Testing useful variable before giving to model

I am doing logistic regression in r and want to give the best features to the model from a list of 200 variables and 25,000 records. A continuous variable(scc) is having 90 % 0’s. Following is the summary.

Min 0

1st Qu: 0

Median: 0

3rd Qu: 0

Max: 130

The rest 10% which are non-zero ranging from 1 to 130 and have close to 2500 records. Is these variable useful in predicting dependent variable as most of the values are 0 and if not how to test that before passing it to model.

what I did is used conditional box plot to compare the distribution conditioned on whether the dependent variable is 1 or 0 but the boxplot for both looks same.

library(fields)

bplot.xy(data $dependent,data$ scc)

This is a form of cheating. This has been discussed extensively on this site. Variable selection that is utilizing relationships with $Y$ will result in serious overfitting and badly biased standard errors. Solutions include data reduction masked to $Y$ and penalization (shrinkage). My RMS course notes go into this in detail - see http://www.fharrell.com/p/blog-page.html .