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.

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?

To spare future scholars the struggle of the early day R programming, I would like to share my custom functioncorrelation_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!

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 visualizeyour 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

#' 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
stopifnot({
is.numeric(digits)
digits >= 0
use %in% c("all", "upper", "lower")
is.logical(replace_diagonal)
is.logical(show_significance)
is.character(replacement)
})
# we need the Hmisc package for this
require(Hmisc)
# 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
}
return(Rnew)
}

save_correlation_matrix

#' 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))
}

Sign up to keep up to date on the latest R, Data Science & Tech content:

Last week, I shared this Medium blog on PPS — or Predictive Power Score — on my LinkedIn and got so many enthousiastic responses, that I had to share it with here too.

Basically, the predictive power score is a normalized metric (values range from 0 to 1) that shows you to what extent you can use a variable X (say age) to predict a variable Y (say weight in kgs).

A PPS high score of, for instance, 0.85, would show that weight can be predicted pretty good using age.

A low PPS score, of say 0.10, would imply that weight is hard to predict using age.

The PPS acts a bit like a correlation coefficient we’re used too, but it is also different in many ways that are useful to data scientists:

PPS also detects and summarizes non-linear relationships

PPS is assymetric, so that it models Y ~ X, but not necessarily X ~ Y

PPS can summarize predictive value of / among categorical variables and nominal data

However, you may argue that the PPS is harder to interpret than the common correlation coefficent:

PPS can reflect quite complex and very different patterns

Therefore, PPS are hard to compare: a 0.5 may reflect a linear relationship but also many other relationships

PPS are highly dependent on the used algorithm: you can use any algorithm from OLS to CART to full-blown NN or XGBoost. Your algorithm hihgly depends the patterns you’ll detect and thus your scores

PPS are highly dependent on the the evaluation metric (RMSE, MAE, etc).

Here’s an example picture from the original blog, showing a case in which PSS shows the relevant predictive value of Y ~ X, whereas a correlation coefficient would show no relationship whatsoever:

Here’s two more pictures from the original blog showing the differences with a standard correlation matrix on the Titanic data:

I highly suggest you readthe original blog for more details and information, and that you check out the associated Python packageppscore:

Installing the package:

pip install ppscore

Calculating the PPS for a given pandas dataframe:

import ppscore as pps pps.score(df, "feature_column", "target_column")

You can also calculate the whole PPS matrix:

pps.matrix(df)

There’s no R package yet, but it should not be hard to implement this general logic.

Florian Wetschoreck — the author — already noted that there may be several use cases where he’d think PPS may add value:

Find patterns in the data [red: data exploration]: The PPS finds every relationship that the correlation finds — and more. Thus, you can use the PPS matrix as an alternative to the correlation matrix to detect and understand linear or nonlinear patterns in your data. This is possible across data types using a single score that always ranges from 0 to 1.

Feature selection: In addition to your usual feature selection mechanism, you can use the predictive power score to find good predictors for your target column. Also, you can eliminate features that just add random noise. Those features sometimes still score high in feature importance metrics. In addition, you can eliminate features that can be predicted by other features because they don’t add new information. Besides, you can identify pairs of mutually predictive features in the PPS matrix — this includes strongly correlated features but will also detect non-linear relationships.

Detect information leakage: Use the PPS matrix to detect information leakage between variables — even if the information leakage is mediated via other variables.

Data Normalization: Find entity structures in the data via interpreting the PPS matrix as a directed graph. This might be surprising when the data contains latent structures that were previously unknown. For example: the TicketID in the Titanic dataset is often an indicator for a family.

Maarten Lambrechts is a data journalist I closely follow online, with great delight. Recently, he shared on Twitter his slidedeck on the 18 most common data visualization pitfalls. You will probably already be familiar with most, but some (like #14) were new to me:

Save pies for dessert

Don’t cut bars

Don’t cut time axes

Label directly

Use colors deliberately

Avoid chart junk

Scale circles by area

Avoid double axes

Correlation is no causality

Don’t do 3D

Sort on the data

Tell the story

1 chart, 1 message

Common scales on small mult’s

#Endrainbow

Normalise data on maps

Sometimes best map is no map

All maps lie

Even though most of these 18 rules below seem quite obvious, even the European Commissions seems to break them every now and then:

R’s standard correlation functionality (base::cor) seems very impractical to the new programmer: it returns a matrix and has some pretty shitty defaults it seems. Simon Jackson thought the same so he wrote a tidyverse-compatible new package: corrr!

Simon wrote some practical R code that has helped me out greatly before (e.g., color palette’s), but this new package is just great. He provides an elaborate walkthrough on his own blog, which I can highly recommend, but I copied some teasers below.

Apart from corrr::correlate to retrieve a correlation data frame and corrr::stretch to turn that data frame into a long format, the new package includes corrr::focus, which can be used to simulteneously select the columns and filter the rows of the variables focused on. For example:

# install.packages("tidyverse")
library(tidyverse)
# install.packages("corrr")
library(corrr)
# install.packages("here")
library(here)
dir.create(here::here("images")) # create an images directory
mtcars %>%
corrr::correlate() %>%
# use mirror = TRUE to not only select columns but also filter rows
corrr::focus(mpg:hp, mirror = TRUE) %>%
corrr::network_plot(colors = c("red", "green")) %>%
ggplot2::ggsave(
filename = here::here("images", "mtcars_networkplot.png"),
width = 5,
height = 5
)

Let’s try some different visualizations:

mtcars %>%
corrr::correlate() %>%
corrr::focus(mpg) %>%
dplyr::mutate(rowname = reorder(rowname, mpg)) %>%
ggplot2::ggplot(ggplot2::aes(rowname, mpg)) +
# color each bar based on the direction of the correlation
ggplot2::geom_col(ggplot2::aes(fill = mpg >= 0)) +
ggplot2::coord_flip() +
ggplot2::ggsave(
filename = here::here("images", "mtcars_mpg-barplot.png"),
width = 5,
height = 5
)

corrr also provides some very helpful functionality display correlations. Take, for instance, corrr::fashion and corrr::shave:

mtcars %>%
corrr::correlate() %>%
corrr::focus(mpg:hp, mirror = TRUE) %>%
# converts the upper triangle (default) to missing values
corrr::shave() %>%
# converts a correlation df into clean matrix
corrr::fashion() %>%
readr::write_excel_csv(here::here("correlation-matrix.csv"))

Finally, there is the great function of corrr::rplot to generate an amazing correlation overview visual in a wingle line. However, here it is combined with corr::rearrange to make sure that closely related variables are actually closely located on the axis, and again the upper half is shaved away:

For some more functionalities, please visit Simon’s blog and/or the associated GitHub page. If you copy the code above and play around with it, be sure to work in an Rproject else the here::here() functions might misbehave.

Xeno.graphics is the collection of unusual charts and maps Maarten Lambrechts maintains. It’s a repository of novel, innovative, and experimental visualizations to inspire you, to fight xenographphobia, and popularize new chart types.

For instance, have you ever before heard of a time curve? These are very useful to visualize the development of a relationship over time.

The upset plot is another example of an upcoming visualization. It can demonstrate the overlap or insection in a dataset. For instance, in the social network of #rstats twitter heroes, as the below example from the Xenographics website does.
The below necklace map is new to me too. What it does precisely is unclear to me as well.
There are hundreds of other interestingcharts, maps, figures, and plots, so do have a look yourself. Moreover, the xenographics collection is still growing. If you know of one that isn’t here already, please submit it. You can also expect some posts about certain topics around xenographics.