Beautiful Correlation Tables in R

I have achieved another victory in getting R to produce SPSS-like results. In experimental psychology, an analysis of measurement variable correlations is a common method in the course of a statistical analysis. Thus, I wanted R to produce a publication-quality output similar to SPSS: a correlation matrix of measurement variables that contains only the lower triangle of observations, where observations have two decimal digits and are flagged with stars (*, **, and ***) according to levels of statistical significance. However, as statmethods notices:
Unfortunately, neither cor( ) or cov( ) produce tests of significance, although you can use the cor.test( ) function to test a single correlation coefficient.
I did a little research and found this post on the R-help list. I modified Chuck Cleland's code a little so that the following command on the swiss data frame (provided in the Hmisc package) produces a beautiful output:

> corstarsl(swiss[,1:4])

Fertility Agriculture Examination
Agriculture 0.35*
Examination -0.65*** -0.69***
Education -0.66*** -0.64*** 0.70***

If one employs the xtable package that produces LaTeX tables from within R, xtable(corstarsl(swiss[,1:4])) produces this:
Isn't that beautiful? I like it a lot. Here's the code (as I said, much of it taken from here):

corstarsl <- function(x){
x <- as.matrix(x)
R <- rcorr(x)$r
p <- rcorr(x)$P

## define notions for significance levels; spacing is important.
mystars <- ifelse(p < .001, "***", ifelse(p < .01, "** ", ifelse(p < .05, "* ", " ")))

## trunctuate the matrix that holds the correlations to two decimal
R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1]

## build a new matrix that includes the correlations with their apropriate stars
Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x))
diag(Rnew) <- paste(diag(R), " ", sep="")
rownames(Rnew) <- colnames(x)
colnames(Rnew) <- paste(colnames(x), "", sep="")

## remove upper triangle
Rnew <- as.matrix(Rnew)
Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
Rnew <- as.data.frame(Rnew)

## remove last column and return the matrix (which is now a data frame)
Rnew <- cbind(Rnew[1:length(Rnew)-1])

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3:44 AM  
Blogger Sean said...

Very nice! Just copied and pasted the code into R and it worked flawlessly. A fast way to get a readable table to share results with colleagues.

8:00 PM  
Blogger Disgruntled PhD said...

Thank you so much for this. Almost finished my first latex and R paper, formatted in APA style when i realised that i had no significance stars. You're a lifesaver, thank you.

5:04 PM  
Blogger Travel Bugs said...

Seriously helpful thanks a ton for this.

11:29 AM  
Blogger aL3xa said...

Take a look at this:


It's very similar, but uses `psych` package instead `Hmisc`

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Anonymous Anonymous said...

thanks, this was a super useful post.

7:40 PM  
Anonymous Anonymous said...

Hi there,

i don't get the same output like although I installed all necessary packages.

My output looks like this:

% latex table generated in R 2.15.1 by xtable 1.7-0 package
% Fri Oct 12 12:45:52 2012
& Fertility & Agriculture & Examination \\
Fertility & & & \\
Agriculture & 0.35* & & \\
Examination & -0.65*** & -0.69*** & \\
Education & -0.66*** & -0.64*** & 0.70*** \\

What to do???


11:49 AM  
Blogger fxen2k said...

Hi there,

i don't get the same output like although I installed all necessary packages.

My output looks like this:

% latex table generated in R 2.15.1 by xtable 1.7-0 package
% Fri Oct 12 12:45:52 2012
& Fertility & Agriculture & Examination \\
Fertility & & & \\
Agriculture & 0.35* & & \\
Examination & -0.65*** & -0.69*** & \\
Education & -0.66*** & -0.64*** & 0.70*** \\

What to do???


11:50 AM  
Blogger Nicole said...

Just wanted to add if you want Spearman's test instead, you have to add it in two lines, otherwise the stars are based on Pearson. I made that mistake and it took me a while to figure it out, since the p-values displayed are still the right ones from Spearman.

R <- rcorr(x, type="Spearman")$r
p <- rcorr(x, type="Spearman")$P

8:02 PM  
Anonymous Anonymous said...

WOW this is unbelievably good. From the perspective of an R newbie where nothing ever works first time, this is AMAZING. Thank you so much!

11:20 AM  
Blogger Conal said...

Fantastic code!,
A lot of professional reporting standards (e.g., APA 6) elimates the "0" in front of each correlation (e.g., 0.77 = .77) because the 0 is assumed. I wonder how this package could be altered to remove the zero in the front?

- Conal

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Anonymous STHDA said...

Nice topic. This help articles helps me.

Several articles on how to compute and visualize correlation matrix are also provided here :

Computing and visualizing correlation in R

8:43 AM  
Anonymous Michael said...

The current procedure to drop the upper triangular is not optimal because it turns all numbers into characters. This may cause problems when you want to round, print in latex tables or calculate.

Therefore you better use

## remove upper triangle
Rnew[upper.tri(Rnew, diag = TRUE)] <- NA

Also, the transformation into matrix/data frame is not necessary.

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