Program r guide

Cook in a Web post about R programming for those coming from other languages. And so, this guide. Our aim here isn't R mastery, but giving you a path to start using R for basic data work: Extracting key statistics out of a data set, exploring a data set with basic graphics and reshaping data to make it easier to analyze.

Here are the latest Insider stories. More Insider Sign Out. Sign In Register. Sign Out Sign In Register. Latest Insider. Check out the latest Insider stories here. All issues in a package's source code and documentation should be addressed until R CMD check returns no error or warning messages anymore.

C Install package from source: Linux: install. Instructions to fully build an R package under Windows can be found here and here. R" imports functions, methods and classes from myscript. R prompt myfct writes help file myfct.

Rd promptClass "myclass" writes file myclass-class. Rd promptMethods "mymeth" writes help file mymeth. Rd files as they look in final help pages checkRd ". Rd help file for problems. The best way of sharing an R package with the community is to submit it to one of the main R package repositories, such as CRAN or Bioconductor. Download on of the above exercise files, then start editing this R source file with a programming text editor, such as Vim, Emacs or one of the R GUI text editors.

Here is the HTML version of the code with syntax coloring. This way one can organize file names by external table. R execute from shell. The script ' sequenceAnalysis. R ' demonstrates how R can be used as a powerful tool for managing and analyzing large sets of biological sequences. Translation of this Page. This site was accessed times detailed access stats.

Venables and B. Ripley Programming with Data , by John M. If Statements If statements operate on length-one logical vectors. Less common are repeat loops. The break function is used to break out of loops, and next halts the processing of the current iteration and advances the looping index. For Loop For loops are controlled by a looping vector.

In every iteration of the loop one value in the looping vector is assigned to a variable that can be used in the statements of the body of the loop. Usually, the number of loop iterations is defined by the number of values stored in the looping vector and they are processed in the same order as they are stored in the looping vector.

Syntax tapply vector, factor, FUN Example Computes mean values of vector agregates defined by factor tapply as. This means there needs to be a second statement to test whether or not to break from the loop. However, this limitation can be overcome by eliminating certain operations in loops or avoiding loops over the data intensive dimension in an object altogether. The latter can be achieved by performing mainly vector-to-vecor or matrix-to-matrix computations which run often over times faster than the corresponding for or apply loops in R.

For this purpose, one can make use of the existing speed-optimized R functions e. Alternatively, one can write programs that will perform all time consuming computations on the C-level. In fact, most of the R software can be viewed as a series of R functions. Naming Function names can be almost anything. Arguments It is often useful to provide default values for arguments e. Calling functions Functions are called by their name followed by parentheses containing possible argument names.

Scope Variables created inside a function exist only for the life time of a function. Stop To stop the action of a function and print an error message, one can use the stop function.

Warning To print a warning message in unexpected situations without aborting the evaluation flow of a function, one can use the function warning " The Debugging in R page provides an overview of the available resources. The following example demonstrates the retrieval of specific lines from an external file with a regular expression.

First, an external file is created with the cat function, all lines of this file are imported into a vector with readLines , the specific elements lines are then retieved with the grep function, and the resulting lines are split into vector fields with strsplit. Second, the files are imported one-by-one using a for loop where the original names are assigned to the generated data frames with the assign function.

Consult help with? R" Table of Contents 2. R the following statement:! R [outfile] The output file lists the commands from the script file and their outputs. If no outfile is specified, the name used is that of infile and. Rout is appended to outfile. R , then nothing will be saved in the. Rdata file which can get often very large. R 10 In the given example the number 10 is passed on from the command-line as an argument to the R script which is used to return to STDOUT the first 10 rows of the iris sample data.

If several arguments are provided, they will be interpreted as one string that needs to be split it in R with the strsplit function. R This script doesn't need to have executable permissions. R is located. To utilize several CPUs on the Linux cluster, one can divide the input data into several smaller subsets and execute for each subset a separate process from a dedicated directory.

An older S3 system and a more recently introduced S4 system. The latter is more formal, supports multiple inheritance, multiple dispatch and introspection.

Many of these features are not available in the older S3 system. So logical class is coerced to numeric class making TRUE as 1. In R, a variable itself is not declared of any data type, rather it gets the data type of the R - object assigned to it. To know all the variables currently available in the workspace we use the ls function. Also the ls function can use patterns to match the variable names. The variables starting with dot. Variables can be deleted by using the rm function.

Below we delete the variable var. On printing the value of the variable error is thrown. An operator is a symbol that tells the compiler to perform specific mathematical or logical manipulations. R language is rich in built-in operators and provides following types of operators. Following table shows the arithmetic operators supported by R language. The operators act on each element of the vector. Following table shows the relational operators supported by R language.

Each element of the first vector is compared with the corresponding element of the second vector. The result of comparison is a Boolean value.

Following table shows the logical operators supported by R language. It is applicable only to vectors of type logical, numeric or complex.

All numbers greater than 1 are considered as logical value TRUE. These operators are used to for specific purpose and not general mathematical or logical computation.

Decision making structures require the programmer to specify one or more conditions to be evaluated or tested by the program, along with a statement or statements to be executed if the condition is determined to be true , and optionally, other statements to be executed if the condition is determined to be false.

R provides the following types of decision making statements. Click the following links to check their detail. An if statement can be followed by an optional else statement, which executes when the Boolean expression is false.

There may be a situation when you need to execute a block of code several number of times. In general, statements are executed sequentially. The first statement in a function is executed first, followed by the second, and so on. Programming languages provide various control structures that allow for more complicated execution paths.

R programming language provides the following kinds of loop to handle looping requirements. Executes a sequence of statements multiple times and abbreviates the code that manages the loop variable. Repeats a statement or group of statements while a given condition is true. It tests the condition before executing the loop body.

Loop control statements change execution from its normal sequence. When execution leaves a scope, all automatic objects that were created in that scope are destroyed. Terminates the loop statement and transfers execution to the statement immediately following the loop. A function is a set of statements organized together to perform a specific task. R has a large number of in-built functions and the user can create their own functions. In R, a function is an object so the R interpreter is able to pass control to the function, along with arguments that may be necessary for the function to accomplish the actions.

The function in turn performs its task and returns control to the interpreter as well as any result which may be stored in other objects. An R function is created by using the keyword function. It is stored in R environment as an object with this name. When a function is invoked, you pass a value to the argument.

Arguments are optional; that is, a function may contain no arguments. Also arguments can have default values. R has many in-built functions which can be directly called in the program without defining them first. We can also create and use our own functions referred as user defined functions. Simple examples of in-built functions are seq , mean , max , sum x and paste They are directly called by user written programs.

You can refer most widely used R functions. We can create user-defined functions in R. They are specific to what a user wants and once created they can be used like the built-in functions. Below is an example of how a function is created and used. The arguments to a function call can be supplied in the same sequence as defined in the function or they can be supplied in a different sequence but assigned to the names of the arguments.

We can define the value of the arguments in the function definition and call the function without supplying any argument to get the default result.

But we can also call such functions by supplying new values of the argument and get non default result. Arguments to functions are evaluated lazily, which means so they are evaluated only when needed by the function body.

Any value written within a pair of single quote or double quotes in R is treated as a string. Internally R stores every string within double quotes, even when you create them with single quote.

The quotes at the beginning and end of a string should be both double quotes or both single quote. They can not be mixed. Many strings in R are combined using the paste function.

It can take any number of arguments to be combined together. But not the space within two words of one string. Vectors are the most basic R data objects and there are six types of atomic vectors. They are logical, integer, double, complex, character and raw. Even when you write just one value in R, it becomes a vector of length 1 and belongs to one of the above vector types. Elements of a Vector are accessed using indexing. The [ ] brackets are used for indexing. Indexing starts with position 1.

Giving a negative value in the index drops that element from result. Two vectors of same length can be added, subtracted, multiplied or divided giving the result as a vector output. If we apply arithmetic operations to two vectors of unequal length, then the elements of the shorter vector are recycled to complete the operations.

A list can also contain a matrix or a function as its elements. List is created using list function. Following is an example to create a list containing strings, numbers, vectors and a logical values. Elements of the list can be accessed by the index of the element in the list. In case of named lists it can also be accessed using the names.

We can add, delete and update list elements as shown below. We can add and delete elements only at the end of a list. But we can update any element. A list can be converted to a vector so that the elements of the vector can be used for further manipulation. All the arithmetic operations on vectors can be applied after the list is converted into vectors. To do this conversion, we use the unlist function.

It takes the list as input and produces a vector. Matrices are the R objects in which the elements are arranged in a two-dimensional rectangular layout. They contain elements of the same atomic types.

Though we can create a matrix containing only characters or only logical values, they are not of much use. We use matrices containing numeric elements to be used in mathematical calculations. Elements of a matrix can be accessed by using the column and row index of the element. We consider the matrix P above to find the specific elements below. Various mathematical operations are performed on the matrices using the R operators.

The result of the operation is also a matrix. The dimensions number of rows and columns should be same for the matrices involved in the operation. Arrays are the R data objects which can store data in more than two dimensions.

Arrays can store only data type. An array is created using the array function. It takes vectors as input and uses the values in the dim parameter to create an array. We can give names to the rows, columns and matrices in the array by using the dimnames parameter. As array is made up matrices in multiple dimensions, the operations on elements of array are carried out by accessing elements of the matrices. We use the apply function below to calculate the sum of the elements in the rows of an array across all the matrices.

Factors are the data objects which are used to categorize the data and store it as levels. They can store both strings and integers. They are useful in the columns which have a limited number of unique values. Like "Male, "Female" and True, False etc. They are useful in data analysis for statistical modeling. On creating any data frame with a column of text data, R treats the text column as categorical data and creates factors on it.

The order of the levels in a factor can be changed by applying the factor function again with new order of the levels. We can generate factor levels by using the gl function.

It takes two integers as input which indicates how many levels and how many times each level. A data frame is a table or a two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column. The statistical summary and nature of the data can be obtained by applying summary function. To add more rows permanently to an existing data frame, we need to bring in the new rows in the same structure as the existing data frame and use the rbind function.

In the example below we create a data frame with new rows and merge it with the existing data frame to create the final data frame. R packages are a collection of R functions, complied code and sample data. They are stored under a directory called "library" in the R environment. By default, R installs a set of packages during installation. More packages are added later, when they are needed for some specific purpose. When we start the R console, only the default packages are available by default.

Other packages which are already installed have to be loaded explicitly to be used by the R program that is going to use them. All the packages available in R language are listed at R Packages. When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc. There are two ways to add new R packages. One is installing directly from the CRAN directory and another is downloading the package to your local system and installing it manually.

The following command gets the packages directly from CRAN webpage and installs the package in the R environment. You may be prompted to choose a nearest mirror. Choose the one appropriate to your location. Go to the link R Packages to download the package needed.

Save the package as a. Before a package can be used in the code, it must be loaded to the current R environment. You also need to load a package that is already installed previously but not available in the current environment. Data Reshaping in R is about changing the way data is organized into rows and columns. Most of the time data processing in R is done by taking the input data as a data frame. It is easy to extract data from the rows and columns of a data frame but there are situations when we need the data frame in a format that is different from format in which we received it.

R has many functions to split, merge and change the rows to columns and vice-versa in a data frame. We can join multiple vectors to create a data frame using the cbind function. Also we can merge two data frames using rbind function.

We can merge two data frames by using the merge function. The data frames must have same column names on which the merging happens. On choosing these two columns for merging, the records where values of these two variables match in both data sets are combined together to form a single data frame. One of the most interesting aspects of R programming is about changing the shape of the data in multiple steps to get a desired shape. The functions used to do this are called melt and cast.

Now we melt the data to organize it, converting all columns other than type and year into multiple rows. We can cast the molten data into a new form where the aggregate of each type of ship for each year is created.

It is done using the cast function. In R, we can read data from files stored outside the R environment. We can also write data into files which will be stored and accessed by the operating system.

R can read and write into various file formats like csv, excel, xml etc. In this chapter we will learn to read data from a csv file and then write data into a csv file. The file should be present in current working directory so that R can read it. Of course we can also set our own directory and read files from there. You can check which directory the R workspace is pointing to using the getwd function. You can also set a new working directory using setwd function. The csv file is a text file in which the values in the columns are separated by a comma.

Let's consider the following data present in the file named input. You can create this file using windows notepad by copying and pasting this data. Save the file as input. Following is a simple example of read.

By default the read. This can be easily checked as follows. Also we can check the number of columns and rows. Once we read data in a data frame, we can apply all the functions applicable to data frames as explained in subsequent section. R can create csv file form existing data frame. The write. This file gets created in the working directory.

Here the column X comes from the data set newper. This can be dropped using additional parameters while writing the file.

Microsoft Excel is the most widely used spreadsheet program which stores data in the. R can read directly from these files using some excel specific packages. Few such packages are - XLConnect, xlsx, gdata etc. We will be using xlsx package. R can also write into excel file using this package. You can use the following command in the R console to install the "xlsx" package. It may ask to install some additional packages on which this package is dependent. Follow the same command with required package name to install the additional packages.

Save the Excel file as "input. You should save it in the current working directory of the R workspace. The input.