Visualizing categorical data with 7 variables I'm working with a grouped data set on the presence of a particular disease (Byssinosis):
>head(data)

    Employment Smoking Sex Race Workspace Byssinosis Non.Byssinosis
 1        <10     Yes   M    W         1          3             37
 2        <10     Yes   M    O         1         25            139
 3        <10     Yes   F    W         1          0              5
 4        <10     Yes   F    O         1          2             22
 5        <10      No   M    W         1          0             16
 6        <10      No   M    O         1          6             75

>str(data)
'data.frame':    72 obs. of  7 variables:
  $ Employment    : Factor w/ 3 levels "<10",">=20","10-19": 1 1 1 1 1 1 1 1 3 3 ...
  $ Smoking       : Factor w/ 2 levels "No","Yes": 2 2 2 2 1 1 1 1 2 2 ...
  $ Sex           : Factor w/ 2 levels "F","M": 2 2 1 1 2 2 1 1 2 2 ...
  $ Race          : Factor w/ 2 levels "O","W": 2 1 2 1 2 1 2 1 2 1 ...
  $ Workspace     : int  1 1 1 1 1 1 1 1 1 1 ...
  $ Byssinosis    : int  3 25 0 2 0 6 0 1 8 8 ...
  $ Non.Byssinosis: int  37 139 5 22 16 75 4 24 21 30 ...

And here is a description of my data set:
In 1973, a large cotton textile company in North Carolina participated in a study to investigate the prevalence of byssinosis, a form of pneumoconiosis to which workers exposed to cotton dust are subject. Data was collected on 5,419 workers.
Type of work place [1 (most dusty), 2 (less dusty), 3 (least
dusty)]
Employment, years [< 10, 10–19, 20+]
Smoking [Smoker, or not in last 5 years]
Sex [Male, Female]
Race [White, Other]
Byssinosis [number of cases having the disease]
Non.Byssinosis [number of cases not having the disease] 
I plan on running a logistic regression on this data set to see the relationship between my response variable and my predictor variables, but I want to visualize my data before regressing. What would be the best way to do that considering I have multiple variables with some having multiple factors? 
 A: It's going to be difficult to consider multiple variables at the same time as it gets messy pretty quick. 
If you consider one independent variable at a time I would recommend a stacked bar graph that stacks the percentage of people within a category that do and do not get the disease with each bar representing a category. 
You would then for example see the percentage of smokers that do and do not get the disease (of the total of all smokers) and next to that the same for the non-smokers. By looking at percentages within a category you take away the fact that you may have more people in one category than in another and only look at how often the disease occurs relatively within the different categories. 
This visualization easily adapts to multiple categories, you'd just have more bars.
If you really want to consider multiple independents at the same time you may be able to pull that off by "merging" categories looking e.g. at the relative prevalence of the disease in the category smokers that work in a dusty work place vs smokers that do not work in a dusty work place vs non-smokers that do work in a dusty work placevs non-smokers that do not work in a dusty work place, but as I said that gets messy pretty quickly. 
EDIT: 
As a visual explanation here is a stacked bar graph of the survival rates by gender of the titanic (data from here )

This clearly shows that they had a policy of women (and children?) first.
The following graph combines gender and passenger/crew type, showing how it becomes messier when you want to show all categories (note that some categories have 0 count e.g. female deck crew, which is why the colums are missing), but the gender effect is still present as is the fact that survival rates are higher for the "upper class" passengers and (perhaps unsurprising)  the deck crew.

