Tag: publishing

Create a publication-ready correlation matrix, with significance levels, in R

Create a publication-ready correlation matrix, with significance levels, in R

In most (observational) research papers you read, you will probably run into a correlation matrix. Often it looks something like this:

FACTOR ANALYSIS

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:

Table 4 from Family moderators of relation between community ...

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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ggstatsplot: Creating graphics including statistical details

ggstatsplot: Creating graphics including statistical details

This pearl had been resting in my inbox for quite a while before I was able to add it to my R resources list. Citing its GitHub pageggstatsplot is an extension of ggplot2 package for creating graphics with details from statistical tests included in the plots themselves and targeted primarily at behavioral sciences community to provide a one-line code to produce information-rich plots. The package is currently maintained and still under development by Indrajeet Patil. Nevertheless, its functionality is already quite impressive. You can download the latest stable version via:

utils::install.packages(pkgs = "ggstatsplot")

Or download the development version via:

devtools::install_github(
  repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
  dependencies = TRUE,                 # installs packages which ggstatsplot depends on
  upgrade_dependencies = TRUE          # updates any out of date dependencies
)

The package currently supports many different statistical plots, including:

?ggbetweenstats
?ggscatterstats
?gghistostats
?ggpiestats
?ggcorrmat
?ggcoefstats
?combine_plots
?grouped_ggbetweenstats
?grouped_ggscatterstats
?grouped_gghistostats
?grouped_ggpiestats
?grouped_ggcorrmat

Let’s take a closer look at the first one:

ggbetweenstats

This function creates either a violin plot, a box plot, or a mix of two for between-group or between-condition comparisons and additional detailed results from statistical tests can be added in the subtitle. The simplest function call looks like the below, but much more complex information can be added and specified.

set.seed(123) # to get reproducible results

# the functions work approximately the same as ggplot2
ggstatsplot::ggbetweenstats(
  data = datasets::iris, 
  x = Species, 
  y = Sepal.Length,
  messages = FALSE
) +   
# and can be adjusted using the same, orginal function calls
  ggplot2::coord_cartesian(ylim = c(3, 8)) + 
  ggplot2::scale_y_continuous(breaks = seq(3, 8, by = 1))

All pictures copied from the GitHub page of ggstatsplot [original]

ggscatterstats

Not all plots are ggplot2-compatible though, for instance, ggscatterstats is not. Nevertheless, it produces a very powerful plot in my opinion.

ggstatsplot::ggscatterstats(
  data = datasets::iris, 
  x = Sepal.Length, 
  y = Petal.Length,
  title = "Dataset: Iris flower data set",
  messages = FALSE
)

All pictures copied from the GitHub page of ggstatsplot [original]

ggcormat

ggcorrmat is also quite impressive, producing correlalograms with only minimal amounts of code as it wraps around ggcorplot. The defaults already produces publication-ready correlation matrices:

ggstatsplot::ggcorrmat(
  data = datasets::iris,
  corr.method = "spearman",
  sig.level = 0.005,
  cor.vars = Sepal.Length:Petal.Width,
  cor.vars.names = c("Sepal Length", "Sepal Width", "Petal Length", "Petal Width"),
  title = "Correlalogram for length measures for Iris species",
  subtitle = "Iris dataset by Anderson",
  caption = expression(
    paste(
      italic("Note"),
      ": X denotes correlation non-significant at ",
      italic("p "),
      "< 0.005; adjusted alpha"
    )
  )
)

All pictures copied from the GitHub page of ggstatsplot [original]

ggcoefstats

Finally, ggcoefstats is a wrapper around GGally::ggcoef, creating a plot with the regression coefficients’ point estimates as dots with confidence interval whiskers. Here’s an example with some detailed specifications:

ggstatsplot::ggcoefstats(
  x = stats::lm(formula = mpg ~ am * cyl,
                data = datasets::mtcars),
  point.color = "red",
  vline.color = "#CC79A7",
  vline.linetype = "dotdash",
  stats.label.size = 3.5,
  stats.label.color = c("#0072B2", "#D55E00", "darkgreen"),
  title = "Car performance predicted by transmission and cylinder count",
  subtitle = "Source: 1974 Motor Trend US magazine"
) +                                    
  ggplot2::scale_y_discrete(labels = c("transmission", "cylinders", "interaction")) +
  ggplot2::labs(x = "regression coefficient",
                y = NULL)

All pictures copied from the GitHub page of ggstatsplot [original]
I for one am very curious to see how Indrajeet will further develop this package, and whether academics will start using it as a default in publishing.