In statistics, **deciles** are numbers that split a dataset into ten groups of equal frequency.

The first decile is the point where 10% of all data values lie below it. The second decile is the point where 20% of all data values lie below it, and so on.

We can use the following syntax to calculate the deciles for a dataset in R:

quantile(data, probs = seq(.1, .9, by = .1))

The following example shows how to use this function in practice.

**Example: Calculate Deciles in R**

The following code shows how to create a fake dataset with 20 values and then calculate the values for the deciles of the dataset:

#create dataset data <- c(56, 58, 64, 67, 68, 73, 78, 83, 84, 88, 89, 90, 91, 92, 93, 93, 94, 95, 97, 99) #calculate deciles of dataset quantile(data, probs = seq(.1, .9, by = .1)) 10% 20% 30% 40% 50% 60% 70% 80% 90% 63.4 67.8 76.5 83.6 88.5 90.4 92.3 93.2 95.2

The way to interpret the deciles is as follows:

- 10% of all data values lie below
**63.4** - 20% of all data values lie below
**67.8**. - 30% of all data values lie below
**76.5**. - 40% of all data values lie below
**83.6**. - 50% of all data values lie below
**88.5**. - 60% of all data values lie below
**90.4**. - 70% of all data values lie below
**92.3**. - 80% of all data values lie below
**93.2**. - 90% of all data values lie below
**95.2**.

It’s worth noting that the value at the 50th percentile is equal to the median value of the dataset.

**Example: Place Values into Deciles in R**

To place each data value into a decile, we can use the **ntile(x, ngroups)** function from the dplyr package in R.

Here’s how to use this function for the dataset we created in the previous example:

library(dplyr) #create dataset data <- data.frame(values=c(56, 58, 64, 67, 68, 73, 78, 83, 84, 88, 89, 90, 91, 92, 93, 93, 94, 95, 97, 99)) #place each value into a decile data$decile <- ntile(data, 10) #view data data values decile 1 56 1 2 58 1 3 64 2 4 67 2 5 68 3 6 73 3 7 78 4 8 83 4 9 84 5 10 88 5 11 89 6 12 90 6 13 91 7 14 92 7 15 93 8 16 93 8 17 94 9 18 95 9 19 97 10 20 99 10

The way to interpret the output is as follows:

- The data value 56 falls between the percentile 0% and 10%, thus it falls in the first decile.
- The data value 58 falls between the percentile 0% and 10%, thus it falls in the first decile.
- The data value 64 falls between the percentile 10% and 20%, thus it falls in the second decile.
- The data value 67 falls between the percentile 10% and 20%, thus it falls in the second decile.
- The data value 68 falls between the percentile 20% and 30%, thus it falls in the third decile.

And so on.

**Additional Resources**

How to Calculate Percentiles in R

How to Calculate Quartiles in R

How to Create Frequency Tables in R