Usefull R Commands

Most useful R commands You should know right Now. Start learning one by one.

• help() #give help regarding a command, e.g. help(hist) 
• c() #concatenate objects, e.g. x = c(3,5,8,9) or y = c(”Jack”,”Queen”,”King”) 1:19 #create a sequence of integers from 1 to 19 
• (…) #give arguments to a function, e.g. sum(x), or help(hist) 
• […] #select elements from a vector or list, e.g. x[2] gives 5, x[c(2,4)] gives 5 9 for x as above matrix() #fill in (by row) the values from y in a matrix of 4 rows and 3 columns by giving #m = matrix(y,4,3,byrow=T) 
• dim() #gives the number of rows and the number of columns of a matrix, or a data frame head() #gives the first 6 rows of a large matrix, or data frame 
• tail() #gives the last 6 rows of a large matrix, or data frame 
• m[ ,3] #gives the 3rd column of the matrix m 
• m[2, ] #gives the 2nd row of the matrix m 
• = or <- #assign something to a variable, e.g. x = c(”a”,”b”,”b”,”e”) 
• == #ask whether two things are equal, e.g. x = c(3,5,6,3) and then x == 3 gives T F F T #Then y[x == 3] gives those entries of y where x equals 3, i.e. the 1st and 4th entry of y < #ask whether x is smaller than y, e.g. x < 6 in the example above gives True True False True > #ask whether x is larger than y 
• & #logical „and‟ 
• | #logical „or‟ 
• sum() #get the sum of the values in x by sum(x) 
• mean() #get the mean of the values in x by mean(x) 
• median() #get the median of the values in x by median(x) 
• sd() #get the standard deviation of the values in x  
• var() #get the variance of the values in x 
• IQR() #get the IQR of the values in x 
• summary() #get the summary statistics of a single variable, or of all variables in a data frame round() #round values in x to 3 decimal places by round(x,3) 
• sort() #sort the values in x by giving sort(x) 
• unique() #get the non-duplicate values from a list, e.g. x = c(3,5,7,2,3,5,9,3) and then unique(x) #gives 3 5 7 2 9 
• length(x) #gives the length of the vector x, which is 8 
• hist() #create a histogram of the values in x by hist(x) 
• stem() #create a stem and leaf plot of the values in x by stem(x) 
• boxplot() #create a boxplot of the values in x by boxplot(x) 
• plot() #scatterplot of x vs. y by plot(x,y); for more parameters see help(plot.default) cor() #gives the linear correlation coefficient 
• lm() #fit a least squares regression of y (response) on x (predictor) by fit = lm(y~x) names() #get or set the names of elements in a R object. E.g. names(fit) will give the names of the R  #object named “fit”, or 
• #get or set the names of variables in a data frame.  
• fit$coef #gives the least squares coefficients from the fit above, i.e. intercept and slope fit$fitted #gives the fitted values for the regression fitted above 
• fit$residuals #gives the residuals for the regression fitted above 
• lines() #add a (regression) line to a plot by lines(x,fit$fitted) 
• abline() #add a straight line to a scatterplot 
• points() #add additional points (different plotting character) to a plot by points(x,y2,pch=5) scan() #read data for one variable from a text file, e.g. y = scan(”ping.dat”) #Don‟t forget to change to the appropriate directory first  
• read.table() #read spreadsheet data (i.e. more than one variable) from a text file table() #frequency counts of entries, ideally the entries are factors(although   #it works with integers or even reals) 
• write() #write the values of a variable y in a file data.txt by write(y,file=”data.txt”) log() #natural logarithm (i.e. base e) 
• log10() #logarithm to base 10 
• seq() #create a sequence of integers from 2 to 11 by increment 3 with seq(2,11,by=3) rep() #repeat n times the value x, e.g. rep(2,5) gives 2 2 2 2 2 
• getwd() #get the current working directory. 
• setwd() #change the directory to. E.g. setwd("c:/RESEARCH/GENE.project/Chunks/") dir() #list files in the current working directory 
• search() #searching through reachable datasets and packages 
• library() #link to a downloaded R package to the current R session. E.g. library(Biostrings) link to the #R package #called “Biostrings” which you had downloaded earlier onto your laptop 
• Input and Display  
• load("c:/RData/pennstate1.RData") #load a R data frame  
• read.csv(filename="c:/stat251/ui.csv",header=T) #read .csv file with labels in first row 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 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  #logical 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  
• Moving Around 
• ls() #list the R objects in the current 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) 
• Data Manipulation 
• x.df=data.frame(x1,x2,x3 ...) #combine different kinds of data into a data frame 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  
• Statistical Tests 
• binom.test() 
• prop.test() #perform test with proportion(s) 
• t.test() #perform t test 
• chisq.test() #perform Chi-square test 
• pairwise.t.test()  
• power.anova.test() 
• power.t.test() 
• aov() 
• anova() 
• TukeyHSD() 
• kruskal.test() 
• Distributions  
• sample(x, size, replace = FALSE, prob = NULL) # take a simple random sample of size n from the  # population x with or without replacement 
• rbinom(n,size,p) 
• pbinom() 
• qbinom() 
• dbinom() 
• rnorm(n,mean,sd) #randomly generate n numbers from a Normal distribution with the specific mean and sd pnorm() #find probability (area under curve) of a Normal(10,3^2) distribution to the left  #of 8,i.e. P(X <= 8), by pnorm(8,mean=10,sd=3) 
• qnorm() #find quantity or value x such that area under Normal(10,3^2) curve and to the left  #of x equals 0.25 by qnorm(0.25,mean=10,sd=3) 
• rt() 
• pt() 
• qt() 
• runif(n,lower,upper) 
• punif() 
• qunif()

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