) #applies the function (FUN) to either rows (1) or columns (2) on object XДЄpply(x,1,min) #finds the minimum for each rowДЄpply(x,2,max) #finds the maximum for each columnД¬ol.max(x) #another way to find which column has the maximum value for each row ) #finds row sums for each level of a grouping variableДЄpply(X, MARGIN, FUN. Princomp() (see principal in the psych package) Solve(A,B) #inverse of A * B (may be used for linear regression)ДЇactanal() (see also fa in the psych package) More statistics: Regression, the linear model, factor analysis and principal components analysis (PCA) Power = NULL, type = c('two.sample', 'one.sample', 'paired'),ДЄlternative = c('two.sided', 'one.sided'),strict = FALSE) Power.t.test(n = NULL, delta = NULL, sd = 1, sig.level = 0.05, Within.var = NULL, sig.level = 0.05, power = NULL) (groups = NULL, n = NULL, between.var = NULL,

Lm(x~y,data=dataset) #basic linear model where x and y can be matrices (see plot.lm for plotting options) Print(model.tables(aov.ex1,'means'),digits=3) #report the means and the number of subjects/cellД«oxplot(DV~IV,data=data.ex1) #graphical summary appears in graphics window Scale(data,scale=FALSE) #centers around the mean but does not scale by the sd)Д¬umsum(x,na=rm=TRUE) #cumulative sum, etc.Д¬or(x,y,use='pair') #correlation matrix for pairwise complete data, use='complete' for complete casesДЄov(x~y,data=datafile) #where x and y can be matricesДЄov.ex1 = aov(DV~IV,data=data.ex1) #do the analysis of variance orДЄov.ex2 = aov(DV~IV1*IV21,data=data.ex2) #do a two way analysis of variance Table(x) #frequency counts of entries, ideally the entries are factors(although it works with integers or even reals) Mad(x, na.rm=TRUE) #(median absolute deviation)ДЇivenum(x, na.rm=TRUE) #Tukey fivenumbers min, lowerhinge, median, upper hinge, max Var(x, na.rm=TRUE) #produces the variance covariance matrix Max(x, na.rm=TRUE) #Find the maximum value in the vector x, exclude missing values Y%in%x #tests each element of y for membership in xДЄll(x%in%y) #true if x is a proper subset of yДЄll(x) # for a vector of logical values, are they all true?ДЄny(x) #for a vector of logical values, is at least one true? X%in%y #tests each element of x for membership in y Transform(data.df,variable names = some operation) #can be part of a set up for a data set Read.table(filename,header=TRUE,sep=',') #read csv filesДЇloor(x) #vector x of largest interger < xДЄs.integer(x) #truncates real x to integers (compare to round(x,0)ДЄs.integer(x < cutpoint) #vector x of 0 if less than cutpoint, 1 if greater than cutpoint)ДЇactor(ifelse(a < cutpoint, 'Neg', 'Pos')) #is another way to dichotomize and to make a factor for analysis Read.table(filename,header=TRUE) #read a tab or space delimited file See the R-reference card by Tom Short for a much more complete list. Unfortunately, knowing what to ask for help about is the hardest problem. For all of these commands, using the help(function) or ? function is the most useful source of information. See the relevant part of the guide for better examples. This helps if you are trying to combine a different data type into your list object, or create a new list with individual elements of a different type in R.ДЇor more information about handy functions for manipulating data, check out our functions reference.A short list of the most useful R commandsДЄ summary of the most important commands with minimal examples.

Note that weвЂ™ve abstracted the new value into a variable you can easily create a loop to iterate through another data structure or process (parse a document, query an API, scrape the web) to generate more sophisticated forms of data and pass results to newelem for insertion in the existing list.

YouвЂ™re working with part of the core R library вЂ“ this append function runs extremely quickly, appending multiple elements to the given list in a sequence very quickly, as apposed to a generic apply function or regular expression.

Specific advantages of this approach include Why not just use the c() function to append values to a list? Syntax would be as follows: # R using c function to append values to list This approach has the disadvantage of being too simple (hah hah). Use of the c() function to append to lists in R This approach makes for more succinct code. # r add elements to list using list or vector as sourceДЄppend (first_vector, c(value1, value2, value3), after=5)

Make a list in r