• Another correlation function?!
• A table
• A Plot
• A print
• Options
• Fun with p-hacking
• Credits

Another correlation function?!

Yes, the correlation function from the psycho package.

devtools::install_github("neuropsychology/psycho.R")  # Install the newest version

library(psycho)
library(tidyverse)

cor <- psycho::affective %>% 
  correlation()

This function automatically select numeric variables and run a correlation analysis. It returns a psychobject.

A table

We can then extract a formatted table that can be saved and pasted into reports and manuscripts by using the summary function.

summary(cor)
# write.csv(summary(cor), "myformattedcortable.csv")

A Plot

It integrates a plot done with ggcorplot.

plot(cor)

A print

It also includes a pairwise correlation printing method.

print(cor)

Pearson Full correlation (p value correction: holm):

   - Age / Life_Satisfaction:   Results of the Pearson correlation showed a non significant and weak negative association between Age and Life_Satisfaction (r(1249) = 0.030, p > .1).
   - Age / Concealing:   Results of the Pearson correlation showed a non significant and weak positive association between Age and Concealing (r(1249) = -0.050, p > .1).
   - Life_Satisfaction / Concealing:   Results of the Pearson correlation showed a non significant and weak positive association between Life_Satisfaction and Concealing (r(1249) = -0.063, p > .1).
   - Age / Adjusting:   Results of the Pearson correlation showed a non significant and weak negative association between Age and Adjusting (r(1249) = 0.027, p > .1).
   - Life_Satisfaction / Adjusting:   Results of the Pearson correlation showed a significant and moderate negative association between Life_Satisfaction and Adjusting (r(1249) = 0.36, p < .001***).
   - Concealing / Adjusting:   Results of the Pearson correlation showed a significant and weak negative association between Concealing and Adjusting (r(1249) = 0.22, p < .001***).
   - Age / Tolerating:   Results of the Pearson correlation showed a non significant and weak negative association between Age and Tolerating (r(1249) = 0.031, p > .1).
   - Life_Satisfaction / Tolerating:   Results of the Pearson correlation showed a significant and weak negative association between Life_Satisfaction and Tolerating (r(1249) = 0.15, p < .001***).
   - Concealing / Tolerating:   Results of the Pearson correlation showed a non significant and weak negative association between Concealing and Tolerating (r(1249) = 0.074, p = 0.05°).
   - Adjusting / Tolerating:   Results of the Pearson correlation showed a significant and weak negative association between Adjusting and Tolerating (r(1249) = 0.29, p < .001***).

Options

You can also cutomize the type (pearson, spearman or kendall), the p value correction method (holm (default), bonferroni, fdr, none…) and run partial, semi-partial or glasso correlations.

psycho::affective %>% 
  correlation(method = "pearson", adjust="bonferroni", type="partial") %>% 
  summary()

Fun with p-hacking

In order to prevent people for running many uncorrected correlation tests (promoting p-hacking and result-fishing), we included the i_am_cheating parameter. If FALSE (default), the function will help you finding interesting results!

df_with_11_vars <- data.frame(replicate(11, rnorm(1000)))
cor <- correlation(df_with_11_vars, adjust="none") 

## Warning in correlation(df_with_11_vars, adjust = "none"): We've detected that you are running a lot (> 10) of correlation tests without adjusting the p values. To help you in your p-fishing, we've added some interesting variables: You never know, you might find something significant!
## To deactivate this, change the 'i_am_cheating' argument to TRUE.

summary(cor)

As we can see, Schopenhauer’s Optimism is strongly related to many variables!!!

Credits

This package was useful? You can cite psycho as follows:

• Makowski, (2018). The psycho Package: an Efficient and Publishing-Oriented Workflow for Psychological Science. Journal of Open Source Software, 3(22), 470. https://doi.org/10.21105/joss.00470