text: user1369: I have Microarray Normalized Expression data for a specific Gene. It looks like below in a dataframe B
  SampleID    Gene    Type
  Sample1     5.02    Tumor
  Sample2     5.06    Tumor
  Sample3     5.1     Tumor
  Sample4     5.11    Tumor
  Sample5    5.127    Normal
  Sample6     5.12    Normal
  Sample7    5.138    Normal
  Sample8    5.149    Normal
  
  I see that the minimum expression value in the table is 5.0 and maximum expression value is around 5.9.
  I wanted to show the expression between two conditions with a boxplot and used following code.
  q <- ggboxplot(B, x = "Type", y = "Gene",
                 color = "black", palette = "npg",
                 add = "jitter", ylab = 'Gene expression', xlab=FALSE,
                 order=c("Normal", "Tumor"))
  q + stat_compare_means(method = "t.test") + 
    geom_point() + 
    stat_n_text()
  
  This gave a plot like this 
  But when I remove the jitter from the code, 
  q <- ggboxplot(B, x = "Type", y = "Gene",
                 color = "black", palette = "npg",
                 ylab = 'Gene expression', xlab=FALSE,
                 order=c("Normal", "Tumor"))
  q + stat_compare_means(method = "t.test") + 
    geom_point() + 
    stat_n_text()
  
  I see many black dots in the middle of the boxplot like this 
  May I know why I see those dots in the second boxplot after removing the jitter. Is there a way to avoid that?
  
  user3360: You add the points with geom_point(). Just remove it and you will get your "empty" boxplot. 
  q <- ggboxplot(B, x = "Type", y = "Gene",
                 color = "black", palette = "npg",
                 ylab = 'Gene expression', xlab=FALSE,
                 order=c("Normal", "Tumor"))
  
  Unfortunately I couldn't use stat_compare_means(method = "t.test") and     stat_n_text(), but this two just add the labels.
  
  
original_text: 1369: <p>I have Microarray Normalized Expression data for a specific Gene. It looks like below in a dataframe <code>B</code></p>
  
  <pre><code>SampleID    Gene    Type
  Sample1     5.02    Tumor
  Sample2     5.06    Tumor
  Sample3     5.1     Tumor
  Sample4     5.11    Tumor
  Sample5    5.127    Normal
  Sample6     5.12    Normal
  Sample7    5.138    Normal
  Sample8    5.149    Normal
  </code></pre>
  
  <p>I see that the minimum expression value in the table is 5.0 and maximum expression value is around 5.9.</p>
  
  <p>I wanted to show the expression between two conditions with a boxplot and used following code.</p>
  
  <pre><code>q &lt;- ggboxplot(B, x = "Type", y = "Gene",
                 color = "black", palette = "npg",
                 add = "jitter", ylab = 'Gene expression', xlab=FALSE,
                 order=c("Normal", "Tumor"))
  q + stat_compare_means(method = "t.test") + 
    geom_point() + 
    stat_n_text()
  </code></pre>
  
  <p>This gave a plot like this <a href="https://i.stack.imgur.com/0wYwx.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/0wYwx.png" alt="Boxplot1"></a></p>
  
  <p>But when I remove the <code>jitter</code> from the code, </p>
  
  <pre><code>q &lt;- ggboxplot(B, x = "Type", y = "Gene",
                 color = "black", palette = "npg",
                 ylab = 'Gene expression', xlab=FALSE,
                 order=c("Normal", "Tumor"))
  q + stat_compare_means(method = "t.test") + 
    geom_point() + 
    stat_n_text()
  </code></pre>
  
  <p>I see many black dots in the middle of the boxplot like this <a href="https://i.stack.imgur.com/wJPmQ.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/wJPmQ.png" alt="Boxplot2"></a></p>
  
  <p>May I know why I see those dots in the second boxplot after removing the jitter. Is there a way to avoid that?</p>
   3360: <p>You add the points with <code>geom_point()</code>. Just remove it and you will get your "empty" boxplot. </p>
  
  <pre><code>q &lt;- ggboxplot(B, x = "Type", y = "Gene",
                 color = "black", palette = "npg",
                 ylab = 'Gene expression', xlab=FALSE,
                 order=c("Normal", "Tumor"))
  </code></pre>
  
  <p>Unfortunately I couldn't use <code>stat_compare_means(method = "t.test")</code> and     <code>stat_n_text()</code>, but this two just add the labels.</p>
  
  <p><a href="https://i.stack.imgur.com/uUDCR.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/uUDCR.png" alt="enter image description here"></a></p>
  