title: stat_cor function incorrectly calculating correlation p value
body: <p>I am using stat_cor with ggplot to add r and p values to a scatter plot. It is incorrectly calculating the p value based on the number of observations in long format data frame. It is confused about long format organization, and the p value is corresponds to if there were as many subjects as there were observations. The subject id variable is properly recognized and understood as factor when I check the structure of the data frame. Does anybody know how to fix this?</p>
  
  <p><strong>Long df example</strong></p>
  
  <pre><code>  subject sex condition    x y
  1       1   M   control  7.9 1
  2       1   M     cond1 12.3 2
  3       1   M     cond2 10.7 3
  4       2   F   control  6.3 4
  5       2   F     cond1 10.6 5
  6       2   F     cond2 11.1 6
  </code></pre>
  
  <p><strong>Here is the code</strong></p>
  
  <pre><code>library(ggplot2)
  library(ggpubr)
  scatter &lt;- ggplot(df, aes(x = x, y =   y)) + 
    geom_point(aes(colour = condition)) +
    geom_smooth(method = "lm") + 
    ggtitle("title") + 
    theme(axis.text=element_text(size=14),
          axis.title=element_text(size=14,face="bold"),
          plot.title = element_text(size = 20, face = "bold"))
  
  scatter + stat_cor(method = "pearson", label.x = -2, label.y = 3)
  </code></pre>
  
  <p>having plot + scat_cor(method = "pearson"...) should calculate the pearson value of x and y (this page has the formula correct: <a href="http://www.stat.wmich.edu/s216/book/node122.html" rel="nofollow noreferrer">http://www.stat.wmich.edu/s216/book/node122.html</a>) 
  it is calculating the p value as if the sample size, n , is the number of observations in long frame df.</p>