# 2 levels, treatment contrasts (default)
# here, by luck, our baseline level happens to be alphabetically first, so we get a sensible
# assignment of treatment=1.
foo <- factor(c("treatment", "control"))
contrasts(foo)
#           treatment
# control           0
# treatment         1

# 2 levels, sum contrasts (better)
contrasts(foo) <- contr.sum
contrasts(foo)
#           [,1]
# control      1
# treatment   -1

# 2 levels, deviation contrasts (preferred way)
contrasts(foo) <- matrix(c(-0.5, 0.5), nrow=2, dimnames=list(c("control", "treatment"), "predictor"))
contrasts(foo)
#           predictor
# control        -0.5
# treatment       0.5

# 3 levels, dummy coding (default)
# here, by sheer luck, a sensible choice for baseline category happens to also be the alphabetically
# first level of the factor, so you get a reasonable dummy coding.
foo <- factor(c("disease+placebo", "disease+treatment", "controls+placebo"))
contrasts(foo)
#                   disease+placebo disease+treatment
# controls+placebo                0                 0
# disease+placebo                 1                 0
# disease+treatment               0                 1

# 3 levels, planned contrasts
foo <- factor(c("disease+placebo", "disease+treatment", "controls+placebo"))
contrasts(foo) <- matrix(c(0, -1/2, 1/2, -2/3, 1/3, 1/3), nrow=3,
                         dimnames=list(levels(foo), c("treatment", "disease")))
contrasts(foo)
#                   treatment    disease
# controls+placebo        0.0 -0.6666667
# disease+placebo        -0.5  0.3333333
# disease+treatment       0.5  0.3333333