Correlations and Scatter Plots

This vignette covers jjcorrmat() for creating correlation matrices and jjscatterstats() for scatter plots.

Correlation matrices with jjcorrmat()

jjcorrmat() visualises pairwise correlations between numeric variables and reports the associated tests. Here we look at the relationships between mpg, hp and wt in the mtcars data.

jjcorrmat(data = mtcars, dep = c(mpg, hp, wt), grvar = NULL)
#> 
#>  CORRELATION MATRIX
#> 
#>  You have selected to use a correlation matrix to compare continuous
#>  variables.
#> 
#>  <div style='margin: 10px 0;'><div style='background-color: #d1ecf1;
#>  border-left: 4px solid #0c5460; padding: 10px; margin: 5px 0;
#>  border-radius: 4px;'><strong style='color: #0c5460;'> INFO: <span
#>  style='color: #0c5460;'>Computed 3 zero-order Pearson correlations of
#>  3 variables.
#> 
#> character(0)
#> 
#>  Correlation Table                                                                                                                  
#>  ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
#>    Variable 1    Variable 2    N     Coefficient    Lower         Upper         p             p (adjusted)    Method                
#>  ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
#>    mpg           hp            32     -0.7761684    -0.8852686    -0.5860994     0.0000002       0.0000004    Pearson correlation   
#>    mpg           wt            32     -0.8676594    -0.9338264    -0.7440872    < .0000001    < .0000001    Pearson correlation   
#>    hp            wt            32      0.6587479     0.4025113     0.8192573     0.0000415       0.0000415    Pearson correlation   
#>  ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 
#>    Note. <b>p (adjusted)</b> applies the Holm correction across all pairwise tests. This is the p-value the plot uses to mark
#>    cells as non-significant at 0.05.

Scatter plots with jjscatterstats()

jjscatterstats() produces a scatter plot with a regression line and textual output describing the correlation and regression statistics.

jjscatterstats(data = mtcars, dep = mpg, group = hp, grvar = NULL)
#> 
#>  SCATTER PLOT
#> 
#>  You have selected to use a scatter plot with correlation analysis.