A Quick Introduction to R

Transcription

1 A Quick Introduction to R Guy Lebanon January 18, Overview R is a relatively new programming language aimed at computational data analysis, statistical modeling, and data visualization. It is similar to Matlab in the following ways In its default setting it is run inside an interactive shell. In many cases, the shell is a complete graphical user interface (GUI) It emphasizes storing and manipulating data as multidimensional arrays - vectors, matrices, tensors, etc. A great deal of functionality is already implemented in packages - both elementary (matrix operations, SVD, eigenvalues solvers, random number generators, optimization routines, etc.) and specicialized (kernel smoothing, support vector machines, etc.). Like Matlab it is slower than native C/C++/Java/Python applications and cannot handle extremely large datasets. Depending on how the code is vectorized 1 this may be a marginal slowdown or an extremely significant one. Both languages can interface with native C/C++ code in order to reduce computational bottlenecks. It is also different from Matlab in some important ways: R is open-source and freely available for all major operating systems (Windows, Linux, Mac). It is much easier to extend R and install packages contributed by others. As a result, R has an enormous number of high quality and well documented packages that are easy to install In contrast to Matlab which was initialy designed for engineers, R was designed for data analysis. As a result, the syntax of the language is much better suited for computational data analysis, statistics, and data visualization. It has several extremely powerful libraries for data visualiztion. R is less polished than Matlab in several ways: simpler GUI, less convenient profiler and editor, worse handling of sparse matrices. 2 First Commands First, download and install a copy of R from that is suitable for your operating system. That website is also an extremely useful source of documentation, news, and user contributed packages. On Linux you can invoke and run R within a terminal by typing R at the prompt. Another convenient way is to run it within emacs using the Emacs Speaks Statistics (ESS) package. In Windows R is used using the graphical user interface. In Mac both terminal (or emacs) and GUI version are available. The easiest way to quit R is to type q() at the prompt. To get help on a specific function, operator, or data object type help(x) where X is a case sensitive string within single or double quotes. Similarly, example(x) yields an example of using X. help.start() starts html-based documentation within a browser that is easy to navigate. Each line may contain one or more commands separated by the ; symbol. Here are some self explanatory important commands. 1 > # This is a comment 2 > x =3; y =3* x +2; # basic variable assignment and arithmetic 3 > y # same as print ( y) 1 Vectorized code means that that basic operations are done on entire arrays rather than on individual array elements inside nested loops 1

2 11 1 > ls (); # lists all variables in workspace "x " "y " 1 > save.image( file = ' varfile ' ); # saves all variables to a file 2 > rm(x, y); # clears variables x and y 3 > ls (); 1 character (0) 1 > load ( ' varfile ' ) # loads all variables back to the workspace 2 > ls (); "x " "y " When R is exited the current variables are saved to a file named.rdata in the current directory. When R is started it automatically uploads that file (in the current directory) if it exists and so retrives the set of variables from the previous session in that directory. Inside R you can change directories or view the current directory using setwd and getwd. Shell commands may be executed with the system functions e.g., in linux display all files in current directory by 1 > system ( ' ls -al ' ); Calling from R to C code, in particular computational bottlenecks is described in One of the nice features of R is the ease with which new packages can be installed (this applies to packages written by both the formal R developers as well as other users). The function install.packages(x) installs the functions and data in the package X (a string with single quotes) from the internet and library(x) brings the data and functions into the program s namespace or scope. The reason for this is that some packages may contain functions or datas that overlaps functions or data in other packages (remember that many packages are developed in a distributed way by independent contributors). A list of available packages, their implementation and documentation is available at As mentioned above a package contains both function definitions and datasets. A list of datasets available inside the package can be obtained using the function data 1 > data ( package = ' ggplot2 ' ); The functions source(x) and sink(x) causes R to executes commands in the file X and records the output to the file X respectively (similar to linux input/output redirection). The conventional suffix for a file with R commands is.r. 3 Vectors, Arrays, Lists, and Dataframes In R, data is kept and manipulated in vectors, arrays, lists and dataframes. A vector is one dimensional ordered collection of variables of the same type e.g., real numbers, booleans values, etc. An array is a multidimensional collection of which matrix is a two dimensional special case. Lists are ordered collections of variables of potentially different types. A dataframes are ordered collections of lists that is often interpreted as samples from a common distribution (think of a matrix whos rows are samples and columns are features or dimensions of potentially different type). Here are some examples concerning vectors. 1 > x=c (4,3,3,4,3,1); x # c() contcatanates arguments to create a vector > y= vector ( mode = ' logical ', length =4); y # a boolean vector initialized to FALSE FALSE FALSE FALSE FALSE 2

3 1 > z= vector ( length =3, mode = ' numeric ' ); z # a numeric vector initialized to > w= seq (0, 1, length. out =11); w # vector of 11 evenly spaced numbers in the range (0,1) > r=w 0.5; r # a boolean vector showing where w is less than or equal to 1/ 2 TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE 1 > w[ w 0.5 ]=0; w # zero out all components less than or equal to 1/ > w =2* w +1; w # element- wise operations Matrices and more generally arrays are multidimensional generalizations of vectors whose dimensionality is specified using the dim attribute. In arrays as in vectors all elements must have the same mode or type (numeric, boolean, character, etc.). 1 > x= array ( data = seq (1,20, length.out =20), dim =c (4,5)); x 1 [,1] [,2] [,3] [,4] [,5] 2 [1,] [2,] [3,] [4,] > x [2,3] # refer to the 2 nd row and 3 rd column 10 1 > x[2,] # refer to the entire second row > x[-1,] # all but the first row - same as x[ c(2,3,4),] 1 [,1] [,2] [,3] [,4] [,5] 2 [1,] [2,] [3,] > y=x[c(1,2),c(1,2)]; y=2*y +1; y # note elementwise operation 1 [,1] [,2] 2 [1,] [2,] > y %*% y # matrix product ( both arguments are matrices ) 3

4 1 [,1] [,2] 2 [1,] [2,] > x[1,] %*% x [1,] # inner product ( both vectors have the same dimensions ) 1 [,1] 2 [1,] > x[,1] %*% t( x [,1]) # outer product ( vectors have different dimensions ; t( X) transposes X) 1 [,1] [,2] [,3] [,4] 2 [1,] [2,] [3,] [4,] > rbind (x[1,],x[1,]) # vertical concatationn 1 [,1] [,2] [,3] [,4] [,5] 2 [1,] [2,] > cbind (x[1,],x[1,]) # horizontal concatanation 1 [,1] [,2] 2 [1,] [2,] [3,] [4,] [5,] Lists are similar to vetctors in being one dimensional but can hold variables of different types. The positions can be given names, which makes referring to them easier. 1 > L= list ( name = ' John ',age =55, manager = FALSE, no.children =3, children.ages =c (15,18,25)) 2 > L [[1]] # variable in first position - note double brackets for list elements [[.]] "John " 1 > L$ name # entry corresponding to name ( first ) "John " 1 > L$ children.ages [2] # same as L [[5]][2] 18 A dataframe is an ordered sequence of lists. It is easy to think of a dataframe as a two dimensional entity whose rows correspond to data examples (samples from a multivariatte distribution) and columns correspond to dimensions or features. They are like matrices with two important differences: the different columns may have different types (numeric, boolean, string) and may be assigned names for easier reference. In the example below we demonstrate the iris dataframe which which is a built-in R dataset (first four dimensions are numeric describing flower measurements, the last dimension is a string describing flower type). 1 > summary ( i r i s ) # statistical summary 4

5 1 Sepal. Length Sepal. Width Petal. Length Petal. Width 2 Min. :4.300 Min. :2.000 Min. :1.000 Min. : st Qu. : st Qu. : st Qu. : st Qu. : Median :5.800 Median :3.000 Median :4.350 Median : Mean :5.843 Mean :3.057 Mean :3.758 Mean : rd Qu. : rd Qu. : rd Qu. : rd Qu. : Max. :7.900 Max. :4.400 Max. :6.900 Max. : Species 9 setosa :50 10 versicolor :50 11 virginica :50 1 > names ( i r i s ) # dimension names "Sepal.Length " "Sepal.Width " "Petal.Length " "Petal.Width " "Species " 1 > i r i s [1,] # first sample 1 Sepal. Length Sepal. Width Petal. Length Petal. Width Species setosa 1 > i r i s $ Sepal. Length [1:10] # sepal length of first ten samples > attach ( i r i s ); # now we can use the shorter Sepal. Length instead of iris $ Sepal. Length 2 > mean ( Sepal.Length ) Dataframes can be easily created from text files with the right formatting. For example, consider the text file containing the same Iris data in - first line is the header containing dimension names and the remaining lines describe the data points. Such a file may be used to create a dataframe using the command 1 > Iris = read.table ( ' irisfile.txt ',header = TRUE ); # or else from internet : 2 > Iris = read.table ( ' http :// / wsc / visualizing.datatables / iris ',header = TRUE ); Dataframes and other variables can also be examined and edited within a spreadsheet like enviornment 1 > edit ( i r i s ) # examine data as spreadsheet 2 > i r i s = edit ( i r i s ) # edit dataframe / variable 3 > newiris = edit ( i r i s ) # edit dataframe / variable but keep original The function search() shows the search path for variables and functions, starting with the current global enviornment. 4 If-Else, Loops, and Functions The flow of control within an R program is very similar to that in other programming languages. Below are some examples of if-else, loops, and function definitions and function calls. 1 > a =10; b =5; c =1; 2 > if ( a<b ) d =1 else if ( a == b) d =2 else d =3; # may need curly braces if more than one line 3 > d 3 Logical operators are similar to C/C++: && for AND for OR. 5

6 1 > sm =0; 2 > for ( num in seq (1,100, by =1)) { 3 + sm=sm+num ; 5 > sm # same as sum (1:100) > repeat { 2 + sm= sm-num ; num = num-1 ; 3 + if ( sm ==0) break 5 > sm 0 1 > a =1; b =10; 2 > while (b >a) { 3 + sm=sm +1; 4 + a=a +1; b= b-1 ; 5 + } 6 > sm 5 Functions in R are similar to those in other languages like C/C++, Java, or Matlab. When calling a function the parameters are copied into the arguments according to the order they are entered. Parameters may be entered out of order if the parameter names are supplied. Default values for missing parameters are supported. 1 > mypower = function ( bas =10, pow =2) { 2 + res = bas pow ; 3 + return ( res ); 5 > mypower (2,3) 8 1 > mypower ( pow =3, bas =2) # same as before - named parameters are out of order 8 1 > mypower ( bas =3); # default power value is used 9 As in other languages, variables defined inside functions (including the arguments) are local and do not affect the program after the function completes its execution (except for the returned value). 5 Customization and Other Topics When R starts it executes the function.first() in the.rprofile file in the home directory (if it exist). This is a good place to put user preferred options. Similarly, a function.last in the same file is executed at the end of any R session. 1 >.First = function () { 2 + options ( prompt = ' R> ',digits =6) 3 + library ( ' ggplot2 ' ); 5 >.Last = function () { 6 + cat ( paste ( date (), "\ nbye \n ")); 7 + } 6

7 R may be executed with flags, for example R -q starts R without printing the initial welcome message or R -d gdb runs R via the debugger gdb. To run an R script file foo.r type R CMD BATCH foo.r &. You can pass arguments to the script using R CMD BATCH -args arg1 arg 2 foo.r &. To retrieve the arguments inside the script type args=commandargs(true). A shorter command for running script is Rscript foo.r. Another possibility to run R scripts is to write a shell script-type file 1 > #! / usr / bin / Rscript 2 > args = commandargs ( TRUE ); # retrieve arguments if available 3 >... # here comes a sequence of R commands 4 > q (1); # return value Such a file may executed by making it executable using chmod 755 filename. It is also possible to redirect input and output by typing in the shell prompt: filename < infile > outfile. 7