Getting help:
help(x) or ?x # help on function `x`
example(x) # print an example of using `x`
??x # search help for instances of string x
apropos('x') # list all objects with `x` in the nameTypes/objects:
mode(x) # type of an object (storage mode)
str(x) # display the structure of an object
ls() # list the objects in the current workspace
rm(x) # delete the object from curren workspaceFile management:
getwd() # list working directory
setwd('dir') # set working directory
dir() # list directories
dir.create(...) # create a directory
Workspace:
save(...) # save objects to a file
save.image() # save entire image to a file
load('file') # load objects written by save
history() # display last few commands
savehistory('f') # save history to a file
loadhistory('f') # load history from a file
options() # list available options (globals)
options(x=3) # set an option
q() # quit session
Stream management:
source('f') # run commands from file `f`
sink('f', split=TRUE) # Tee output into a file
Package management:
.libPaths() # dir where are packages saved
installed.packages() # see details/versions/etc.
install.packages() # installation of packages
update.packages() # updating to latest
library() # list of installed packages
library(x) # load package
help(package='x') # get help on a package
Basic stats/math:
data() # list available datasets
runif(x) # generate x uniformly distributed numbers
rnorm(x) # generate x normally distributed numbers
summary(x) # print summary info for statistical objects
lm(x~y[, data=z]) # linear regression
integrate(f, i, j) # integrate `f` in range
Plotting basics:
dev.new() # create new plotting device and set active
def.off() # delete the last plotting device
png/pdf('x') # write graphics to a file
hist(x) # compute a histogram object (and plot by default)
plot(x, y) # plot `x` against `y`
plot(x~y) # plot `x` against `y`
plot(x~y, data=z) # plot `x` against `y` from dataframe
abline(...) # add a line to plot
curve(dnorm, -4, 4) # plot a function
Vectors:
x<-c(1, 2, 3) # constructor
x[1] # 1-based indexes
x[5]<-5 # expansion
x[c(1,2)] # get multiple indexes
1:5 # range (inclusive)
Factors:
# efficient storage of low cardinality
factor(c('x', 'y', 'z', 'x'))
Data frames:
data.frame(v1, v2) # populate a two-column data frame
names(x)<-c('a', 'b') # name the columns
x[2] # get a column
x['b'] # get a column
x$b # get a column
x[2:3] # get multiple columns
x[,2:3] # get multiple columns
with(x, { a }) # refer to a column, save typing
x[2:3,] # get multiple rows
