Even though there are more flexible classification techniques, logistic regression remains popular. It's fast, it's interpretable, and it is much easier to do inference (such as constructing confidence intervals) other than prediction with logistic regression than more modern machine learning techniques. Although logistic regression is covered as a subtopic in other books, if you use it a lot you will benefit from a dedicated resource that gives applicationspecific advice.
Recommended Books

Applied Logistic Regression
David W. Hosmer Jr., Stanley Lemeshow, and Rodney X. Sturdivant
Key Features
 Intext exercises
Key Topics
 Area Under the Receiver Operator Characteristic (ROC) Curve
 Bayesian Logistic Regression
 CaseControl Studies
 Cohort Studies
 Confidence Intervals
 Diagnostics
 Fitting
 Goodness of Fit
 Hypothesis Testing
 Interaction
 Interpretation
 Logistic Regression for Correlated Data
 Matched CaseControl Studies
 Mediation
 Missing Data
 Multinomial Logistic Regression
 Multiple Logistic Regression
 Ordinal Logistic Regression
 Other Link Functions
 Propensity Score Methods
 Sample Size Issues
 Variable Selection
Description
This is an excellent practical guide for using logistic regression. As you would expect, construction and fitting of logistical regression are neatly introduced, as are the usual regression tests. More importantly, this book covers the interpretation of the model, including in the case of correlated data. Many useful fit diagnostics are discussed, and there is a useful discussion of alternative link functions and the Bayesian viewpoint on logistic regression (the Bayesian section could use some expansion). We found the exercises interesting, but there is little in the way of actual code support (there is some discussion of software packages). However, for most of the primary techniques, it isn't that hard to track down R packages that are suitable.