** TLDR;** You can use the

`corrtable`

package (see CRAN or Github)!In most (observational) research papers you read, you will probably run into a **correlation matrix**. Often it looks something like this:

In Social Sciences, like Psychology, researchers like to denote the **statistical significance levels** of the correlation coefficients, often using asterisks (i.e., *). Then the table will look more like this:

Regardless of my personal preferences and opinions, I had to make many of these tables for the scientific (non-)publications of my Ph.D..

I remember that, when I first started using R, I found it quite difficult to generate these correlation matrices automatically.

**Yes**, there is the `cor`

function, but it does not include significance levels.

Then there the (in)famous `Hmisc`

package, with its `rcorr`

function. But this tool provides a **whole new range of issues**.

What’s this `storage.mode`

, and what are we trying to coerce again?

Soon you figure out that `Hmisc::rcorr`

only takes in matrices *(thus with only numeric values)*. **Hurray**, now you can run a correlation analysis on your *dataframe*, you think…

Yet, the output is **all but publication-ready**!

You wanted one correlation matrix, but now you have two… **Double the trouble?**

[UPDATED] To **spare future scholars the struggle** of the early day R programming, Laura Lambert and I created an R package `corrtable`

, which includes the helpful function `correlation_matrix`

.

This `correlation_matrix`

takes in a *dataframe*, selects only the numeric (and boolean/logical) columns, calculates the correlation coefficients and p-values, and outputs a **fully formatted publication-ready correlation matrix**!

You can specify **many formatting options** in `correlation_matrix`

.

For instance, you can use only 2 decimals. You can focus on the lower triangle *(as the lower and upper triangle values are identical)*. And you can drop the diagonal values:

Or maybe you are interested in a **different type of correlation coefficients**, and not so much in significance levels:

For other formatting options, do have a look at the source code on github.

Now, to make matters **even easier**, the package includes a second function (`save_correlation_matrix`

) to directly save any created correlation matrices:

Once you open your new correlation matrix file in Excel, it is **immediately ready** to be copy-pasted into Word!

If you are looking for ways to **visualize **your correlations do have a look at the packages `corrr`

, `corrplot`

, or `ppsr`

.

**I hope this package is of help to you!**

Do reach out if you get to use them in any of your research papers!

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