ggplot Chi Square for a DF and a special Level of Significance I am working on Datascience course on R (Jupiter Notebook ), and I would like to plot the Chi Square for learner in order to let them feel the meaning of Null and Alternative hypothesis. 
So I would like to graph (ggplot if possible ), with a grayed  zone, a Chi Square for a special DF and a Level of significance on R program. 

 A: The question has gaps and inconsistencies. I have made guesses in order
to give an answer that I hope is helpful.
Although you do not say so, you may have a random sample of size $n=25$
from a normal population with sample variance $S^2 = 12.25$ and wish to do a test about the sample variance $\sigma^2:$ Specifically, to test
$H_0: \sigma^2 \ge 7.2^2$ against $H_a: \sigma^2 < 7.2^2$ at the 5% level
of significance.
Then then you have $\frac{(n-1)S^2}{\sigma^2} \sim \mathsf{Chisq}(\nu=24).$ 
Because quantile .05 of this distribution is 13.85, you reject $H_0$
at the 5% level because the test statistic $\frac{24(12.25)}{7.2^2} = 5.67$ is smaller than the critical value 13.85.[This is hardly a surprising result because the sample variance $S^2 = 12.25$ is much smaller than the hypothetical value of the population variance $\sigma^2 = 7.2^2 =  51.84.]$
qchisq(.05, 24)
[1] 13.84843

24*12.25/7.2^2
[1] 5.671296

The figure below shows the density function of $\mathsf{Chisq}(\nu=24),$
along with the critical value (dotted red line) and the test statistic
(solid black). The area to the left of the red line is 0.05. 

R code for figure:
curve(dchisq(x, 24), 0, 75, lwd=2, 
    ylab="PDF", xlab="Chi-sq", main="Density of CHISQ(24)")
 abline(h=0, col="green2"); abline(v=0, col="green2")
 abline(v=13.85, col="red", lwd=2, lty="dotted")
 abline(v = 5.67, lwd=2)

Notes: There are inconsistencies in your graph. The axes are not labeled. The only chi-squared distribution for which the 5% critical value is 13.85 has 24 degrees of freedom, which does not match the shape of your density curve. (There is no chi-squared distribution with 5% lower critical value 5.67.) It seems that your figure reverses labels for the test statistic and the critical value. 
Below is output from Minitab statistical software. [Some unnecessary items in the output have been edited out; Minitab shows a P-value rather than a critical value.]
Test for One Variance 

Method

Null hypothesis         σ = 7.2
Alternative hypothesis  σ < 7.2

Statistics

 N  StDev  Variance
25   3.50     12.25

Test

                 Test
Method      Statistic  DF  P-Value
Chi-Square       5.67  24    0.000

