```
# Create a contingency table using xtabs()
<- xtabs(~ cyl + am, data = mtcars)
table_cars
# View the resulting table
table_cars
```

```
am
cyl 0 1
4 3 8
6 4 3
8 12 2
```

rtip

Author

Steven P. Sanderson II, MPH

Published

June 20, 2023

As a programmer, you’re constantly faced with the task of organizing and analyzing data. One powerful tool in your R arsenal is the xtabs() function. In this blog post, we’ll explore the versatility and simplicity of xtabs() for aggregating data. We’ll use the `mtcars`

dataset and the `healthyR.data::healthyR_data`

dataset to illustrate its functionality. Get ready to dive into the world of data aggregation with xtabs()!

The xtabs() function in R allows you to create contingency tables, which are a handy way to summarize data based on multiple factors or variables. It takes a formula-based approach and can handle both one-dimensional and multi-dimensional tables.

Let’s start with the mtcars dataset, which contains information about various car models. Suppose we want to understand the distribution of cars based on the number of cylinders and the transmission type. We can use xtabs() to accomplish this:

```
# Create a contingency table using xtabs()
table_cars <- xtabs(~ cyl + am, data = mtcars)
# View the resulting table
table_cars
```

```
am
cyl 0 1
4 3 8
6 4 3
8 12 2
```

In this example, the formula `~ cyl + am`

specifies that we want to cross-tabulate the “cyl” (number of cylinders) variable with the “am” (transmission type) variable. The resulting table provides a clear breakdown of car counts based on these two factors.

The xtabs() function also allows you to specify the order of the variables in the formula. For example, the following formula would create the same contingency table as the previous formula, but the rows of the table would be ordered by the number of cylinders in the car:

Let’s now explore the `healthyR.data::healthyR_data`

dataset, which is a simulated administrative dataset. Suppose we’re interested in analyzing the distribution of patients’ insurance type based on their type of stay. Here’s how we can use xtabs() for this analysis:

```
# Load the dataset
library(healthyR.data)
# Create a contingency table using xtabs()
table_health <- xtabs(~ payer_grouping + ip_op_flag, data = healthyR_data)
# View the resulting table
table_health
```

```
ip_op_flag
payer_grouping I O
? 1 0
Blue Cross 10797 13560
Commercial 3328 3239
Compensation 787 1715
Exchange Plans 1206 1194
HMO 8113 9331
Medicaid 7131 1646
Medicaid HMO 15466 10018
Medicare A 52621 1
Medicare B 293 22270
Medicare HMO 13572 5425
No Fault 1713 645
Self Pay 2089 1560
```

In this example, the formula `~ payer_grouping + ip_op_flag`

specifies that we want to cross-tabulate the “payer_grouping” variable with the “ip_op_flag” variable. By using `xtabs()`

, we obtain a comprehensive summary of patients’ insurance type and their stay type.

The xtabs() function in R provides a straightforward and effective way to aggregate data into contingency tables. It allows you to explore the relationships between multiple variables and gain insights into your dataset. In this blog post, we’ve covered two examples using the mtcars and healthyR_data datasets. However, xtabs() can be applied to any dataset with categorical variables. Experiment with this powerful function, and unlock new possibilities for data analysis and exploration in your programming journey.

Happy coding with xtabs()!