control flow:
if()
for()
while()

command list:
?: get help
abline(): add straight lines to a plot
abs(): computes the absolute value
anova(): analysis of variance
apply(): apply functions over array margins
as.dist(): converts a distance matrix to an object of class dist
as.factor(): converts its argument to an object of class factor
as.formula(): converts its argument to an object of class formula
as.numeric(): converts its argument to an object of class numeric
attach(): data frame is added to the R search path
attr(): access an attribute of an object
attributes(): access attributes of an object
biplot(): a generalization of the scatterplot to two pairs of axes
boot(): bootstrap resampling
bs(): basis spline [for a gam formula]
c(): combine values
cbind(): combine columns of objects
coef(): extract model coefficients
coefficients(): an alias for coef()
confint(): confidence intervals for model parameters
contour(): create a contour plot
contrasts(): display encodings for a factor
cor(): correlation
cos(): cosine
cov(): covariance
cumsum(): cumulative sum
cut(): convert numeric to factor
cutree(): cut a tree into groups
cv.glm(): cross validation for generalized linear models
cv.glmnet(): cross validation for generalized linear models with elastic net regularization
cv.tree(): cross validation for choosing classification/regression tree complexity
data.frame(): create a data frame (a generalization of a matrix to mixed data types)
dev.off(): shuts down a graphics device
dim(): displays the dimensions of an object (e.g. the number of rows and columns)
dist(): computes a distance matrix
exp(): exponential function (based on euler's number: 2.718282)
fix(): invokes an editor
function(): creates a new user defined function
gam(): generalized additive model
gbm(): gradient boosting machine
glm(): generalized linear model
glmnet(): generalized linear model with elastic net regularization
hatvalues(): extracts values from the diagonal of the hat matrix
hclust(): hierarchical agglomerative clustering
hist(): computes a histogram
I(): inhibits interpretation (e.g. arithmetic operators instead of formula operators)
identify(): identifies points in a scatter plot
ifelse(): conditional element selection
image(): displays a grid of colors
importance(): extract a variable importance measure
is.na(): indicates absence/presence of missing values
jitter(): add a small amount of noise
jpeg(): configure a joint photographic experts group (jpeg) format output file
kmeans(): k means clustering
knn(): k nearest neighbor
lda(): linear discriminant analysis [not latent dirichlet allocation (for this class)]
legend(): add a legend to a plot
length(): length of a vector
library(): load and attach an add-on package
lines(): add a line segment to a graph
list(): create a list
lm(): linear model
lo(): loess fit [for a gam formula]
loadhistory(): load a previously saved command history
loess(): local regression
log(): logarithm using euler's number for the base (2.718282)
ls(): list objects
matlines(): plot columns of matrices
matrix(): creates a matrix
max(): selects the max value
mean(): computes the arithmetic mean
model.matrix(): expands factors to dummy variables
names(): retrieves the names for an object
na.omit(): omits rows containing missing values
nrow(): number of rows
ns(): natural splines [for a gam formula]
outer(): outer product of arrays
pairs(): scatterplot matrix
par(): graphical parameters
paste(): concatenate strings
pcr(): principal components regression
pdf(): pdf graphics device
performance(): evaluation of predictions
persp(): perspective plot (for 3 dimensional data)
plot(): scatter/line plot
plot.gam(): plot components of a gam object
plsr(): partial least squares regression
points(): plot points
poly(): compute orthogonal polynomials
prcomp(): principal components analysis
predict(): make predictions (behavior varies by class; e.g. predict.glmnet())
prediction(): creates a prediction object
print(): print values
prune.misclass(): cost complexity pruning of a tree, based on misclassifications
prune.tree(): cost complexity pruning of a tree, based on deviance
q(): quit R
qda(): quadratic discriminant analysis
rainbow(): create a vector of contiguous colors
randomForest(): random forest (bagging with random subspace projections)
range(): computes minimum and maximum values
rbind(): combine rows of objects
read.csv(): read a comma separated value text file
read.table(): read a text file
regsubsets(): feature selection [forward, backward, best]
rep(): replicate elements
residuals(): extract model residuals
return(): return a value from a user defined function
rm(): remove objects
rnorm(): random number generation for the normal (gaussian) distribution
row.names(): row names for a data frame
rstudent(): "studentized" residuals
s(): smoothing spline [for a gam formula]
sample(): random samples and permutations
savehistory: save the command history for the current session
scale(): centering (subtracting the mean) and scaling (e.g. dividing by the standard deviation)
sd(): standard deviation
seq(): sequence generation
set.seed(): specify an initial seed for the random number generator (for repeatability)
smooth.spline(): smoothing spline [see equation 5.78 in Elements of Statistical Learning]
sqrt(): square root
sum(): sum
summary(): summarizes an object
svm(): support vector machine
t(): transpose
table(): creates a contingency table
text(): add text to a plot
title(): add labels to a plot
tree(): classification or regression tree (depending on type of response)
tune(): parameter tuning using grid search
unique(): unique values
update(): update a model
validationplot(): plots validation statistics
var(): variance
varImpPlot(): variable importance plot
vif(): variance inflation factors
which.max(): returns index of the maximum value
which.min(): returns index of the minimum value
write.table(): create a text output file