How do I interpret the output of `glm` in R? I'm very new to R and get some trouble to interpret. Here is the code
Crabs <- read.table("http://www.stat.ufl.edu/~aa/cat/data/Crabs.dat", 
                      header = TRUE)
fit <- glm(y ~ width + factor(color), family = binomial, data = Crabs)
summary(fit)

And the output is
Call:
glm(formula = y ~ width + factor(color), family = binomial, data = Crabs)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1124  -0.9848   0.5243   0.8513   2.1413  

Coefficients:
                Estimate Std. Error z value Pr(>|z|)    
(Intercept)    -11.38519    2.87346  -3.962 7.43e-05 ***
width            0.46796    0.10554   4.434 9.26e-06 ***
factor(color)2   0.07242    0.73989   0.098    0.922    
factor(color)3  -0.22380    0.77708  -0.288    0.773    
factor(color)4  -1.32992    0.85252  -1.560    0.119    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 225.76  on 172  degrees of freedom
Residual deviance: 187.46  on 168  degrees of freedom
AIC: 197.46

Number of Fisher Scoring iterations: 4

Q1: In sector 'Coefficients:' there is no 'factor(color)1' but others. Why?
Q2: What's the difference between 'Null deviance' and 'Residual deviance'
Q3: What does 'AIC' mean?
Could I please have some help? Short answer is fine
 A: Your questions are already answered in several threads on the site, so I'll provide links to detailed explanations:

*

*There is no factor(color)1 because the categorical variable is dummy coded, so one of the categories is always dropped.

*Your second question is answered in Interpreting Residual and Null Deviance in GLM R.

*AIC stands for Akaike Information Criterion, it is a log-likelihood penalized by the number of parameters of the model, and it is used for model selection.

A: 
Q1: In sector 'Coefficients:' there is no 'factor(color)1' but others. Why?

Because it is included in the intercept. It's similar to a situation where you have a continuous variable - the intercept is the expected value when the variable is zero. With categorical variables we call the level which is included in the intercept the "reference level" and the estimates for the other levels are the expected difference in the response between the reference level and the estimated level

Q2: What's the difference between 'Null deviance' and 'Residual deviance'

Null deviance relates to a model with no explanatory variables whereas the residual deviance is for the model that you fitted.

Q3: What does 'AIC' mean?

AIC stands for Akaike Information Criterion. It is a measure of goodness of fit.
