Mastering tbl_summary(): Grouping Variables into Toplevel Variables like a Pro!

Welcome to the world of data manipulation and summarization! Today, we’re going to dive into the fantastic universe of tbl_summary() and explore one of its most powerful features: grouping variables into toplevel variables. Get ready to unlock the full potential of your data and take your summary tables to the next level!

What are Toplevel Variables?

Before we dive into the nitty-gritty of grouping variables, let’s quickly cover what toplevel variables are. In the context of tbl_summary(), toplevel variables refer to the main columns or variables in your dataset that you want to summarize or describe. Think of them as the “headlining” variables that will be displayed prominently in your summary table.

For example, if you’re working with a dataset about customers, some toplevel variables might include:

• Customer ID
• Age
• Gender
• Country

These variables are the foundation of your summary table, and the ones you’ll want to group other variables under.

Why Group Variables into Toplevel Variables?

Now that we’ve covered what toplevel variables are, let’s talk about why grouping variables into them is so powerful. By grouping variables into toplevel variables, you can:

1. Simplify complex data: Grouping variables helps to organize and structure your data in a way that’s easy to understand and work with.
2. Reduce data clutter: By grouping related variables together, you can eliminate clutter and make your summary table more concise and readable.
3. Enhance data insights: Grouping variables allows you to focus on the most important aspects of your data, making it easier to identify trends, patterns, and correlations.
4. Improve data visualization: With grouped variables, you can create more effective and informative visualizations that tell a clear story about your data.

How to Group Variables into Toplevel Variables using tbl_summary()

Now that we’ve covered the why, let’s get to the how! To group variables into toplevel variables using tbl_summary(), you’ll need to follow these steps:

Step 1: Prepare Your Data

Before you can start grouping variables, you need to prepare your data by:

• Loading the gtsummary package: library(gtsummary)
• Loading your dataset: data(your_data)
• Converting your data to a tibble: your_data %>% as_tibble()

> library(gtsummary)
> data(mtcars)
> mtcars %>% as_tibble()

Step 2: Identify Your Toplevel Variables

Next, identify the toplevel variables in your dataset that you want to group other variables under. For our example, let’s say we want to use the cyl, vs, and am columns as our toplevel variables.

> toplevel_vars <- c("cyl", "vs", "am")

Step 3: Group Variables into Toplevel Variables

Now, use the tbl_summary() function to group variables into your toplevel variables. You can do this using the by argument, which specifies the toplevel variables to group by.

> mtcars %>% 
  tbl_summary(by = toplevel_vars)

This will create a summary table with the toplevel variables as the main columns, and the remaining variables grouped underneath.

Customizing Your Summary Table

Once you’ve created your summary table, you can customize it to fit your needs. Here are a few ways to do so:

Adding Additional Variables

Use the add_n() function to add additional variables to your summary table. For example:

> mtcars %>% 
  tbl_summary(by = toplevel_vars) %>% 
  add_n(label = "Number of Rows")

Modifying Variable Labels

Use the modify_header() function to modify the labels of your variables. For example:

> mtcars %>% 
  tbl_summary(by = toplevel_vars) %>% 
  modify_header(cyl = "Cylinders", vs = "V/S", am = "Automatic/Manual")

Changing the Summary Statistics

Use the add_stat() function to change the summary statistics displayed in your table. For example:

> mtcars %>% 
  tbl_summary(by = toplevel_vars) %>% 
  add_stat(funs = list(mean, sd))

Putting it All Together

Let’s put everything we’ve learned together in a single example:

> library(gtsummary)
> data(mtcars)
> mtcars %>% as_tibble() %>% 
  tbl_summary(by = c("cyl", "vs", "am")) %>% 
  add_n(label = "Number of Rows") %>% 
  modify_header(cyl = "Cylinders", vs = "V/S", am = "Automatic/Manual") %>% 
  add_stat(funs = list(mean, sd))

…

Cylinders	V/S	Automatic/Manual	mpg	disp	hp	drat	wt	qsec
4	0	0	26.00 (4.51)	105.14 (26.41)	82.64 (20.93)	3.77 (0.39)	2.29 (0.43)	19.13 (1.87)
4	1	0	28.40 (4.47)	102.88 (22.47)	75.80 (18.80)	3.91 (0.50)	2.27 (0.42)	18.60 (1.67)

And that’s it! With these simple steps, you’ve successfully grouped variables into toplevel variables using tbl_summary(). From here, the possibilities are endless – you can customize your summary table to fit your specific needs, explore your data in new and exciting ways, and uncover insights that might have otherwise gone unnoticed.

Conclusion

Mastering the art of grouping variables into toplevel variables is a crucial step in summarizing and visualizing your data. With the powerful tbl_summary() function and the techniques outlined in this article, you’re now equipped to take your data analysis to the next level. Remember to keep it simple, stay organized, and always keep your toplevel variables in mind – happy summarizing!

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