Beginning R: the Statistical programming language. Published by learning of R. Under the Manuals link are several manuals available in HTML or as PDF. Beginning R: The Statistical Programming Language Beginning R: An Introduction to Statistical Programming. Read more Statistical bioinformatics with R. Introduction This is a beginning to intermediate book on the statistical language and computing environment called R. As you will learn, R is freely available and .

Beginning R The Statistical Programming Language Pdf

Language:English, Portuguese, French
Genre:Children & Youth
Published (Last):10.11.2015
ePub File Size:19.59 MB
PDF File Size:12.41 MB
Distribution:Free* [*Register to download]
Uploaded by: SHOSHANA

chapteR 12 Writing Your Own Scripts: Beginning to Program. Beginning. R the StatiStical pRogRamming language. Mark Gardener .. PDF Device Driver. Beginning R, 2nd - Ebook download as PDF File .pdf), Text File .txt) or read book If you are already familiar with programming and statistics. Branch: master. R/Beginning R - The Statistical Programming Lang. - M. Gardener (Wrox, ) Find file Copy path. Fetching contributors Cannot.

For someone with both a fair grasp of traditional statistics and some programming experience. Chapters 1 through 5 focus on gaining familiarity with the R language itself. Your authors run on bit operating systems. At this writing.

If you do not already have R on your system. A dedicated core team of R experts maintains the language. Because not everything R does in Unix-based systems can be done in Windows.

An Introduction to Statistical Programming

Go ahead and download Rstudio current version as of this writing is 0. Developing from a novice into a more competent user of R may take as little as three months by only using R on a part-time basis disclaimer: R is an open-source implementation of the S language created and developed at Bell Labs. If you use Linux. R is accurate.

There are literally thousands of contributed packages available to R users for specialized data analyses. R works on Windows. I often switch to Ubuntu to do those things. One author runs Ubuntu on the site Cloud. You can stop anywhere on that journey you like.

We wish you the best of luck! Enthusiastic users. If you do not already have R. Mac OS. R users often develop into R programmers who write R functions. R is not sensitive to white space the way some languages are. There are some reserved names in R. You can use R in batch mode. Launch Rstudio and examine the resulting interface. Figure There are two basic assignment operators in R.

The best way to learn R is to use R. R is a high-level vectorized computer language and statistical computing environment. In some computer languages. As you learn more about R and how to use it effectively. R blog sites. We will begin with the basics in this book but will quickly progress to the point that you are ready to become a purposeful R programmer. You can write your own R code.

Then type the following. In the R console. Sometimes R will do something but give you warnings. You can assign values to variables without declaring the variable type.

Using the exponentiation operator 1: The most basic use of R is as a command-line interpreted language. Always remember that R. We can create sequences of integers by using the colon operator.

If R cannot do what you are asking. R will return the result on the next line. When you get to the personal information. If R can do what you are asking. This is because R does not recognize a scalar value. We also see square brackets [ and ].

The object y is a vector with one element. You can change the working directory by typing setwd. The [1] in front of x means that the first element of the vector appears at the beginning of the line. This allows us to gain access to our files from any Internet- connected computer. With the name and address. Character strings must be enclosed in quotation marks. We will call the vector x. As you can see from the code listing here..

Because the numbers are random. Numbers can be assigned as they were with the myPhone variable. To see a list of all the objects in your R session.

We can also create vectors. Unlike vectors. Go ahead and quit the current R session. These operators are vectorized. This is saved in an R history file. Table This will keep you from having to reenter your variables. For example. When you find that file and open it. In any of these cases. As with the mathematical operators and the logical operators see Chapter 4.

When you save your R session in an RData file. According to the R documentation. Before we go back to our R session. We will come back to the same session in a few minutes. To quit your R session. What was going on in the background while we played with R was that R was recording everything you typed in the console and everything it wrote back to the console. We will put R through some more paces now that you have a better understanding of its data types and its operators.

Note that some statistical software programs such as SPSS do not uniformly support the use of strings as factors. We can use it to add elements to a vector. If you try to make a vector with multiple data types. If you create a vector of two or more objects.

R treats a single number as a vector of length 1. Missing data in R are indicated by NA. There is also a special object called NULL. A data frame is a special kind of list and the most common data object for statistical analysis.

You will not need the list with your name and data. That way the 1 conveys meaning. Some character information can be used for factors. We can also subset a vector by using a range of indexes. As an example.

As you have learned. It makes more sense to have a column in a data frame labeled sex with two entries. In addition to vectors. R will coerce the vector into a single type. Like any list. There are symbol objects and function objects. My friends who are programmers who dabble in statistics think factors are evil. Vectors must contain only one type of data. If you like using 1s and 0s for factors. R has language objects including calls. If you wonder if something is possible. Take a look at what happens when we ask R for the letters of the alphabet and use the power of built-in character manipulation functions to make something a reproducible snippet of code.

For now. You can find many examples of efficient R code on the web. Always think like a programmer rather than a user. We can. The more you know about R.

You just saw me waste our time by typing in the letters A through J. Use a web search engine. R already knows the alphabet. Over two million people are using R right now. Create a simple vector using the c function some people say it means combine. The built-in 7 www. Everyone starts as an R user and ideally becomes an R programmer. R has a variety of built-in functions that automate even the simplest kind of operations.

That means a blinding flash of the obvious. I have had many of those in my experience with R. The R manual is also helpful. Observe that the negative index removes the selected element or elements from the vector but only changes the vector if you reassign the new vector to x.

I prefer combine because there is also a cat function for concatenating output. It is better to develop good habits in the beginning than it is to develop bad habits and then having to break them first before you can learn good ones. This is what Dr. When the length of the longer vector is not an exact multiple of the shorter 8 www.

See that R recycles y for each value of x. R coerces the data to a character vector because we added a character object to it. I used the index [11] to add the character element to the vector. Not every built-in function includes the na. To demonstrate this. You can use a negative index. You can also check to see if our modified vector is integer again.

NA is a legitimate logical character. No explicit looping was required: The na. We will discuss functional programming in Chapter 5. To determine the structure of a data object in R. But the vector now contains characters and you cannot do math on it. We will add a missing value by entering NA as an element of our vector. We can build matrices from vectors by using the cbind or rbind functions. If the off-diagonal elements of a square matrix are the same above and below the diagonal.

In statistics. Matrix multiplication is the most important operation for statistics. A vector or array is a 1-by-n or an n-by-1 matrix. Although R gave us a warning. Some of the most common matrix manipulations are transposition. Remember that z has 33 elements and x has If you have studied matrix algebra.

An entire matrix is represented by a boldface letter. This can produce unusual results. The diagonal of a square matrix is the set of elements going from the upper left corner to the lower right corner of the matrix. Matrices have rows and columns. We can also find the determinant of a square matrix.

You can coerce a data frame to a matrix by using the as. We will never use anything but numbers in matrices in this book.

A difficulty in the real world is that some matrices cannot be inverted. The is. My best advice. When we have character data. Note the way we do this to avoid duplicating A. This is the matrix algebraic analog of division if you talk to a mathematician. With this background behind us. Given two square matrices. In matrix algebra. A and B. The matrix inversion algorithm accumulates some degree of rounding error.

Lists are unusual in a couple of ways. The inverse multiplied by the original matrix should give us the identity matrix. The final grades might look like the following: Dr Pace. Donte F 80 freshman 8 20 Roper. Gabe G 75 freshman 6 12 Hall. As you can see. I saved the roster as a comma-separated value CSV file and then read it into R using the read. The [[1]] indicates the first element of the list.

Jordan G 72 junior 9 21 Harrison. Avry G 74 junior 4 5 Blossomgame. The sapply function produces a simplified view of the means and standard deviations.

Recall that earlier we discussed both getwd and setwd. A data frame is a list.

Beginning R: The Statistical Programming Language

Please note that in this case. Austin G 78 sophomore 3 3 Holmes. Damarcus G 76 senior continued 12 www. Every row in the data frame represents a case. Note that the lapply function works here as well. Rod G 73 senior 7 15 Grantham. Every column represents a variable or a factor in the dataset.

As with the name and address data. Jaron F 79 sophomore 5 10 DeVoe. Patrick G 74 freshman 2 1 Ajukwa. Landry C 82 junior 12 44 McGillan. To view your data without editing them. Josh F 80 junior 11 35 Nnoko. Riley G 72 junior 13 50 Djitte. An Approach for Data Scientists. We will touch lightly on the issues of dealing with R in the cloud and with big or at least bigger data in subsequent chapters. Date [1] " " By adding symbols and using the format command. To lay the foundation for discussing some ways of dealing with real-world data effectively.

Data lakes solve the problem of independently managed information silos an old problem in information technology. If you are particularly interested in using R for cloud computing. In later chapters. You learned about various data types in Chapter 1.

Date function: You can return the current date and time by using the date function and the current day by using the Sys. Dates are represented as the number of days since January 1. These symbols are as follows: This flies in the face of the carefully structured and highly managed data most of us have come to know and love. Chapter 2 Dealing with Dates. Data analysts are facing major issues related to the use of larger datasets.

This is a good precursor to our more detailed discussion of text mining later on. Fisher was a staunch advocate of declaring a null hypothesis that stated a certain population state of affairs.

Every experiment may be said to exist only to give the facts a chance of disproving the null hypothesis. We will look at how to get string data into R. I found the quote on a statistics quotes web page. Although scan is more flexible. Fisher Although it would be possible to type this quote into R directly using the console or the R Editor. As a statistical aside. Notice the use of cat to concatenate and output the desired objects: January There are many good text editors.

R" You can read the entire text file into R using either readLines or scan. The null hypothesis is never proved or established. The reserved characters are. Most characters. These expressions match themselves. You can also use the glob2rx function to create specific patterns for use in regular expressions.

As always. Before we do. We can also use functions on character strings as we do with numbers. We can search for specific characters. We will continue to work with our quotation. This is certainly not true. R also includes special reserved characters called metacharacters in the extended regular expressions. A regular expression is a specific pattern in a string or a set of strings. These have a special status. R uses three types of such expressions: We will have much more to discuss about the current state and likely future state of null hypothesis significance testing NHST.

In addition to these functions. Let us pretend that Jerzy Neyman actually made the quotation we attributed to Fisher. Median Mean 3rd Qu. The complete dataset has 32 cars and 10 variables for each car.

To illustrate We will also learn how to find specific rows of data: We can refer to this column in two ways. If you have used other statistical packages. The head function returns the first part or parts of a vector..

Because data frames have both rows and columns. There are many ways to create data frames. To refer to an entire row or an entire column. Sometimes we need to change the structure of the data frame to accommodate certain situations. Median Mean 3rd Qu.. We can find the row containing a particular value very easily using the which function: Attaching 19 www. As with vectors. Here is how to subset the data in R. We will remove the displacement variable.

For this illustration. I was able to create it and add it to the data frame at the same time.

We will start with a narrow or stacked representation of our data. Narrow data. It is easier to show this than it is to explain it. Notice I only used the first 13 entries of colors as mpgMan only has 13 manual vehicles: Recall our roster data from Chapter 1. I created a character vector with three color names. Wide and narrow data are often referred to as unstacked and stacked. Both can be used to display tabular data.

Examine the following code listing to see how this works. Data cleaning and data munging are rarely included as a subject in statistics classes.

Each correct and incorrect responses are labeled as such. The majority of the time I have spent with data analysis has been in preparation of the data for subsequent analyses. Real data distributions are rarely normally distributed. As you learned earlier. Datasets often have missing values and outliers. There are many ways perhaps to create the proportions we seek. I can use R to make my list of variable names without having to type as much as you might suspect.

For each of the words. Of note here is that we definitely recommend using the top left Rscript area of Rstudio to type in these functions.

This is additionally interesting because we have text rather than numerical data a frequent enough phenomena in survey data. I want to subset the data. Keeping in mind the DRY principle.

The rbind function is used to simply make it all look pretty consider typing in percents into your Rstudio console after running the below code to see why rbind is so helpful. Since we want a proportion table for each word. Before we could do that. The table function creates a contingency table with a count of each combination of factors.

It seems a little changed. And yet. At the end. We have added that to the data frame using the cbind function. Knowing these functions will make your life easier. As you have already seen. Until you have memorized them. If the file is in the current directory.

You can find out whether you did by using the file. People are usually better at entering data in columns than rows. R is perhaps not as flexible as Python or other languages.

You also learned in Chapter 2 how to read in string data from a text file. To get a complete list of all of the functions related to files and directories. R provides all of the basic input and output capabilities that an average user is likely to need. There are many more things you can do with strings.

In the example of the statistics quote.

Gardener M. Beginning R: The Statistical Programming Language

In Chapter 3. This will help greatly with your file management for both input and output. Chapter 3 Input and Output As a scripting language. We can use the scan function to read in data instead of typing the data in by using the c function. If you need a file in a different directory. Remember the functions getwd and setwd are used to identify and change the working directory. To prepare for our discussion of input and output. If you want to know the information about a particular file.

When the demands of data input exceed the capacity of the console. Read it into the workspace as follows: Examine the following code fragment so see how this works: It is also possible to read a vector into your workspace by using the scan function. When you fix the labels. We might use the fix function. Although the Data Editor is not suitable for creating larger datasets.

I find the Rstudio editor useful for writing lines of code and editing them before executing them. To open the Data Editor. We will discuss functional programming in more depth in Chapter 5. R data files. The scan function and the readline function can be used as well. You can also request user input via the console.

I want the strings to be recognized as factors. For text files. The default behavior is TRUE. You must also tell read. To read the function into your R session. If you use the read. We will discuss factors in much more detail later.: Remember that when the data are in text format. When the function is executed. You should see the code in the R console now. After the user enters these. Omit the R command prompts when typing code in the Editor window. The basic operation in R to read in a data file is read.

Some of the most common matrix manipulations are transposition, addition and subtraction, and multiplication. Matrix multiplication is the most important operation for statistics. We can also find the determinant of a square matrix, and the inverse of a square matrix with a nonzero determinant. In matrix algebra, we write the following, where B1 is the inverse of B.

With this background behind us, lets go ahead and use some of Rs matrix operators. A difficulty in the real world is that some matrices cannot be inverted.

For example, a so-called singular matrix has no inverse. Lets start with a simple correlation matrix: 1. Note the way we do this to avoid duplicating A; for very large data, this may be more compute efficient. Start on. Show related SlideShares at end. WordPress Shortcode.

Published in: Full Name Comment goes here. Are you sure you want to Yes No. Be the first to like this.

No Downloads. Views Total views. Actions Shares. Embeds 0 No embeds. No notes for slide. Beginning r the statistical programming language [pdf] download 1.

Beginning R: Book Details Author: Mark Gardener Pages:In this particular case, we see that the values are fairly close to each other, and that both are not very different from the standard deviation. Becoming a good programmer usually is a developmental process. When you can create and then validate a useful R program, you will have a great and well deserved sense of personal satisfaction.

Launch Rstudio and examine the resulting interface. Statistical bioinformatics with R. RData file by using the ls command:.