Beautiful and Powerful Correlation Tables in R

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 apsychobject.

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")

	Age	Life_Satisfaction	Concealing	Adjusting
Age
Life_Satisfaction	0.03
Concealing	-0.05	-0.06
Adjusting	0.03	0.36***	0.22***
Tolerating	0.03	0.15***	0.07	0.29***

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()

	Age	Life_Satisfaction	Concealing	Adjusting
Age
Life_Satisfaction	0.01
Concealing	-0.06	-0.16***
Adjusting	0.02	0.36***	0.25***
Tolerating	0.02	0.06	0.02	0.24***

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)

	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10	X11
X1
X2	-0.04
X3	-0.04	-0.02
X4	0.02	0.05	-0.02
X5	-0.01	-0.02	0.05	-0.03
X6	-0.03	0.03	0.08*	0.02	0.02
X7	0.03	-0.01	-0.02	-0.04	-0.03	-0.04
X8	0.01	-0.07*	0.04	0.02	-0.01	-0.01	0.00
X9	-0.02	0.03	-0.03	-0.02	0.00	-0.04	0.03	-0.02
X10	-0.03	0.00	0.00	0.01	0.01	-0.01	0.01	-0.02	0.02
X11	0.01	0.01	-0.03	-0.05	0.00	0.05	0.01	0.00	-0.01	0.07*
Local_Air_Density	0.26***	-0.02	-0.44***	-0.15***	-0.25***	-0.50***	0.57***	-0.11***	0.47***	0.06	0.01
Reincarnation_Cycle	-0.03	-0.02	0.02	0.04	0.01	0.00	0.05	-0.04	-0.05	-0.01	0.03
Communism_Level	0.58***	-0.44***	0.04	0.06	-0.10**	-0.18***	0.10**	0.46***	-0.50***	-0.21***	-0.14***
Alien_Mothership_Distance	0.00	-0.03	0.01	0.00	-0.01	-0.03	-0.04	0.01	0.01	-0.02	0.00
Schopenhauers_Optimism	0.11***	0.31***	-0.25***	0.64***	-0.29***	-0.15***	-0.35***	-0.09**	0.08*	-0.22***	-0.47***
Hulks_Power	0.03	0.00	0.02	0.03	-0.02	-0.01	-0.05	-0.01	0.00	0.01	0.03

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

转自：https://neuropsychology.github.io/psycho.R//2018/05/20/correlation.html