docs/_posts/2018-05-20-correlation.md

In neuropsychology/psycho.R: Efficient and Publishing-Oriented Workflow for Psychological Science

layout: post title: "Beautiful and Powerful Correlation Tables in R" author: Dominique Makowski author_web: https://dominiquemakowski.github.io/ date: 2018-05-20 summary: Beautiful and Powerful Correlation Tables in R.

• 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")

| | 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

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neuropsychology/psycho.R documentation built on Jan. 25, 2021, 7:59 a.m.