrollno column values. See the below output.

[1] 15

Example 5: Adding values of multiple columns

To get the sum of multiple columns, use the “mapply()” function in combination with the sum() function.

df <- data.frame(
  col1 = c(1, 2, 3),
  col2 = c(4, 5, 6),
  col3 = c(7, 8, 9)
)

mapply(sum, df[, c(2, 3)]) 

Output

col2   col3
 15     24

That’s it.

Krunal Lathiya

Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.