When doing data science with r programming you may need to calculate mean in r by group when evaluating a data set. The mean value of a group is an important statistic. It not only provides you with the central value of the group but is used to calculate other important statistical values.
Description
You can calculate mean in r by group using the aggregate function. This is a base r function that is used to apply a function across an entire data set based on groups and it has the format of aggregate(x, by, FUN).
- “x” is the vector the function is being applied to.
- “by” is the vector the function defines the groups to group by.
- “FUN” is the function being applied. In this case, it is the mean function.
Explanation
A mean calculated by groups is a summary statistic that indicates the central tendency for the data value groups defined by the grouping variable. We summarize a data set in this fashion as part of descriptive statistics. The results of descriptive statistics are affected by the sample size, which is one reason a larger sample size is better than a small one. The aggregate function makes this possible by spreading the mean function over the groups of the entire vector.
<h2>Examples</h2>Here are three examples of using the aggregate function to calculate mean in r by group. They each illustrate a different aspect of this function.
> df = data.frame(X = c(“A”, “B”, “C”, “D”, “A”, “B”, “C”, “D”, “A”, “B”, “C”, “D”, “A”, “B”, “C”, “D”),
+ Y = c(111, 211, 311, 411, 122, 222, 322, 422, 133, 233, 333, 433, 144, 244, 344, 444))
> aggregate(x = df$Y,
+ by = list(df$X),
+ FUN = mean)
Group.1 x
1 A 127.5
2 B 227.5
3 C 327.5
4 D 427.5
This is just a straightforward example of this function using a data frame.
> df = data.frame(X = c(“A”, “B”, “C”, “D”, “A”, “B”, “C”, “D”, “A”, “B”, “C”, “D”, “A”, “B”, “C”, “D”),
+ Y = c(111, 211, 311, 411, 122, NA, 322, 422, 133, 233, 333, 433, 144, 244, 344, 444))
> aggregate(x = df$Y,
+ by = list(df$X),
+ FUN = mean)
Group.1 x
1 A 127.5
2 B NA
3 C 327.5
4 D 427.5
Here is an example of how this function handles a missing value. It simply returns NA values for any affected groups.
> X = c(“A”, “B”, “C”, “D”, “A”, “B”, “C”, “D”, “A”, “B”, “C”, “D”, “A”, “B”, “C”, “D”)
> Y = c(111, 211, 311, 411, 122, 222, 322, 422, 133, 233, 333, 433, 144, 244, 344, 444)
> aggregate(x = Y,
+ by = list(X),
+ FUN = mean)
Group.1 x
1 A 127.5
2 B 227.5
3 C 327.5
4 D 427.5
In this example, we simply use two vectors as the arguments in the function. It works just as well, as it would if we were using a data frame.
Application
There are many applications to calculating the mean value of groups because a sample mean value can be used to calculate other statistical factors such as the standard deviation and confidence interval. This also makes it an indispensable part of statistical learning. Being able to make such calculations for individual data groups greatly expands what can be learned about a data set. Finding the mean value of each group is just the beginning of the process because a lot of other statistical data relies on it.
It is an important part of r programming to be able to calculate mean in r by group. This opens the door to being able to calculate a lot of other statistical data. It is a straightforward process of using the aggregate function to separate your data set into groups to supply the mean values of those groups.