The conditional mean in r is used to find the mean value of selected rows of a particular data frame column as determined by matching a value in another column. This is a way of selectively evaluating related data based on a factor described in another column. This ability increases the power of the mean function beyond its basic form.

### How to Find the Conditional Mean (not just in R)

A conditional mean is a variation on the mean function that allows you to select the rows in a data frame. It does not produce an error or warning message when it encounters missing values, but it does return an NA value when it does. By adding another variable to a data frame, you can perform a conditional mean on that vector based on the data frame content. This feature supplies a lot of extra flexibility to the mean function by allowing you to select the content to be evaluated based on specific criteria. It is excellent for comparing groups of data, by setting the group as the condition.

### Explanation – Calculate Conditional Mean in R

As a variation on the mean function a conditional mean supplies the mean value of a group of values. It works similarly to an ifelse statement. The selection criteria are in essence an independent variable that selects data frame rows to be analyzed. This independent variable can be a random variable, dummy variable, factor, or even a constant. It looks at the specified column in the data frame and when it finds a matching value it locates the value in the same row in the column to be evaluated. The formula then takes the mean of all the selected values from that column.

### Examples – Calculate Conditional Mean in R

Here are three examples of the conditional mean in action. Each one shows it used with a different data type as the selection criteria. It shows both the conditional and standard mean values for each column.

> x = data.frame(a=c(“A”,”B”,”A”,”B”,”A”,”B”),

+ b=c(2, 4, 6, 8, 10, 12),

+ c=c(3, 6, 9, 12, 15, 18),

+ d=c(4, 8, 12, 16, 20, 24))

> mean(x$b)

[1] 7

> mean(x[x$a == ‘A’, ‘b’])

[1] 6

> mean(x$c)

[1] 10.5

> mean(x[x$a == ‘A’, ‘c’])

[1] 9

> mean(x$d)

[1] 14

> mean(x[x$a == ‘A’, ‘d’])

[1] 12

This example shows the conditional mean being used with character values for all three of the other columns.

> x = data.frame(a=c(1, 2, 1, 2, 1, 2),

+ b=c(2, 4, 6, 8,10,12),

+ c=c(3, 6, 9,12,15,18),

+ d=c(4, 8,12,16,20,24))

> mean(x$b)

[1] 7

> mean(x[x$a == 1, ‘b’])

[1] 6

> mean(x$c)

[1] 10.5

> mean(x[x$a == 1, ‘c’])

[1] 9

> mean(x$d)

[1] 14

> mean(x[x$a == 1, ‘d’])

[1] 12

This example shows the conditional mean being used with numeric values for all three of the other columns.

> x = data.frame(a=c(TRUE,FALSE,TRUE,FALSE,TRUE,FALSE),

+ b=c(2, 4, 6, 8, 10, 12),

+ c=c(3, 6, 9, 12, 15, 18),

+ d=c(4, 8, 12, 16, 20, 24))

> mean(x$b)

[1] 7

> mean(x[x$a == TRUE, ‘b’])

[1] 6

> mean(x$c)

[1] 10.5

> mean(x[x$a == TRUE, ‘c’])

[1] 9

> mean(x$d)

[1] 14

> mean(x[x$a == TRUE, ‘d’])

[1] 12

This example shows the conditional mean being used with logical values for all three of the other columns.

### Applications of Calculating The Conditional Mean in R

The conditional mean has many handy applications. It is helpful in finding the mean value of a selected area of a plot, linear model, or linear regression. In regression, it helps you to find a conditional distribution, conditional probability, and conditional expectation of the selected values. Along with the probability, it helps you to find the variance of selected values. Furthermore, it is helpful in finding group means within a data frame. For example, you can use it to find the mean value of the salaries of male and female employees separately. You can use it to find the mean earnings of people of different ages. You can use it to find the mean height of groups of people. Anyplace we have groups of people, animals, or any other kind of object, if you have a feature of those objects described numerically a conditional mean in r will be a useful tool.

The conditional mean in r is a handy function for manipulating data. The selection process allows you to evaluate separate groups of values from a formula that otherwise would give you the mean of the entire column. This makes it a powerful tool for evaluating the numerical contents of data frames. It is one function that you will find extremely useful.