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**

**x:**It is a numeric vector or data frame.**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 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.