# Summarize Cases

The function `summarize()` (which may also be written `summarise()`) creates a table in which you will find the result(s) of the summary function(s) you have chosen to apply to a data frame. The summary functions may be:

• `mean()`: which returns the mean of a variable,
• `sd()`: which returns the standard deviation of a variable,
• `median()`: which returns the median of a variable,
• `min()`: which returns the minimum value of a variable,
• `max()`: which returns the maximum value of a variable,
• `var()`: which returns the variance of a variable,
• `sum()`: which returns the sum of a variable,
• etc.

To apply one or more of these summary functions to a data frame, you just have to indicate in `summarise()` which function(s) you want to apply and on which variable of the data frame. The syntax is:

`summarise(dataframe, function1(variable), function2(variable), ...) `

Alternatively, using pipes, the syntax is:

```dataframe %>%
summarise(function1(variable), function2(variable), ...) ```

Let’s use the data frame `Orange` as an example. The top of the data frame looks like this:

`head(Orange)`

To calculate the mean and the standard deviation of the variable `circumference`, we write either

`summarise(Orange, mean(circumference), sd(circumference))`

OR

```Orange %>%
summarise(mean(circumference), sd(circumference))```

which both result in:

This example actually does not make much sense in terms of biology. Indeed, we have calculated the average of circumference for different trees, but considering measurements performed at 7 different time points… Instead we could calculate the average circumference and standard deviation for each time point described in `age` by using `group_by` on the variable `age` (read more about `group_by` here).

To calculate the group means and standard deviations of the variable `circumference`, we write:

```Orange %>%
group_by(age) %>%
summarise(mean(circumference), sd(circumference))```

which results in:

Each line in the result table now shows the mean and standard deviation for each of 7 factors in `age` described in the first column.

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