The colSums() function in R is **“used to calculate the sum of each column in a data frame or matrix”**.

**Syntax**

`colSums(x, na.rm = FALSE)`

**Parameters**

**x:**It is an array of two or more dimensions containing numeric, complex, integer, or logical values or a numeric data frame.**na.rm**: It is logical. Should missing values (including NaN) be omitted from the calculations?

**Example 1: Use colSums() with Data Frame**

```
df <- data.frame(
run1 = c(11, 31, 23, 24, 52),
run2 = c(19, 21, 25, 32, 22),
run3 = c(21, 31, 62, 62, 82),
run4 = c(18, 11, 22, 14, 29)
)
colSums(df)
```

**Output**

```
run1 run2 run3 run4
141 119 258 94
```

**Example 2: Use the colSums() function with NA Values in Data Frame**

You can exclude **NA** values from the data frame by passing the **na.rm = TRUE** parameter to the colSums() function.

```
df <- data.frame(
run1 = c(11, 31, NA, 24, 52),
run2 = c(19, 21, 25, NA, 22),
run3 = c(21, NA, 62, 62, 82),
run4 = c(NA, 11, 22, 14, 29)
)
colSums(df, na.rm = TRUE)
```

**Output**

```
run1 run2 run3 run4
118 87 227 76
```

**Example 3: Use colSums() with specific columns**

You can use the colSums() function to find the sum of the specific columns of a data frame.

```
df <- data.frame(
run1 = c(11, 31, 23, 24, 52),
run2 = c(19, 21, 25, 32, 22),
run3 = c(21, 31, 62, 62, 82),
run4 = c(18, 11, 22, 14, 29)
)
colSums(df[, c(2, 4)])
```

**Output**

```
run2 run4
119 94
```

**Example 4: Use the colSums() function on Matrix**

You can use the colSums() function to calculate the sum of column values of the matrix.

```
mtrx <- matrix(rep(1:4), 2, 2)
mtrx
cat("The sum of columns is: ", "\n")
colSums(mtrx)
```

**Output**

```
[,1] [,2]
[1,] 1 3
[2,] 2 4
The sum of columns is:
[1] 3 7
```

**Example 5: Calculating the sum of column values in Data Set**

You can use the inbuilt R dataset like **ChickWeight** and calculate the sum of its column values. But first, let’s get the snapshot of the **ChickWeight** dataset using the **head() **function.

`head(USArrests, 5)`

**Output**

```
Murder Assault UrbanPop Rape
Alabama 13.2 236 58 21.2
Alaska 10.0 263 48 44.5
Arizona 8.1 294 80 31.0
Arkansas 8.8 190 50 19.5
California 9.0 276 91 40.6
```

We will use the colSums() function to calculate the sum of **Murder, Assult, UrbanPop, **and **Rape **column values.

`colSums(USArrests)`

**Output**

```
Murder Assault UrbanPop Rape
389.4 8538.0 3277.0 1061.6
```

That’s it.

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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.