Correlation "long" data to matrix

[DominiqueMakowski]

I would like report to create traditional correlation matrices from the data provided by the new correlation package, which is in a long format.

For square matrices (i.e., all variables correlated with all variables), something like this could be a first step:

model <- correlation::correlation(iris)
cells <- model$r
m <- matrix(cells, nrow = as.integer(sqrt(length(cells))), ncol=as.integer(sqrt(length(cells))), byrow = TRUE)

However, colnames and rownames still need to be named appropriately. Moreover, this wouldn't work in the case of uneven matrices, such as:

model <- correlation::correlation(
 select(iris, Sepal.Length),
 select(iris, starts_with("Petal"))
)

@strengejacke do you have by any chance any intuition?

[strengejacke]

When I try to run your sample, I get an error... Is there anything I'm out of date? I just installed all latest easystats-packages with install_github().

#>  Correlation: 
#> Error in round(x[ll], digits = rdig) : 
#>   non-numeric argument to mathematical function

[strengejacke]

Ok, works now.

[DominiqueMakowski]

Added, might not be the best solution, but seems like it works :)

[IndrajeetPatil]

I think the only thing now remains is to retain the variable names.

I have shown below two different workflows, one using psych and one using correlation and the only difference is that the variable names are lost in the correlation-based workflow.

# setup
set.seed(123)
library(psych)
library(report)
library(ggcorrplot)
#> Loading required package: ggplot2
#> 
#> Attaching package: 'ggplot2'
#> The following objects are masked from 'package:psych':
#> 
#>     %+%, alpha

# correlation object
corr_obj <- corr.test(mtcars)

# checking class of object containing correlation coefficients
class(corr_obj$r)
#> [1] "matrix"

# plot
ggcorrplot(corr_obj$r)

# using `correlation` package
model <- correlation::correlation(mtcars)
cells <- model$r
m <- matrix(
  data = cells,
  nrow = as.integer(sqrt(length(cells))),
  ncol = as.integer(sqrt(length(cells))),
  byrow = TRUE
)

# plot
ggcorrplot(m)

^{Created on 2019-09-20 by the reprex package (v0.3.0)}

Session info

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[DominiqueMakowski]

library(correlation)
library(ggcorrplot)
#> Warning: package 'ggcorrplot' was built under R version 3.6.1
#> Loading required package: ggplot2
#> Warning: package 'ggplot2' was built under R version 3.6.1

cormat <- as.table(correlation(iris))
row.names(cormat) <- cormat$Parameter
cormat <- cormat[-1]

ggcorrplot(cormat)

^{Created on 2019-10-21 by the reprex package (v0.3.0)}

@strengejacke Think we could add that to see for a plot method?

[DominiqueMakowski]

Can now do:

correlation(iris) %>%
  as.matrix() %>%
  ggcorrplot()

We could also include an argument to get an graph network instead of a heatmap-like plot (See updated README)

[DominiqueMakowski]

Closing this as the main point has been addressed!