Tag: r

JavaScript for R — ebook

JavaScript for R — ebook

The R programming language has seen the integration of many languages; C, C++, Python, to name a few, can be seamlessly embedded into R so one can conveniently call code written in other languages from the R console. Little known to many, R works just as well with JavaScript—this book delves into the various ways both languages can work together.


John Coene is an well-known R and JavaScript developer. He recently wrote a book on JavaScript for R users, of which he published an online version free to access here.

The book is definitely worth your while if you want to better learn how to develop front-end applications (in JavaScript) on top of your statistical R programs. Think of better understanding, and building, yourself Shiny modules or advanced data visualizations integrated right into webpages.

A nice step on your development path towards becoming a full stack developer by combining R and JavaScript!

Yet most R developers are not familiar with one of web browsers’ core technology: JavaScript. This book aims to remedy that by revealing how much JavaScript can greatly enhance various stages of data science pipelines from the analysis to the communication of results.


Want to learn more about JavaScript in general, then I recommend this book:

Bayesian Statistics using R, Python, and Stan

Bayesian Statistics using R, Python, and Stan

For a year now, this course on Bayesian statistics has been on my to-do list. So without further ado, I decided to share it with you already.

Richard McElreath is an evolutionary ecologist who is famous in the stats community for his work on Bayesian statistics.

At the Max Planck Institute for Evolutionary Anthropology, Richard teaches Bayesian statistics, and he was kind enough to put his whole course on Statistical Rethinking: Bayesian statistics using R & Stan open access online.

You can find the video lectures here on Youtube, and the slides are linked to here:

Richard also wrote a book that accompanies this course:

For more information abou the book, click here.

For the Python version of the code examples, click here.

10 Guidelines to Better Table Design

10 Guidelines to Better Table Design

Jon Schwabisch recently proposed ten guidelines for better table design.

Next to the academic paper, Jon shared his recommendations in a Twitter thread.

Let me summarize them for you:

  • Right-align your numbers
  • Left-align your texts
  • Use decimals appropriately (one or two is often enough)
  • Display units (e.g., $, %) sparsely (e.g., only on first row)
  • Highlight outliers
  • Highlight column headers
  • Use subtle highlights and dividers
  • Use white space between rows and columns
  • Use white space (or dividers) to highlight groups
  • Use visualizations for large tables
Highlights in a table. Via twitter.com/jschwabish/status/1290324966190338049/photo/2
Visualizations in a table. Via twitter.com/jschwabish/status/1290325409570197509/photo/3
Example of a well-organized table. Via twitter.com/jschwabish/status/1290325663543627784/photo/2
How most statistical tests are linear models

How most statistical tests are linear models

Jonas Kristoffer Lindeløv wrote a great visual explanation of how the most common statistical tests (t-test, ANOVA, ANCOVA, etc) are all linear models in the back-end.

Jonas’ original blog uses R programming to visually show how the tests work, what the linear models look like, and how different approaches result in the same statistics.

George Ho later remade a Python programming version of the same visual explanation.

If I was thought statistics and methodology this way, I sure would have struggled less! Have a look yourself: https://lindeloev.github.io/tests-as-linear/

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:


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?

To spare future scholars the struggle of the early day R programming, I would like to share my custom function correlation_matrix.

My 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 below.

Now, to make matters even more easy, I wrote 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 and corrplot.

I hope my functions are of help to you!

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

I would be super interested and feel honored.


#' correlation_matrix
#' Creates a publication-ready / formatted correlation matrix, using `Hmisc::rcorr` in the backend.
#' @param df dataframe; containing numeric and/or logical columns to calculate correlations for
#' @param type character; specifies the type of correlations to compute; gets passed to `Hmisc::rcorr`; options are `"pearson"` or `"spearman"`; defaults to `"pearson"`
#' @param digits integer/double; number of decimals to show in the correlation matrix; gets passed to `formatC`; defaults to `3`
#' @param decimal.mark character; which decimal.mark to use; gets passed to `formatC`; defaults to `.`
#' @param use character; which part of the correlation matrix to display; options are `"all"`, `"upper"`, `"lower"`; defaults to `"all"`
#' @param show_significance boolean; whether to add `*` to represent the significance levels for the correlations; defaults to `TRUE`
#' @param replace_diagonal boolean; whether to replace the correlations on the diagonal; defaults to `FALSE`
#' @param replacement character; what to replace the diagonal and/or upper/lower triangles with; defaults to `""` (empty string)
#' @return a correlation matrix
#' @export
#' @examples
#' `correlation_matrix(iris)`
#' `correlation_matrix(mtcars)`
correlation_matrix <- function(df, 
                               type = "pearson",
                               digits = 3, 
                               decimal.mark = ".",
                               use = "all", 
                               show_significance = TRUE, 
                               replace_diagonal = FALSE, 
                               replacement = ""){
  # check arguments
    digits >= 0
    use %in% c("all", "upper", "lower")
  # we need the Hmisc package for this
  # retain only numeric and boolean columns
  isNumericOrBoolean = vapply(df, function(x) is.numeric(x) | is.logical(x), logical(1))
  if (sum(!isNumericOrBoolean) > 0) {
    cat('Dropping non-numeric/-boolean column(s):', paste(names(isNumericOrBoolean)[!isNumericOrBoolean], collapse = ', '), '\n\n')
  df = df[isNumericOrBoolean]
  # transform input data frame to matrix
  x <- as.matrix(df)
  # run correlation analysis using Hmisc package
  correlation_matrix <- Hmisc::rcorr(x, type = type)
  R <- correlation_matrix$r # Matrix of correlation coeficients
  p <- correlation_matrix$P # Matrix of p-value 
  # transform correlations to specific character format
  Rformatted = formatC(R, format = 'f', digits = digits, decimal.mark = decimal.mark)
  # if there are any negative numbers, we want to put a space before the positives to align all
  if (sum(!is.na(R) & R < 0) > 0) {
    Rformatted = ifelse(!is.na(R) & R > 0, paste0(" ", Rformatted), Rformatted)

  # add significance levels if desired
  if (show_significance) {
    # define notions for significance levels; spacing is important.
    stars <- ifelse(is.na(p), "", ifelse(p < .001, "***", ifelse(p < .01, "**", ifelse(p < .05, "*", ""))))
    Rformatted = paste0(Rformatted, stars)
  # make all character strings equally long
  max_length = max(nchar(Rformatted))
  Rformatted = vapply(Rformatted, function(x) {
    current_length = nchar(x)
    difference = max_length - current_length
    return(paste0(x, paste(rep(" ", difference), collapse = ''), sep = ''))
  }, FUN.VALUE = character(1))
  # build a new matrix that includes the formatted correlations and their significance stars
  Rnew <- matrix(Rformatted, ncol = ncol(x))
  rownames(Rnew) <- colnames(Rnew) <- colnames(x)
  # replace undesired values
  if (use == 'upper') {
    Rnew[lower.tri(Rnew, diag = replace_diagonal)] <- replacement
  } else if (use == 'lower') {
    Rnew[upper.tri(Rnew, diag = replace_diagonal)] <- replacement
  } else if (replace_diagonal) {
    diag(Rnew) <- replacement


#' save_correlation_matrix
#' Creates and save to file a fully formatted correlation matrix, using `correlation_matrix` and `Hmisc::rcorr` in the backend
#' @param df dataframe; passed to `correlation_matrix`
#' @param filename either a character string naming a file or a connection open for writing. "" indicates output to the console; passed to `write.csv`
#' @param ... any other arguments passed to `correlation_matrix`
#' @return NULL
#' @examples
#' `save_correlation_matrix(df = iris, filename = 'iris-correlation-matrix.csv')`
#' `save_correlation_matrix(df = mtcars, filename = 'mtcars-correlation-matrix.csv', digits = 3, use = 'lower')`
save_correlation_matrix = function(df, filename, ...) {
  return(write.csv2(correlation_matrix(df, ...), file = filename))

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David Robinson’s R Programming Screencasts

David Robinson’s R Programming Screencasts

David Robinson (aka drob) is one of the best known R programmers.

Since a couple of years David has been sharing his knowledge through streaming screencasts of him programming. It’s basically part of R’s #tidytuesday movement.

Alex Cookson decided to do us all a favor and annotate all these screencasts into a nice overview.


Here you can search for video material of David using a specific function or method. There are already over a thousand linked fragments!

Very useful if you want to learn how to visualize data using ggplot2 or plotly, how to work with factors in forcats, or how to tidy data using tidyr and dplyr.

For instance, you could search for specific R functions and packages you want to learn about:

Thanks David for sharing your knowledge, and thanks Alex for maintaining this overview!