<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.sstatic.net/uUDCR.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/uUDCR.png" alt="enter image description here"></a></p>

CC BY-SA 4.0
2018-11-23T19:43:16.067Z
yes, this is the mistake I'm doing. I removed geom_point(). It looks fine now. thanq
beginner
CC BY-SA 4.0
2018-11-23T17:17:40.800Z
2018-11-23T17:17:40.800Z
Mr_Z
<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.sstatic.net/0wYwx.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/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.sstatic.net/wJPmQ.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/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>

CC BY-SA 4.0
2018-11-23T13:52:44.467Z
I don't know from where does the `ggboxplot` function comes from or `stat_compare_means`? Also please, continue [edit]ing the question to clarify the question. It is still unclear to me if you want to remove some points, add them or something else
llrs
CC BY-SA 4.0
2018-11-23T14:22:39.270Z
from the package ggpubr
beginner
CC BY-SA 4.0
2018-11-23T17:46:41.970Z
I am confused by the wording in "May I know why I see those dots in the second boxplot after removing the jitter. Is there a way to avoid that?" Are you trying to ask  "Why did the points not disappear after I remove jitter?"
winni2k
<r><rna-seq><microarray><ggplot2>
Question about the dots on Quartile groups in boxplot
# Question about the dots on Quartile groups in boxplot

(Asked by: username_0 on 2018-11-23)

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?

**Tags:** r, rna-seq, microarray, ggplot2
> **Comments:**
> **username_1**: I don't know from where does the `ggboxplot` function comes from or `stat_compare_means`? Also please, continue [edit]ing the question to clarify the question. It is still unclear to me if you want to remove some points, add them or something else  
> **username_0**: from the package ggpubr  
> **username_2**: I am confused by the wording in "May I know why I see those dots in the second boxplot after removing the jitter. Is there a way to avoid that?" Are you trying to ask  "Why did the points not disappear after I remove jitter?"  

---

## 1 answers

### Answer by username_3 on 2018-11-23. Score: 3 | Accepted answer

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.

> **Comments:**
> **username_0**: yes, this is the mistake I'm doing. I removed geom_point(). It looks fine now. thanq  

---

