The R program offers many functions to compute the value of the dataset through any mathematical means so long as the values used are of recognizable units. In computing the resultant value from combining two values and computing the product in r, the resultant value is found to be corresponding in magnitude to both vectors combined. Let’s look at one example where the vector has a discerning element that when combined with another vector will show the combined inputs represented by the product computed in r programming.

The product of the vectors is computed with the prod() function. Given a simple vector to calculate, the function multiplies all vectors referenced to the initial inputs dataset. When entering a complex value, be sure to set the parameters so the function can give a relevant answer in the correct format.

Here we’ll set parameters for a value from our dataset for the prod() function to multiply;

data 4:8

cat(“The product of a vector is: “, “\q”)

product prod(data)

and when we compute the given product will correspond to the worth of the combined vectors in the array.

cat(product)

Now the product is combined through the cat function and will be presented as a numeric value on the screen, in this argument the product 6720 is displayed.

YOu can also tell your R programming to tackle complex vectors. Here we’ll use imaginary numbers in an argument where the answer involves two different values for the corresponding inputs that will need to be calculated separately but remain in the same answer line.

complex c(5+6i, 8-1i)

cat(“The product of a complex vector is: “, “\q”)

complexprod prod(complex)

cat(complexprod)

The prod() will have to combine the imaginary numbers the array separate from the remaining values and combine to get two values that make up the answer. Warning of the different values in the array, the complex product function makes the according to calculations and returns the correct answer; 5+8, 6i-1i = 13+5i. That’s how to do complex vector functions in R.

Switching from single rows to more substantial arrays, we’ll next take a dataset framed in columns and rows and command through the product function to find the combined worth of the vertical and horizontal vectors. Our aim is to have the function apply to only the intersection between the vertical and horizontal, so we command that it apply its assessment to those named values.

apply(data, Vert, prod)

apply(data, Hor, prod)

Now when we use the product function, our answers will correctly match in magnitude to the combined correlating inputs of the top and side strings.

YOur not limited to just multiplications. The R package contains tools for addition, division, and other complex calculations that require parameters for input values to get answers.

When looking for the sum of a string, the function sum() requires only the numeric values in the string.

vec = c(1, 2, 3 , 4)

prod(vec) = (1+2+3+4) = 10

Now for a more complex function, we’ll look at calculating the mean of a dataset. In this case, we’ll also see how to implement a string with missing values and the NA.rm command that can either include the NA in place of the missing element or remove it entirely from the string.

x1 c(1, 2, 3, NA, 5, NA, NA)

mean(x1, na.rm = FALSE) = NA

mean(x1, na.rm = TRUE) = 2.75

You’ll find many uses for the mathematical tools available in r programming as you get more familiar with their requirements and applications. Moving forward, look to push the limits with your modeling and graphing projects by having the prod() function on hand to develop more in-depth data on your original pool. Use this and keep expanding your toolset so you can further improve as a data scientists.