Learning R cheatsheet

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 name

Types/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 workspace

File 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

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