Understanding the shape of the distribution of a random variable What plots can I draw to understand the shape of the distribution of a random variable?
I do know that histograms can be plotted to do the above. But can a box plot and a violin plot be plotted as well to help me understand the shape of the distribution?
 A: The goal of any kind of plotting is to check how the data is distributed with respect to the parameter that we are looking for. For example the plot for Time Series Data will be different as compared to the plot for checking the frequency of different data points in a dataset.  
So taking into consideration Box plots, lets look at what they represent.

So looking at it from the point of view of understanding distributions, we can see that the graph would be peaking around the Left (imagine flipping the boxplot clockwise 90 degrees). Hence it is a Right-Skewed Distribution.
Similarly, Violin plot would look like:

Correction:  In the Dark Blue Violin Plot, instead of Upper IQR it is the Upper Quantile, and similarly for Lower IQR it is Lower Quartile.
Here we can see the median is given in the plot, which is one of the measures for checking if a distribution is skewed or not.
So coming to your question, if you know what you are looking for, Boxplots and Violin Plots are a great alternative to check if your data is skewed or not.
As per the suggestions of Mr. Martijin Weterings, I replaced the original boxplot with a custom made boxplot made from the Prima Diabetes Dataset. 
A: Yes, above mentioned plots are helpful. Another famous way is through Kernel Density Estimation. In which Kernel and Bandwidths are involved. For more detail check
https://en.wikipedia.org/wiki/Kernel_density_estimation.
Different packages are available in R, which can be directly used for this purpose, like, KernSmooth, ks,np and etc. 
