1 R
Language and environment for statistical computing and graphics
- Full-featured programming language
- Interactive and interpretted – convenient and forgiving
- Coherent, extensive documentation
- Statistical, e.g.
factor(),NA - Extensible – CRAN, Bioconductor, github, …
1.1 Vectors, classes, objects
- Efficient vectorized calculations on ‘atomic’ vectors
logical,integer,numeric,complex,character,byte - Atomic vectors are building blocks for more complicated objects
matrix– atomic vector with ‘dim’ attributedata.frame– list of equal length atomic vectors- Formal classes represent complicated combinations of vectors, e.g., the return value of
lm(), below
1.2 Functions, generics, methods
- Functions transform inputs to outputs, perhaps with side effects, e.g.,
rnorm(1000) - Argument matching first by name, then by position
- Functions may define (some) arguments to have default values
- Generic functions dispatch to specific methods based on class of argument(s), e.g.,
print(). - Methods are functions that implement specific generics, e.g.,
print.factor; methods are invoked indirectly, via the generic.
1.3 Introspection
- General properties, e.g.,
class(),str() - Class-specific properties, e.g.,
dim()
1.4 Help
?print: help on the generic print?print.data.frame: help on print method for objects of class data.frame.
Example
x <- rnorm(1000) # atomic vectors
y <- x + rnorm(1000, sd=.5)
df <- data.frame(x=x, y=y) # object of class 'data.frame'
plot(y ~ x, df) # generic plot, method plot.formula
fit <- lm(y ~x, df) # object of class 'lm'
methods(class=class(fit)) # introspection## [1] add1 alias anova case.names coerce confint
## [7] cooks.distance deviance dfbeta dfbetas drop1 dummy.coef
## [13] effects extractAIC family formula fortify hatvalues
## [19] influence initialize kappa labels logLik model.frame
## [25] model.matrix nobs plot predict print proj
## [31] qr residuals rstandard rstudent show simulate
## [37] slotsFromS3 summary variable.names vcov
## see '?methods' for accessing help and source code