# is for comments
Input and display
#read files with labels in first row
read.table(filename,header=TRUE) #read a tab or space delimited file
read.table(filename,header=TRUE,sep=',') #read csv files
x <- a="" c="" create="" data="" elements="" nbsp="" p="" specified="" vector="" with="">y <- 1="" a="" c="" create="" data="" elements="" nbsp="" p="" to10="" vector="" with="">n <- 10="" p="">x1 <- a="" c="" create="" deviates="" item="" n="" nbsp="" normal="" of="" p="" random="" rnorm="" vector="">y1 <- a="" added="" c="" create="" distribution="" each="" has="" item="" n="" nbsp="" p="" random="" runif="" that="" to="" uniform="" vector="">z <- binomial="" create="" from="" n="" nbsp="" of="" p="" prob="" probability="" rbinom="" samples="" size="" the="" with="">vect <- and="" c="" combine="" into="" length="" nbsp="" of="" one="" p="" vector="" vectors="" x="" y="">mat <- 2="" a="" and="" cbind="" combine="" into="" matrix="" n="" nbsp="" p="" x="" y="">mat[4,2] #display the 4th row and the 2nd column
mat[3,] #display the 3rd row
mat[,2] #display the 2nd column
subset(dataset,logical) #those objects meeting a logical criterion
subset(data.df,select=variables,logical) #get those objects from a data frame that meet a criterion
data.df[data.df=logical] #yet another way to get a subset
x[order(x$B),] #sort a dataframe by the order of the elements in B
x[rev(order(x$B)),] #sort the dataframe in reverse order
browse.workspace #a Mac menu command that creates a window with information about all variables in the workspace
Moving around
ls() #list the variables in the workspace
rm(x) #remove x from the workspace
rm(list=ls()) #remove all the variables from the workspace
attach(mat) #make the names of the variables in the matrix or data frame available in the workspace
detach(mat) #releases the names (remember to do this each time you attach something)
with(mat, .... ) #a preferred alternative to attach ... detach
new <- column="" drop="" n="" nbsp="" nth="" old="" p="" the="">new <- drop="" n="" nbsp="" nth="" old="" p="" row="" the="">new <- and="" c="" column="" drop="" i="" ith="" j="" jth="" nbsp="" old="" p="" the="">new <- cases="" condition="" logical="" meet="" nbsp="" old="" p="" select="" subset="" that="" the="" those="">complete <- cases="" complete.cases="" data.df="" find="" missing="" nbsp="" no="" p="" subset="" those="" values="" with="">new <- n1:n2="" n1="" n2="" n3:n4="" n3="" n4="" nbsp="" of="" old="" p="" rows="" select="" the="" through="" variables="">
Distributions
beta(a, b)
gamma(x)
choose(n, k)
factorial(x)
dnorm(x, mean=0, sd=1, log = FALSE) #normal distribution
pnorm(q, mean=0, sd=1, lower.tail = TRUE, log.p = FALSE)
qnorm(p, mean=0, sd=1, lower.tail = TRUE, log.p = FALSE)
rnorm(n, mean=0, sd=1)
dunif(x, min=0, max=1, log = FALSE) #uniform distribution
punif(q, min=0, max=1, lower.tail = TRUE, log.p = FALSE)
qunif(p, min=0, max=1, lower.tail = TRUE, log.p = FALSE)
runif(n, min=0, max=1)
Data manipulation
replace(x, list, values) #remember to assign this to some object i.e., x <- replace="" x="=-9,NA) </p"> #similar to the operation x[x==-9] <- na="" p="">scrub(x, where, min, max, isvalue,newvalue) #a convenient way to change particular values (in psych package)
cut(x, breaks, labels = NULL,
include.lowest = FALSE, right = TRUE, dig.lab = 3, ...)
x.df <- ...="" a="" combine="" data.frame="" data="" different="" frame="" into="" kinds="" nbsp="" of="" p="" x1="" x2="" x3=""> as.data.frame()
is.data.frame()
x <- as.matrix="" p="">scale() #converts a data frame to standardized scores
round(x,n) #rounds the values of x to n decimal places
ceiling(x) #vector x of smallest integers > x
floor(x) #vector x of largest interger < x
as.integer(x) #truncates real x to integers (compare to round(x,0)
as.integer(x < cutpoint) #vector x of 0 if less than cutpoint, 1 if greater than cutpoint)
factor(ifelse(a < cutpoint, "Neg", "Pos")) #is another way to dichotomize and to make a factor for analysis
transform(data.df,variable names = some operation) #can be part of a set up for a data set
x%in%y #tests each element of x for membership in y
y%in%x #tests each element of y for membership in x
all(x%in%y) #true if x is a proper subset of y
all(x) # for a vector of logical values, are they all true?
any(x) #for a vector of logical values, is at least one true?
Statistics and transformations
max(x, na.rm=TRUE) #Find the maximum value in the vector x, exclude missing values
min(x, na.rm=TRUE)
mean(x, na.rm=TRUE)
median(x, na.rm=TRUE)
sum(x, na.rm=TRUE)
var(x, na.rm=TRUE) #produces the variance covariance matrix
sd(x, na.rm=TRUE) #standard deviation
mad(x, na.rm=TRUE) #(median absolute deviation)
fivenum(x, na.rm=TRUE) #Tukey fivenumbers min, lowerhinge, median, upper hinge, max
table(x) #frequency counts of entries, ideally the entries are factors(although it works with integers or even reals)
scale(data,scale=FALSE) #centers around the mean but does not scale by the sd)
cumsum(x,na=rm=TRUE) #cumulative sum, etc.
cumprod(x)
cummax(x)
cummin(x)
rev(x) #reverse the order of values in x
cor(x,y,use="pair") #correlation matrix for pairwise complete data, use="complete" for complete cases
aov(x~y,data=datafile) #where x and y can be matrices
aov.ex1 = aov(DV~IV,data=data.ex1) #do the analysis of variance or
aov.ex2 = aov(DV~IV1*IV21,data=data.ex2) #do a two way analysis of variance
summary(aov.ex1) #show the summary table
print(model.tables(aov.ex1,"means"),digits=3) #report the means and the number of subjects/cell
boxplot(DV~IV,data=data.ex1) #graphical summary appears in graphics window
lm(x~y,data=dataset) #basic linear model where x and y can be matrices (see plot.lm for plotting options)
t.test(x,g)
pairwise.t.test(x,g)
power.anova.test(groups = NULL, n = NULL, between.var = NULL,
within.var = NULL, sig.level = 0.05, power = NULL)
power.t.test(n = NULL, delta = NULL, sd = 1, sig.level = 0.05,
power = NULL, type = c("two.sample", "one.sample", "paired"),
alternative = c("two.sided", "one.sided"),strict = FALSE)
Regression, the linear model, factor analysis and principal components analysis (PCA)
matrices
t(X) #transpose of X
X %*% Y #matrix multiply X by Y
solve(A) #inverse of A
solve(A,B) #inverse of A * B (may be used for linear regression)
data frames are needed for regression
lm(Y~X1+X2)
lm(Y~X|W)
factanal() (see also fa in the psych package)
princomp() (see principal in the psych package)
Useful additional commands
colSums (x, na.rm = FALSE, dims = 1)
rowSums (x, na.rm = FALSE, dims = 1)
colMeans(x, na.rm = FALSE, dims = 1)
rowMeans(x, na.rm = FALSE, dims = 1)
rowsum(x, group, reorder = TRUE, ...) #finds row sums for each level of a grouping variable
apply(X, MARGIN, FUN, ...) #applies the function (FUN) to either rows (1) or columns (2) on object X
apply(x,1,min) #finds the minimum for each row
apply(x,2,max) #finds the maximum for each column
col.max(x) #another way to find which column has the maximum value for each row
which.min(x)
which.max(x)
z=apply(x,1,which.min) #tells the row with the minimum value for every column
Graphics
par(mfrow=c(nrow,mcol)) #number of rows and columns to graph
par(ask=TRUE) #ask for user input before drawing a new graph
par(omi=c(0,0,1,0) ) #set the size of the outer margins
mtext("some global title",3,outer=TRUE,line=1,cex=1.5) #note that we seem to need to add the global title last
#cex = character expansion factor
boxplot(x,main="title") #boxplot (box and whiskers)
title( "some title") #add a title to the first graph
hist() #histogram
plot()
plot(x,y,xlim=range(-1,1),ylim=range(-1,1),main=title)
par(mfrow=c(1,1)) #change the graph window back to one figure
symb=c(19,25,3,23)
colors=c("black","red","green","blue")
charact=c("S","T","N","H")
plot(PA,NAF,pch=symb[group],col=colors[group],bg=colors[condit],cex=1.5,main="Postive vs. Negative Affect by Film condition")
points(mPA,mNA,pch=symb[condit],cex=4.5,col=colors[condit],bg=colors[condit])
curve()
abline(a,b)
abline(a, b, untf = FALSE, ...)
abline(h=, untf = FALSE, ...)
abline(v=, untf = FALSE, ...)
abline(coef=, untf = FALSE, ...)
abline(reg=, untf = FALSE, ...)
identify()
plot(eatar,eanta,xlim=range(-1,1),ylim=range(-1,1),main=title)
identify(eatar,eanta,labels=labels(energysR[,1]) ) #dynamically puts names on the plots
locate()
legend()
pairs() #SPLOM (scatter plot Matrix)
pairs.panels () #SPLOM on lower off diagonal, histograms on diagonal, correlations on diagonal
#not standard R, but in the psych package
matplot ()
biplot ())
plot(table(x)) #plot the frequencies of levels in x
x= recordPlot() #save the current plot device output in the object x
replayPlot(x) #replot object x
dev.control #various control functions for printing/saving graphic files
pdf(height=6, width=6) #create a pdf file for output
dev.of() #close the pdf file created with pdf
layout(mat) #specify where multiple graphs go on the page
#experiment with the magic code from Paul Murrell to do fancy graphic location
layout(rbind(c(1, 1, 2, 2, 3, 3),
c(0, 4, 4, 5, 5, 0)))
for (i in 1:5) {
plot(i, type="n")
text(1, i, paste("Plot", i), cex=4)
}
Distributions
To generate random samples from a variety of distributions
rnorm(n,mean,sd)
rbinom(n,size,p)
sample(x, size, replace = FALSE, prob = NULL) #samples with or without replacement
Working with Dates
date <-strptime a="" as.character="" change="" d="" date="" field="" for="" form="" internal="" m="" nbsp="" p="" the="" time="" to="" y=""> #see ?formats and ?POSIXlt
as.Date
month= months(date) #see also weekdays, Julian
Input and display
#read files with labels in first row
read.table(filename,header=TRUE) #read a tab or space delimited file
read.table(filename,header=TRUE,sep=',') #read csv files
x <- a="" c="" create="" data="" elements="" nbsp="" p="" specified="" vector="" with="">y <- 1="" a="" c="" create="" data="" elements="" nbsp="" p="" to10="" vector="" with="">n <- 10="" p="">x1 <- a="" c="" create="" deviates="" item="" n="" nbsp="" normal="" of="" p="" random="" rnorm="" vector="">y1 <- a="" added="" c="" create="" distribution="" each="" has="" item="" n="" nbsp="" p="" random="" runif="" that="" to="" uniform="" vector="">z <- binomial="" create="" from="" n="" nbsp="" of="" p="" prob="" probability="" rbinom="" samples="" size="" the="" with="">vect <- and="" c="" combine="" into="" length="" nbsp="" of="" one="" p="" vector="" vectors="" x="" y="">mat <- 2="" a="" and="" cbind="" combine="" into="" matrix="" n="" nbsp="" p="" x="" y="">mat[4,2] #display the 4th row and the 2nd column
mat[3,] #display the 3rd row
mat[,2] #display the 2nd column
subset(dataset,logical) #those objects meeting a logical criterion
subset(data.df,select=variables,logical) #get those objects from a data frame that meet a criterion
data.df[data.df=logical] #yet another way to get a subset
x[order(x$B),] #sort a dataframe by the order of the elements in B
x[rev(order(x$B)),] #sort the dataframe in reverse order
browse.workspace #a Mac menu command that creates a window with information about all variables in the workspace
Moving around
ls() #list the variables in the workspace
rm(x) #remove x from the workspace
rm(list=ls()) #remove all the variables from the workspace
attach(mat) #make the names of the variables in the matrix or data frame available in the workspace
detach(mat) #releases the names (remember to do this each time you attach something)
with(mat, .... ) #a preferred alternative to attach ... detach
new <- column="" drop="" n="" nbsp="" nth="" old="" p="" the="">new <- drop="" n="" nbsp="" nth="" old="" p="" row="" the="">new <- and="" c="" column="" drop="" i="" ith="" j="" jth="" nbsp="" old="" p="" the="">new <- cases="" condition="" logical="" meet="" nbsp="" old="" p="" select="" subset="" that="" the="" those="">complete <- cases="" complete.cases="" data.df="" find="" missing="" nbsp="" no="" p="" subset="" those="" values="" with="">new <- n1:n2="" n1="" n2="" n3:n4="" n3="" n4="" nbsp="" of="" old="" p="" rows="" select="" the="" through="" variables="">
Distributions
beta(a, b)
gamma(x)
choose(n, k)
factorial(x)
dnorm(x, mean=0, sd=1, log = FALSE) #normal distribution
pnorm(q, mean=0, sd=1, lower.tail = TRUE, log.p = FALSE)
qnorm(p, mean=0, sd=1, lower.tail = TRUE, log.p = FALSE)
rnorm(n, mean=0, sd=1)
dunif(x, min=0, max=1, log = FALSE) #uniform distribution
punif(q, min=0, max=1, lower.tail = TRUE, log.p = FALSE)
qunif(p, min=0, max=1, lower.tail = TRUE, log.p = FALSE)
runif(n, min=0, max=1)
Data manipulation
replace(x, list, values) #remember to assign this to some object i.e., x <- replace="" x="=-9,NA) </p"> #similar to the operation x[x==-9] <- na="" p="">scrub(x, where, min, max, isvalue,newvalue) #a convenient way to change particular values (in psych package)
cut(x, breaks, labels = NULL,
include.lowest = FALSE, right = TRUE, dig.lab = 3, ...)
x.df <- ...="" a="" combine="" data.frame="" data="" different="" frame="" into="" kinds="" nbsp="" of="" p="" x1="" x2="" x3=""> as.data.frame()
is.data.frame()
x <- as.matrix="" p="">scale() #converts a data frame to standardized scores
round(x,n) #rounds the values of x to n decimal places
ceiling(x) #vector x of smallest integers > x
floor(x) #vector x of largest interger < x
as.integer(x) #truncates real x to integers (compare to round(x,0)
as.integer(x < cutpoint) #vector x of 0 if less than cutpoint, 1 if greater than cutpoint)
factor(ifelse(a < cutpoint, "Neg", "Pos")) #is another way to dichotomize and to make a factor for analysis
transform(data.df,variable names = some operation) #can be part of a set up for a data set
x%in%y #tests each element of x for membership in y
y%in%x #tests each element of y for membership in x
all(x%in%y) #true if x is a proper subset of y
all(x) # for a vector of logical values, are they all true?
any(x) #for a vector of logical values, is at least one true?
Statistics and transformations
max(x, na.rm=TRUE) #Find the maximum value in the vector x, exclude missing values
min(x, na.rm=TRUE)
mean(x, na.rm=TRUE)
median(x, na.rm=TRUE)
sum(x, na.rm=TRUE)
var(x, na.rm=TRUE) #produces the variance covariance matrix
sd(x, na.rm=TRUE) #standard deviation
mad(x, na.rm=TRUE) #(median absolute deviation)
fivenum(x, na.rm=TRUE) #Tukey fivenumbers min, lowerhinge, median, upper hinge, max
table(x) #frequency counts of entries, ideally the entries are factors(although it works with integers or even reals)
scale(data,scale=FALSE) #centers around the mean but does not scale by the sd)
cumsum(x,na=rm=TRUE) #cumulative sum, etc.
cumprod(x)
cummax(x)
cummin(x)
rev(x) #reverse the order of values in x
cor(x,y,use="pair") #correlation matrix for pairwise complete data, use="complete" for complete cases
aov(x~y,data=datafile) #where x and y can be matrices
aov.ex1 = aov(DV~IV,data=data.ex1) #do the analysis of variance or
aov.ex2 = aov(DV~IV1*IV21,data=data.ex2) #do a two way analysis of variance
summary(aov.ex1) #show the summary table
print(model.tables(aov.ex1,"means"),digits=3) #report the means and the number of subjects/cell
boxplot(DV~IV,data=data.ex1) #graphical summary appears in graphics window
lm(x~y,data=dataset) #basic linear model where x and y can be matrices (see plot.lm for plotting options)
t.test(x,g)
pairwise.t.test(x,g)
power.anova.test(groups = NULL, n = NULL, between.var = NULL,
within.var = NULL, sig.level = 0.05, power = NULL)
power.t.test(n = NULL, delta = NULL, sd = 1, sig.level = 0.05,
power = NULL, type = c("two.sample", "one.sample", "paired"),
alternative = c("two.sided", "one.sided"),strict = FALSE)
Regression, the linear model, factor analysis and principal components analysis (PCA)
matrices
t(X) #transpose of X
X %*% Y #matrix multiply X by Y
solve(A) #inverse of A
solve(A,B) #inverse of A * B (may be used for linear regression)
data frames are needed for regression
lm(Y~X1+X2)
lm(Y~X|W)
factanal() (see also fa in the psych package)
princomp() (see principal in the psych package)
Useful additional commands
colSums (x, na.rm = FALSE, dims = 1)
rowSums (x, na.rm = FALSE, dims = 1)
colMeans(x, na.rm = FALSE, dims = 1)
rowMeans(x, na.rm = FALSE, dims = 1)
rowsum(x, group, reorder = TRUE, ...) #finds row sums for each level of a grouping variable
apply(X, MARGIN, FUN, ...) #applies the function (FUN) to either rows (1) or columns (2) on object X
apply(x,1,min) #finds the minimum for each row
apply(x,2,max) #finds the maximum for each column
col.max(x) #another way to find which column has the maximum value for each row
which.min(x)
which.max(x)
z=apply(x,1,which.min) #tells the row with the minimum value for every column
Graphics
par(mfrow=c(nrow,mcol)) #number of rows and columns to graph
par(ask=TRUE) #ask for user input before drawing a new graph
par(omi=c(0,0,1,0) ) #set the size of the outer margins
mtext("some global title",3,outer=TRUE,line=1,cex=1.5) #note that we seem to need to add the global title last
#cex = character expansion factor
boxplot(x,main="title") #boxplot (box and whiskers)
title( "some title") #add a title to the first graph
hist() #histogram
plot()
plot(x,y,xlim=range(-1,1),ylim=range(-1,1),main=title)
par(mfrow=c(1,1)) #change the graph window back to one figure
symb=c(19,25,3,23)
colors=c("black","red","green","blue")
charact=c("S","T","N","H")
plot(PA,NAF,pch=symb[group],col=colors[group],bg=colors[condit],cex=1.5,main="Postive vs. Negative Affect by Film condition")
points(mPA,mNA,pch=symb[condit],cex=4.5,col=colors[condit],bg=colors[condit])
curve()
abline(a,b)
abline(a, b, untf = FALSE, ...)
abline(h=, untf = FALSE, ...)
abline(v=, untf = FALSE, ...)
abline(coef=, untf = FALSE, ...)
abline(reg=, untf = FALSE, ...)
identify()
plot(eatar,eanta,xlim=range(-1,1),ylim=range(-1,1),main=title)
identify(eatar,eanta,labels=labels(energysR[,1]) ) #dynamically puts names on the plots
locate()
legend()
pairs() #SPLOM (scatter plot Matrix)
pairs.panels () #SPLOM on lower off diagonal, histograms on diagonal, correlations on diagonal
#not standard R, but in the psych package
matplot ()
biplot ())
plot(table(x)) #plot the frequencies of levels in x
x= recordPlot() #save the current plot device output in the object x
replayPlot(x) #replot object x
dev.control #various control functions for printing/saving graphic files
pdf(height=6, width=6) #create a pdf file for output
dev.of() #close the pdf file created with pdf
layout(mat) #specify where multiple graphs go on the page
#experiment with the magic code from Paul Murrell to do fancy graphic location
layout(rbind(c(1, 1, 2, 2, 3, 3),
c(0, 4, 4, 5, 5, 0)))
for (i in 1:5) {
plot(i, type="n")
text(1, i, paste("Plot", i), cex=4)
}
Distributions
To generate random samples from a variety of distributions
rnorm(n,mean,sd)
rbinom(n,size,p)
sample(x, size, replace = FALSE, prob = NULL) #samples with or without replacement
Working with Dates
date <-strptime a="" as.character="" change="" d="" date="" field="" for="" form="" internal="" m="" nbsp="" p="" the="" time="" to="" y=""> #see ?formats and ?POSIXlt
as.Date
month= months(date) #see also weekdays, Julian
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