Having multiple `pander()`s in a function

As data scientists and analysts, we often find ourselves working with data that requires formatting and visualization. One tool that has gained popularity in recent years is the pander package in R, which allows us to easily format our output and make it more readable. However, when using pander within a function, there’s an issue that can lead to unexpected behavior.

In this article, we’ll explore what’s happening behind the scenes of pander() and how to work around its limitations.

Understanding `pander()`

For those unfamiliar with the pander package, it provides a convenient way to format data in R. The pander() function can be used to display various types of data, such as tables, summary statistics, and even data frames. One of its key features is its ability to automatically detect the type of data being passed to it and format it accordingly.

Here’s an example of using pander() to display a simple table:

# Create some sample data
df <- data.frame(x = 1:5, y = 6:10)

# Use pander() to display the data
pander(df)

In this case, pander() will automatically detect that we’re dealing with a data frame and format it accordingly.

However, when using pander() within a function, things can get tricky.

The Problem

Consider the following example:

tmp <- function() {
  pander('A')
  pander('B')
  pander('C')
}

tmp()

As we can see, only the last output ('C') is displayed. This is because of how pander() works internally.

When using pander() within a function, it doesn’t actually write its output to the console; instead, it returns an object that contains the formatted data. However, this object is not automatically written to the console or output area when we call the function.

By default, knitr, which is used in conjunction with pander(), will only show the last output of a function if you’re using its “asis” (inline) output method. This means that if you want to see all outputs from within a function, you need to disable this feature and instead use its chunk-based output method.

The Solution

To fix this issue, we can use the panderOptions() function to disable the auto-asis convenience option, like so:

# Disable the auto-asis convenience option
panderOptions('knitr.auto.asis', FALSE)

tmp <- function() {
  pander('A')
  pander('B')
  pander('C')
}

tmp()

By disabling this feature, we can ensure that each output is written to the console individually, rather than being buffered and only displayed when the function returns.

Using `knitr` Chunk Options

Another solution is to use the chunk options provided by knitr. Specifically, we can use the “results” option to specify whether we want to write our outputs to the console or not. Here’s an example:

# Create a knitr chunk with results set to "asis"
```{r results='asis'}
library(pander)
panderOptions('knitr.auto.asis', FALSE)

tmp <- function() {
  pander('A')
  pander('B')
  pander('C')
}

tmp()

By using the “asis” option, we can ensure that each output is written to the console individually.

Additional Considerations

There are a few additional things to consider when working with pander() and chunk-based output methods:

Chunk-level formatting: When using the chunk-based output method, you may need to use additional commands or functions to format your data at the chunk level. For example, you can use the chunk_options argument in the knitr chunk header to specify different formatting options.
Output buffer size: The output buffer size can affect how much output is written to the console before it’s displayed. You may need to adjust this setting depending on your specific use case.

Conclusion

Working with pander() and chunk-based output methods can be a bit tricky, especially when using these functions within a function. However, by understanding the limitations of pander() and taking steps to address them, you can ensure that your data is formatted correctly and displayed as expected.

In this article, we’ve explored what happens behind the scenes of pander(), how it works with chunk-based output methods, and some additional considerations for working with these functions. By mastering these concepts, you’ll be able to create high-quality reports and visualizations that showcase your data in its best possible light.

Last modified on 2023-09-25

Having multiple pander()s in a function

Understanding pander()