Beautiful and Powerful Correlation Tables in R
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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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