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I have a dataframe in the following form: 

|Regions | F1 | F2 | F3 | ... | Fn |
|Region_1| X11| X12| X13| ... | X1n|
|Region_2| X21| X22| X23| ... | X2n|
...
|Region_k| Xk1| Xk2| Xk3| ... | Xkn|

Where every row represents an Italian region and every column is a feature that the individuals living in that region have. The value $X_{ij}$ is the number of individuals living in the region i and having the feature j, so the sum of each row gives the popolation living in that region; the sum of each column gives the people having that feature.

Boxplot depicts groups of numerical data through their quartiles. It is correct to create a boxplot on the columns of my data? It gives the correct information or I have to apply some transformation on my data (e.g., scaling, normalization, etc)?

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  • $\begingroup$ Every Xij is an absolute frequency. $\endgroup$
    – dariodip
    Commented Nov 26, 2016 at 11:45
  • $\begingroup$ Do you want to summarise each column or do you want to compare the distributions? $\endgroup$
    – mdewey
    Commented Nov 27, 2016 at 13:44
  • $\begingroup$ I want to summarise each column $\endgroup$
    – dariodip
    Commented Nov 27, 2016 at 14:50

2 Answers 2

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The Boxplot will aggregate your values by columns so if you want to know for each feature the distribution of people with that characteristic, you can do it and it could be useful, but it's still not clear to me what do you mean with "it make sense and it gives the correct informations".

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  • $\begingroup$ these data are absolute frequencies, it is correct to use a boxplot on them? $\endgroup$
    – dariodip
    Commented Nov 26, 2016 at 11:42
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To Fracesco's answer I would add that if features are measured in different scales (example: height, weight, income, number of children...), each boxplot will make sense in its own but comparing them in the same graph and the same scale won't make sense - and that is what boxplot(myDataFrame) does.

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