frmla <- bad_econ ~ blah1 + blah2 + blah3 + [etc...]

fit = rpart(frmla, method='class', data=datas)

printcp(fit) # display the results
plotcp(fit) # visualize cross-validation results
summary(fit) # detailed summary of splits
fit

pred = predict(fit)
table(pred, datas$bad_econ)

pred 0  1
  0  x  y
  1  z  a

pred                 0  1
  0                  8  0
  0.2                4  1
  0.333333333333333  8  4
  0.4                6  4
  0.666666666666667  5 10
  0.714285714285714  2  5
  0.782608695652174  5 18
  0.8                1  4
  0.857142857142857  1  6
  0.928571428571429  1 13
  1                  0 93

Residual mean deviance:  0.09359 = 17.13 / 183
Distribution of residuals:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-0.9286  0.0000  0.0000  0.0000  0.1429  0.8000

pred = predict(fit, type = "class")
table(pred, datas$bad_econ)