How to ignore null values in R?

Question

I have a data set with some null values in one field. When I try to run a linear regression, it treats the integers in the field as category indicators, not numbers.

E.g., for a field that contains no null values...

summary(lm(rank ~ num_ays, data=a)),

Returns:

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 10.607597   0.019927 532.317  < 2e-16 ***
num_ays      0.021955   0.007771   2.825  0.00473 ** 

But when I run the same model on a field with null values, I get:

Coefficients:
              Estimate Std. Error  t value Pr(>|t|)    

(Intercept)  1.225e+01  1.070e+00   11.446  < 2e-16 ***
num_azs0    -1.780e+00  1.071e+00   -1.663  0.09637 .  
num_azs1    -1.103e+00  1.071e+00   -1.030  0.30322    
num_azs10   -9.297e-01  1.080e+00   -0.861  0.38940    
num_azs100   1.750e+00  5.764e+00    0.304  0.76141    
num_azs101  -6.250e+00  4.145e+00   -1.508  0.13161    

What's the best and/or most efficient way to handle this, and what are the tradeoffs?

Answer 1

You can ignore null values like so:

a[!is.null(a$num_ays),]

Answer 2

And to build on Shane's answer: you can use that in the data= argument of lm():

summary(lm(rank ~ num_ays, data=a[!is.null(a$num_ays),]))