# Product of Vector & Data Frame Rows & Columns in R (2 Examples)

In this R tutorial you’ll learn how to compute the product of a vector, and the rows and columns of a data frame.

## Example Data

```data(iris) # Example data head(iris) # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 setosa # 2 4.9 3.0 1.4 0.2 setosa # 3 4.7 3.2 1.3 0.2 setosa # 4 4.6 3.1 1.5 0.2 setosa # 5 5.0 3.6 1.4 0.2 setosa # 6 5.4 3.9 1.7 0.4 setosa```

## Example 1: Get Product of All Vector Elements Using prod() Function

```prod(iris\$Sepal.Length) # Product of column vector # [1] 2.25744e+114```

## Example 2: Get Product of Numeric Data Frame Rows & Columns

```apply(iris[ , 1:4], 2, prod) # Product of all columns # Sepal.Length Sepal.Width Petal.Length Petal.Width # 2.257440e+114 1.390618e+72 3.522857e+76 5.945429e-12```
```apply(iris[ , 1:4], 1, prod) # Product of all rows # [1] 4.9980 4.1160 3.9104 4.2780 5.0400 14.3208 6.5688 5.1000 # [9] 3.5728 2.2785 5.9940 5.2224 2.0160 1.4190 5.5680 15.0480 # [17] 10.9512 7.4970 11.0466 8.7210 6.2424 11.3220 3.3120 14.3055 # [25] 6.2016 4.8000 10.8800 5.4600 4.9504 4.8128 4.7616 11.0160 # [33] 3.1980 6.4680 4.5570 3.8400 5.0050 2.4696 3.4320 5.2020 # [41] 6.8250 4.0365 3.6608 16.8000 14.7288 6.0480 6.2016 4.1216 # [49] 5.8830 4.6200 147.3920 138.2400 157.2165 65.7800 125.5800 93.3660 # [57] 156.3408 38.8080 114.4572 76.6584 35.0000 111.5100 52.8000 116.4002 # [65] 76.0032 127.9432 113.4000 64.2060 92.0700 60.0600 163.1232 88.8160 # [73] 115.7625 96.3312 103.7504 121.9680 127.9488 170.8500 117.4500 51.8700 # [81] 55.1760 48.8400 73.2888 132.1920 109.3500 146.8800 146.4285 82.8828 # [89] 89.5440 71.5000 75.5040 117.8520 72.3840 37.9500 82.5552 86.1840 # [97] 90.2538 100.5082 42.0750 85.0668 311.8500 151.7454 263.9070 184.1616 # [105] 248.8200 316.0080 93.7125 240.0678 174.8700 395.2800 212.1600 174.0096 # [113] 235.6200 142.5000 198.7776 249.6512 193.0500 431.2924 317.7174 99.0000 # [121] 289.4688 153.6640 288.9040 150.0282 264.6567 248.8320 149.9904 161.4060 # [129] 210.7392 200.4480 240.1448 384.2560 220.7744 134.9460 124.3424 324.0930 # [137] 287.8848 196.4160 155.5200 242.5626 279.1488 250.9047 151.7454 295.2832 # [145] 315.0675 240.3960 149.6250 202.8000 261.8136 162.4860```

## Further Resources & Related Tutorials

Please find some related tutorials on topics such as data objects, vectors, and naming data in the following list.