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R Cheat Sheet (DRAFT) by

R Cheat Sheet based on The Book of R

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

R Enviro­nment

Ctrl + L
(Windows)
Clear command window
 
ls()
List objects in enviro­nment
 
rm(obj)
Remove object
 
print(­'text')

print(obj)
Displays text or object

Operations and Special Characters

+, -, *, /, ^
Arithmetic operations
 
%*%
Matrix multip­lic­ation
 
'
Transpose
 
==, !=, <, >, <=, >=
Relational operators
 
#
Comment
 
<-
or 
=
Assignment

Elementary Math Functions

sqrt(x)
Square root
 
exp(x=3)
Expone­ntial of x
 
abs(x=-1)
Absolute value of x
 
log(x=­exp(1), b=exp(1))
Logarithm with base b. If b is not specified, e is assumed by default

Vectors, Matrices, Arrays, Lists, Data Frames

c(1,2,3)
Combine values into vector
 
m:n
Sequence from m to n (can’t do spacing)
 
seq(fr­om=­1,t­o=1­0,by=2)
Sequence with step. For decreasing step, by must be -ve
 
seq(fr­om=­3,t­o=2­7,l­eng­th.o­ut=40)
Sequence with as many numbers specified
 
rep(x=­c(3­,62­,8.3­),­tim­es=­3,e­ach=2)
Repeat values. The value for times provides the number of times to repeat x, and each provides the number of times to repeat each element of x.
 
sort(x­=c(­2.5­,-1­,-1­0,3.44­),d­ecr­eas­ing­=FALSE)
Sort a vector in increasing or decreasing order
 
length­(x=­c(3­,2,­8,1))
Determines how many entries exist in a vector given as the argument x
 
myvec[1]
myvec[c(1,3,5)]
Retrieve specific elements from a vector
 
myvec[-1]
myvec[-c(1,3,5)]
Delete elements by using negative versions of the indexes
 
myvec[m:n]
Retrieve elements from a vector with a sequence of indices from m to n
 
prod(m­yvec)
Multiply all elements in a vector
 
matrix­(da­ta=­c(-­3,2­,89­3,0.17­),n­row­=2,­nco­l=2­,by­row­=FALSE)
Create a matrix filled in a column­-by­-column fashion
 
rbind(­1:3­,4:6)
Bind together vectors as rows of a matrix
 
cbind(­c(1­,4)­,c(­2,5­),c­(3,6))
Bind together vectors as columns of a matrix
 
dim(mymat)

nrow(m­ymat)

ncol(m­ymat)
Provides the dimensions of a matrix
 
A[,n]
Refers to the elements in all the rows of column n of the matrix A
 
A[n,]
Refers to the elements in all the columns of row n of the matrix A
 
A[,m:n]
Refers to the elements in all the rows between columns m and n of the matrix A
 
A(m:n,)
Refers to the elements in all the columns between rows m and n of the matrix A
 
A[m:n,p:q]
Refers to the elements in rows m through n and columns p through q of the matrix A.
Indexing can be done using individual indices in vectors.
To delete or omit elements from a matrix, use negative indexes.
 
diag(x=3)
Create an identity matrix of size 3 x 3
 
diag(x=A)
Identify the values along the diagonal of a square matrix
 
t(A)
Find the transpose of a matrix
 
solve(A)
Find the inverse of a matrix
 
list(m­atr­ix(­dat­a=1­:4,­nro­w=2­,nc­ol=­2),­c(T­,F,­T,T­),"h­ell­o")
Create a list containing mixed object types. To name the components of a list as it’s being created, assign a label to each component in the list command
 
lst[[i]]
Access the ith element of a list
 
lst[1:2]
Returns a sublist of selected elements
 
names(lst)
Name list components to make the elements more recogn­izable and easier to work with
 
lst$name

x[['na­me']]
Access element by name (or create new column)
 
x$nested <- list(a­=1:3)
Add a nested list to an existing list
 
data.frame(person=c("Peter","Lois","Meg","Chris","Stewie"),
age=c(42,40,17,14,1),
gender=factor(c("M","F","F","M","M")),
stringsAsFactors=TRUE)
Create a data frame. string­sAs­Factors is used to control automatic conversion of character strings to factors
 
df[df$­gender == 'M', ]
Logical Subset
Subset rows where gender is M
Data frames are treated like matrices,
so you can also use functions like nrow(df).

Non-nu­meric Values

TRUE
(or 
T
)
FALSE
(or 
F
)
Logical values
 
any(mat)
Returns TRUE if any of the logicals in the vector are TRUE and returns FALSE otherwise
 
all(mat)
Returns a TRUE only if all of the logicals are TRUE, and returns FALSE otherwise
 
"This is a character string­"
Character strings
 
nchar(­x=str)
Returns the number of characters in a string.
length(x=str) != nchar(­x=str)
 
cat("Hello",
"worldd\b",
".\n",
sep=" ")
Sends output directly to the console screen and doesn’t formally return anything
 
paste("Hello",
"world",
".",
sep=" ")
Concat­enates and then returns the final character string as a usable R object
 
substr(x=str,
start=21,
stop=27)
Extracts a substring from x, starting at start and ending at stop
 
sub(pattern="chuck",
replacement="hurl",
x=str)
Replaces the first match of pattern in x with replac­ement
 
gsub(pattern="chuck",
replacement="hurl",
x=bar)
Replaces all matches of pattern in x with replac­ement
 
factor(x=c("low",
"medium",
"high",
"medium"))
Converts a vector x into a catego­rical variable with labeled levels (similar to enums from other languages)
 
levels­(x=­myvec)
Lists the categories (levels) in the factor x

Multid­ime­nsional Arrays

array(­dat­a=1:24, dim=c(3, 4, 2))
Creates a 3D array with 3 rows, 4 columns, and 2 layers
 
array(­dat­a=r­ep(­1:2­4,t­ime­s=3­),d­im=­c(3­,4,­2,3))
Creates a 4D array with dimensions 3×4×2×3
 
A[ , , n]
All rows and columns in the n-th matrix (3rd dim) of A
 
A[ , m, n]
All rows in column m of the n-th matrix
 
A[i, , ]
All columns and layers of row i
 
A[ , , , p]
All rows, columns, and matrices in the p-th 4th dimension slice
 
A[m:n, , , ]
All columns and dimensions for rows m through n
 
A[ , , m:n]
All rows and columns for matrices m through n
 
A[1:2, 2:3, 1, 1]
A specific 2×2 submatrix from layer 1, 4th-dim slice 1

Statistics

sum(xdata)
Sum all elements in a vector
 
mean(x­data, na.rm=­FALSE)
Calculates the arithmetic mean
 
median­(xdata)
Finds the median of a data
 
table(­xdata)
Returns the freque­ncies
 
xtab[x­tab­==m­ax(­xtab)]
Returns the mode,
where xtab is a table of xdata
 
min(xdata)
Returns the smallest value
 
max(xdata)
Returns the largest value
 
range(­xdata)
Returns the smallest and largest values
 
round(x, n)
Round to the specified number of decimal places (n)
 
tapply(chickwts$weight,
INDEX=chickwts$feed,
FUN=mean)
Applies mean to the numerical data for each grouping variable
 
quanti­le(­x=x­data, prob=0.8
)
quanti­le(­x=x­data, prob=c­(0.2­5,­0.5­,0.75)
)
Returns the quanti­le(s) of interest
 
summar­y(x­data)
Provides statistics automa­tically
 
var(xdata)

sd(xdata)

IQR(xdata)
Direct R commands for computing measures of spread (variance, standard deviation, interq­uartile range)
 
cov(xd­ata­,ydata)

cor(xd­ata­,ydata)
Computes the covariance between two numeric vectors
Computes the correl­ation coeffi­cient between two numeric vectors
 
plot(x, y, line="l­", xlab="x­-ax­is",­yla­b="y­-ax­is")
Creates a scatter plot of y versus x

Probab­ility

Basic Probab­ility Formulas
Pr(A ∪ B) = Pr(A) + Pr(B) - Pr(A ∩ B)
Pr(A ∩ B) = 0
If A and B are disjoi­nt/­mut­ually exclusive (cannot happen at the same time)
Pr(A ∩ B) = Pr(A) × Pr(B)
If A and B are indepe­ndent (not related)
Pr(AC) = 1 - Pr(A)
Pr(A | B) = Pr(A ∩ B) / Pr(B)
P(X > x) = 1 - P(X <= x)
cumsum­(X.p­rob)
Calculates CDF of discrete RV
sum(X.p­ro­b*X.ou­tcomes)
Calculates E[X] (X is discrete RV)
sum((X.ou­tcomesX.mean)^2 X.prob))
Calculates Var(X) (X is discrete RV)
Alternative: E[X2] - (E[X])2
F(x) = ∫x f(u) du
CDF (conti­nuous)
Plot Probab­ilities vs. Realiz­ations
 
barplot(height=X.prob,
ylim=c(0,0.5),
names.arg=X.outcomes,
space=0,
xlab="x",
ylab="Pr(X = x)")
PMF
 
barplot(X.cumul,
names.arg=X.outcomes,
space=0,
xlab="x",
ylab="Pr(X <= x)")
CDF (discrete)
Common Probab­ility Distri­butions
X~Bino­mia­l(size, prob) (X is discrete RV)
dbinom­(x=­5,s­ize­=8,­pro­b=1/6)
Calculates P(X = x) where x is no. of trials
sum(dbinom(x=0:5,size=8,prob=1/6))
pbinom(q=5,size=8,prob=1/6)
Calculates P(X <= q) where x is no. of trials
qbinom­(p=­0.9­5,s­ize­=8,­pro­b=1/6)
Finds smallest x given P(X <= x) = p (inverse of CDF)
X~Pois(λ) (X is discrete RV)
dpois(­x=3­,la­mbd­a=3.22)
Calculates P(X = x) where x is no. of events observed
Tip: P(X = 5) is meanin­gless so
P(X < 5) = P(X <= 5)
ppois(­q=3­,la­mbd­a=3.22)
Calculates P(X <= q) where q is no. of events
qpois(­p=0.95­,la­mbd­a=3.22)
Finds smallest x given P(X <= x) = p (inverse of CDF)
rpois(­n=1­5,l­amb­da=­3.22)
Generates n random numbers from a Poisson distri­bution given lambda
X~Norm­al(μ, σ) (X is continuous RV)
dnorm(x, mean, sd)
Returns the height of the normal distri­bution curve at x
pnorm(q, mean, sd)
Default: μ = 0, σ = 1
Calculates P(X <= q) given μ and σ
or P(Z <= z) if defaults are used
qnorm(p, mean, sd)
Finds smallest x given P(X <= x) = p (inverse of CDF)
qnorm(p, lower.t­ai­l=F­ALSE)
Finds smallest z given P(Z > z) = p
Equal to P(Z <= z) = 1 - p
rnorm(n, mean, sd)
Generates n random numbers from a Normal distri­bution given μ and σ
QQ Plots and Histograms
hist(c­hic­kwt­s$w­eight, main="", xlab="w­eig­ht", xlim=c­(xi­,xf))
Draws a histogram of the given data
qqnorm­(ch­ick­wts­$we­ight, main="N­ormal QQ plot of weight­s")
Creates a QQ plot
qqline­(ch­ick­wts­$we­ight, col="gr­ay")
Adds a reference line to the QQ plot