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R Commands Cheat Sheet (DRAFT) by

Basic R commands used in a lecture on statistical programming.

This is a draft cheat sheet. It is a work in progress and is not finished yet.

Basic Math

exp(x)
Expone­ntial
 
sum(x)
Sum
 
log(x)
Natural log
 
cumsum(x)
Cumulative Sum
 
max(x)
Largest element
 
ceil(x)
Round up
 
min(x)
Smallest element
 
floor(x)
Round down
 
x %% y
Modulo

Control Flow

for (variable in sequence) {...}
for-loop. If the loop body contains only a single line, the curly brackets can be omitted.
 
while (condi­tion) {...}
while-loop
 
if (i > 5) {

  ...

else {

  ...

}
if-els­e-block
 
foo = functi­on(­arg1, arg2, ...) {

  ...

  return­(var)

}
function

Vectors

Creating Vectors
 
c(2, 4, 6)
Join elements into a vector
 
2:6
An integer sequence (end inclus­ive!)
 
seq(2,3, by=0.5)
Complex sequence (s. np.lin­space)
 
rep(1:2, 3)
Repeat vector
 
rep(1:2, 3:4)
Repeat each element
Functions
 
sort(x)
Return x sorted.
 
rev(x)
Return x reversed.
 
unique(x)
See unique values.
 
length(x)
Length of x.
Selecting Vector Elements
By Position
 
x[4]
The fourth element
 
x[-4]
All but the fourth.
 
x[2:4]
Elements two to four
 
x[-(2:4)]
All elements except 2 to four
 
x[c(1, 5)]
Elements one and five.
By Value
 
x[x == 10]
All elements equal to 10
 
x[x  < 10]
All elements less than 10.
 
x[x %in% c(1, 2, 5)]
Elements in the given set.
Named Vectors
 
x['apple']
Element with name 'apple'.

Tables

table(­data)
get absolute freque­ncies of values
 
as.num­eri­c(tab); as.vec­tor­(tab)
Extract values and their absolute freque­ncies from table
 
tab/le­ngt­h(data)
Compute relative freque­ncies

Matrices

m = matrix(x, nrow = 3, ncol = 3)
Create a matrix from vector 
x
 
t(m)
transpose
 
m %*% n
Matrix multip­lic­ation
 
solve(m, n)
Find x in 
m * x = n
 
det(m)
Determ­inant
 
eigen(m)
Find eigen vectors and values

Data sets

Data=d­ata.fr­ame­(pr­ice­=c(­11,­20,­14,15), number­=c(­40,­50,­60,20))
Create a data set
Intera­cting with data sets
 
col_1 = data$c­ol_­1_name
Access column data
 
Data[1,2]; Data[,2]; Data[[1]]
Access data with index notation
I/O
 
data = read.c­sv(­"­fil­e.c­sv", header­=FALSE, sep="")
Read csv (function arguments similar to that used in pandas)
 
write.c­sv­(data, "­dat­a.c­sv", row.na­mes­=FALSE, sep=" ")
Write data set as csv
Filter
 
df[df$kids == "­Jac­k",]
Filter data frame
 
subset(df,  kids== "­Joh­n" & Grade == 1.3)
Filter multiple columns
 
subset(df, kids %in% c("J­ack­", "­Joh­n"))
Filter a column with multiple values
 
unique­(ho­using[, c("S­tat­e", "­reg­ion­")])
Extract unique rows
Sort
 
housin­g[o­rde­r(h­ous­ing­$Ho­me.V­alue), ]
Order data frame in ascending order
 
housin­g[o­rde­r(h­ous­ing­$Ho­me.V­alue, decreasing = TRUE), ]
order data frame in descending order
Meta
 
dim(df)
Check the dimensions of a data frame
 
colnam­es(d)
Return the column names
Manipulate data
 
Data_F­ram­e_New <- Data_F­ram­e[-­c(1), -c(1)]
Remove columns and/or rows from data frame
 
rbdind­(df_1, df_2)
Combine data frames vertically

I/O

write(­data, "­myd­ata.da­t")
Write data as binary.
 
scan("m­yda­ta.d­at­")
Read binary data.
 
getcwd()
Current working directory

Random Numbers

sample­(1:­3,p­rob­=c(­1/6­,1/­3,1­/2)­,re­pla­ce=­TRU­E,20)
Draw 20 balls, labeled from 1 to 3, from box with replac­ement.
 
r<d­istr. ID>
(n, params)
Draw 
n
numbers from distri­bution 
<distr. ID>
with parameters 
params
(see Distri­butions in R for more details)

Charac­ter­istics of data sequences

mean(x)
Arithmetic mean of the data sequence
 
var(x)
Variance
 
median(x)
Median
 
quanti­le(x, type=7)
Quantile. 
type=7
is the default comput­ation algorithm, i.e. the function returns the value at position 
k=1+p(n-1)
, if this is an integer. Otherwise, R computes a weighted mean of the two neighb­oring integers
 
quanti­le(x, type=1)
General inverse function of the ECDF (smallest p-quan­tile). Largetst p-quantile can be obtained indirectly by slightly increasing p
 
summary(x)
Overview of important measures
 
cov(x,y)
Covariance
 
cor(x,y)
Correl­ation

Distri­butions in R

General usage
 
d<d­istr. ID>­(pa­rams)
density function
 
q<d­istr. ID>­(pa­rams)
quantile function. Always computes the smallest quantile
 
p<d­istr. ID>­(pa­rams)
cumulative distri­bution function
 
r<d­istr. ID>­(pa­rams)
random variate generation
Distri­butions
 
dbinom(x, size=p, prob=p)
Binomial
 
dchisq(x, df, ncp=0)
Chi-sq­uared
 
dexp(x, rate=1)
Expone­ntial
 
dgamma(x, shape=r, rate=l)
Gamma
 
dgeom(x, prob=p)
Geometric
 
dnbinom(x, size, prob)
Negative binomial
 
dnorm(x, mean=0, sd=1)
Normal
 
dpois(x, lambda)
Poisson
 
dt(x, df, ncp)
t-dist­rib­ution
 
dunif(x, min=0, max=1)
Uniform

Plotting

Basic plots
 
plot(data)
Plot quick overview.
 
plot(x, y, xlab="m­u", ylab="P­owe­r", type="l­", col="re­d", ylim=c­(0,1), lwd=1.5)
Plot data with custom style options
Lines and curves
 
abline­(a,­b,c­ol=­"­red­")
Add a red line with intercept 
a
and slope 
b
to the plot.
 
abline­(v=­a,c­ol=­"­red­")
add vertical line at 
x=a
 
abline­(h=­b,c­ol=­"­red­")
add horizontal line at 
y=b
 
lines(x, y, col="gr­een­", lwd=1.5)
Add a generic line
 
curve(­sin­,-p­i,p­i,a­dd=­TRUE)
Draw a curve of a function over the specified interval
Data visual­ization
 
plot.e­cdf­(data)
Plot ECDF.
 
barplot(x, main="T­itl­e", xlab="x label")
Annotated barplot of absolute freque­ncies
 
hist(data, prob=TRUE, breaks=30)
Histogram of relative freque­ncies (30 bins).
 
rug(data)
1D-plot
 
boxplo­t(d­ata1, data2, ... ,range­=1.5)
Plot boxplots of one or more data sequences in one window. 
range
determines the extend of the whiskers. Default 
range=1.5
, i.e. 1.5 x IQR
 
qqnorm(x)
QQ-Plot against standard normal distri­bution
 
qqPlot(x, dist="u­nif­"­,...)
QQ-Plot against any R-standard distri­bution. Additional arguments such as 
df
, 
ncp
can also be specified.
 
legend­(x,y, legend­=c(­"­n=1­0"),­col­=c(­"­red­"), lty=1, cex=0.8)
Add legend to plot as position 
(x,y)

Statis­tical hypothesis testing

One-Sample tests
 
t.test­(x,­mu=­mu0­,al­t="l­ess­", conf.l­eve­l=1­-alpha)
Performs one and two sample t-tests on vectors of data.
 
power.t.t­est(n = 100, delta=0.1, sd=2, sig.le­vel­=0.1, type="o­ne.s­am­ple­", alt="on­e.s­ide­d")
Compute the power of the one- or two- sample t test, or determine parameters to obtain a target power.
 
binom.t­es­t(s­um(­x),­n,p­0,a­lt=­"­gre­ate­r", conf.l­eve­l=1­-alpha)
Performs an exact test of a simple null hypothesis about the probab­ility of success in a Bernoulli experi­ment. It might happen that the decision based on the p-value differs from that of the confidence interval. Choose the decision based on the p-value in such cases.
Two-Sample tests
 
t.test­(sh­oes­$A,­sho­es$­B,p­air­ed=­FALSE, var.eq­ual­=TRUE)
Example for an unpaired sample t-test
 
var.te­st(­x,y­,co­nf.l­ev­el=­1-a­lpha)
Performs an F test to compare the variances of two samples from normal popula­tions.
GOF tests
 
shapir­o.t­est(x)
Performs the Shapir­o-Wilk test of normality.
 
chisq.t­es­t(t­abl­e(x), p=p_0)
Test for distri­bution with probab­ilities 
p_0
. If 
p
is not specified, R tests for a uniform distri­bution
 
chisq.t­es­t(t­abl­e(x), p=p_0, simula­te.p.v­alu­e=TRUE)
Do not use Chi2-appro­xim­ation to calculate the p-value
 
pwr.ch­isq.te­st(­w=ncp, df=s-1, sig.le­vel­=alpha, power=0.9)
Determine the number of samples needed to reach the desired power at the given signif­icance level
 
ks.test(x, "­pno­rm", 0, 1)
One-sample Kolmog­oro­v-S­mirnov test against hypoth­etical distri­bution
 
lillie.te­st(x)
Lilliefors (Kolmo­gor­ov-­Smi­rnov) test for the composite hypothesis of normality
Tests of indepe­ndence
 
chisq.t­est(M)
Chi2-test of indepe­ndence. M hast to be a matrix! (conti­ngency table)
 
fisher.te­st(M)
Exact test of Fisher. If the table entries are too large, use 
simula­te.p.v­alu­e=TRUE
 
runs.t­est(x)
Runs test of indepe­ndence. 
x
hast to be a factor (use 
as.fac­tor()
if necessary)

Runs Test of Randomness

rle(x)
Compute the lengths and values of runs of equal values in a vector .
 
rle(x)­$le­ngths
Vector containing the length of each run.
 
rle(x)­$values
Vector of the same length as lengths with the corres­ponding values.

Optimi­zation

nlm(E2­,0.5)
Carries out a minimi­zation of the function f using a Newton­-type algorithm. May not give all solutions. The function must be vectorized
 
E2vec=­Vec­tor­ize(E2, vector­ize.ar­gs=­c("n­"))
vectorize a function. 
vector­ize.args
: explicitly state arguments to be vector­ized.
Distri­bution Fit
 
fitdis­tr(x, "­Poi­sso­n")
Maximu­m-l­ike­lihood fitting of univariate distri­but­ions, allowing parameters to be held fixed if desired. (
librar­y(MASS)
)
Regression
 
reg=lm­(x~t)
Fit a linear function 
x=a+bt
 
summar­y(reg)
Obtain further inform­ation about regression result
Important fields:
- Residual standard error: sd of residuals (with normal­ization 
n-2
)
-t value: Test null hypothesis "­est­imate is 0" with assumption of a normally distri­buted random mechanism
-multiple R-squared: squared corr. coef. Null hypothesis r2=0 is tested with F-stat­istic
 
reg=lm(x ~ t+ I(t^2))
Fit a polynomial function. 
I()
inhibits R from interp­reting 
t^2
as a formula
 
form=x ~ a/(1+e­xp(­-b*­(t-c)))

reg=nl­s(form, data=U­SPop, start=­c(a­=40­0,b­=0.0­2,­c=2­000))
perform non-linear least-­squares regression
 
plot(t­,pr­edi­ct(­reg))
Plot regression result
Root finding
 
res = uniroo­t(func, c(0,10))
Searches interval for a root of the function 
func
. 
res$root
and 
res$f.root
give the location of the root and the value of the function

Help

?sqrt
Display docume­ntation of the command 
sqrt
`
 
?'%%'
use quotation marks for special characters

Miscel­laneous

Printing
 
print(­"­Tex­t")
Default print
 
sprint­f("F­orm­atted %s: %.3f", object, mean)
Formatted print
 
(x=3)
enclose an R command with brackets to directly print the result
 
edit(x)
Invoke text editor on R object
Libraries
 
librar­y(MASS)
Load package MASS
 
uniroot(f, interval)
find 1D root
Step functions
 
stepfu­nc(x,y)
Given the vectors (x1, ...,xn) and (y0,...,yn) (one value more!), returns an interp­olating ‘step’ function
 
knots(x)
returns jump positions of stepfu­nction