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http://personality-project.org/r/r.commands.html
A short list of the most useful R commandsA summary of the most important commands with minimal examples. See the relevant part of the guide for better examples. For all of these commands, using the help(function) or ? function is the most useful source of information. Unfortunately, knowing what to ask for help about is the hardest problem. See the R-reference card by Tom Short for a much more complete list.
Input and display
read.table(filename,header=TRUE) #read a tab or space delimited file
read.table(filename,header=TRUE,sep=',') #read csv files
x=c(1,2,4,8,16 ) #create a data vector with specified elements
y=c(1:10) #creat a data vector with elements 1-10
n=10
x1=c(rnorm(n)) #create a n item vector of random normal deviates
y1=c(runif(n))+n #create another n item vector that has n added to each random uniform distribution
z=rbinom(n,size,prob) #create n samples of size "size" with probability prob from the binomial
vect=c(x,y) #combine them into one vector of length 2n
mat=cbind(x,y) #combine them into a n x 2 matrix
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 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
new=old[,-n] #drop the nth column
new=old[n,] #drop the nth row
new=subset(old,logical) #select those cases that meet the logical condition
complete = subset(data.df,complete.cases(data.df)) #find those cases with no missing values
new=old[n1:n2,n3:n4] #select the n1 through n2 rows of variables n3 through n4)
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,x==-9,NA)
#similar to the operation x[x==-9] <- NA
cut(x, breaks, labels = NULL,
include.lowest = FALSE, right = TRUE, dig.lab = 3, ...)
x.df=data.frame(x1,x2,x3 ...) #combine different kinds of data into a data frame
as.data.frame()
is.data.frame()
x=as.matrix()
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()
min()
mean()
median()
sum()
var() #produces the variance covariance matrix
sd() #standard deviation
mad() #(median absolute deviation)
fivenum() #Tukey fivenumbers min, lowerhinge, median, upper hinge, max
table() #frequency counts of entries, ideally the entries are factors(although it works with integers or even reals)
scale(data,scale=T) #centers around the mean and scales by the sd)
cumsum(x) #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)
More statistics: Regression and Linear model
lm(Y~X) #Y and X can be matrices
lm(Y~X1+X2)
lm(Y~X|W)
solve(A,B) #inverse of A * B - used for linear regression
solve(A) #inverse of A
factanal()
princomp()
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(big5r,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 uses a function found in useful.r
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
runif(n,lower,upper)
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(as.character(date), "%m/%d/%y") #change the date field to a internal form for time
#see ?formats and ?POSIXlt
as.Date
month= months(date) #see also weekdays, Julian
Additional functions that I have created because I needed some specific operation may be included in the workspace by issuing the source command:
source(http://personality-project.org/r/useful.r)
These functions include:
#alpha.scale #find coefficient alpha for a scale and a dataframe of items
#describe give means, sd, skew, n, and se
#summ.stats #basic summary statistics by a grouping variable
#error.crosses (error bars in two space)
#skew find skew
#panel.cor taken from the examples for pairs
#pairs.panels adapted from panel.cor -- gives a splom, histogram, and correlation matrix
#multi.hist #plot multiple histograms
#correct.cor #given a correlation matrix and a vector of reliabilities, correct for reliability
#fisherz #convert pearson r to fisher z
#paired.r #test for difference of dependent correlations
#count.pairwise #count the number of good cases when doing pairwise analysis
#eigen.loadings #convert eigen vector vectors to factor loadings by unnormalizing them
#principal #yet another way to do a principal components analysis -- brute force eignvalue decomp
#factor.congruence #find the factor congruence coeffiecints
#factor.model #given a factor model, find the correlation matrix
#factor.residuals #how well does it fit?
#factor.rotate # rotate two columns of a factor matrix by theta (in degrees)
#phi2poly #convert a matrix of phi coefficients to polychoric correlations
part of a short guide to R
Version of February 20, 2005
William Revelle
Department of Psychology
Northwestern University
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发表于 2013-4-8 18:52:51
感谢分享！