Rcommands-for those who interested in R.
0 likes143 views
This document provides a concise list of essential R commands, including basic commands for reading files, creating data vectors, manipulating data frames, and running statistical analyses. It emphasizes using the help function for detailed information and includes various helpful tips for data visualization. Additionally, it references a psych package with 350 functions developed for specific statistical operations.
![A short list of the most useful R
commands
A 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.
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 <- c(1,2,4,8,16 ) #create a data vector with specified elements
y <- c(1:10) #create 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 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](https://image.slidesharecdn.com/rcommands-160922160852/85/Rcommands-for-those-who-interested-in-R-1-320.jpg)
![new <- old[,-n] #drop the nth column
new <- old[-n,] #drop the nth row
new <- old[,-c(i,j)] #drop the ith and jth column
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
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 <- 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](https://image.slidesharecdn.com/rcommands-160922160852/85/Rcommands-for-those-who-interested-in-R-2-320.jpg)
![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](https://image.slidesharecdn.com/rcommands-160922160852/85/Rcommands-for-those-who-interested-in-R-5-320.jpg)
Rcommands-for those who interested in R.
- 1. A short list of the most useful R commands A 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. 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 <- c(1,2,4,8,16 ) #create a data vector with specified elements y <- c(1:10) #create 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 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
- 2. new <- old[,-n] #drop the nth column new <- old[-n,] #drop the nth row new <- old[,-c(i,j)] #drop the ith and jth column 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 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 <- 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
- 3. 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)
- 4. 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, 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
- 5. 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
- 6. 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(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 And more... The psych package includes about 350 additional functions that I have created in the last 9 years. These were created because my students and I needed some specific operation. Some functions were added following requests from other users. Follow the instructions for installing the psych package. 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
- 7. #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 Reference: http://www.personality-project.org/r/r.commands.html prepared by VOLKAN OBAN