R programming for data science pdf download

Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts in social media mining. This book is composed of 9 chapters introducing advanced text mining techniques.

They are various techniques from relation extraction to under or less resourced language. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.

Learn how to use a problem's "weight" against itself. Learn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all. Its function is something like a traditional textbook — it will provide the detail and background theory to support the School of Data courses and challenges.

This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience D3 Tips and Tricks is a book written to help those who may be unfamiliar with JavaScript or web page creation get started turning information into visualization. Create and publish your own interactive data visualization projects on the Web—even if you have little or no experience with data visualization or web development.

MapReduce [45] is a programming model for expressing distributed computations on massive amounts of data and an execution framework for large-scale data processing on clusters of commodity servers. It was originally developed by Google It aims to make Hadoop knowledge accessible to a wider audience, not just to the highly technical.

Intro to Hadoop - An open-source framework for storing and processing big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines. This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop. In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success.

The Data Science Handbook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you. Useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends.

Practical programming for total beginners. In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required. This is a hands-on guide to Python 3 and its differences from Python 2. Each chapter starts with a real, complete code sample, picks it apart and explains the pieces, and then puts it all back together in a summary at the end.

The first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know to analyze their own data using the R language.

Each chapter gives you the complete source code for a new game and teaches the programming concepts from these examples.

I Dani started teaching the introductory statistics class for psychology students offered at the University of Adelaide, using the R statistical package as the primary tool. These are my own notes for the class which were trans-coded to book form. Introduction to computer science using the Python programming language.

It covers the basics of computer programming in the first part while later chapters cover basic algorithms and data structures. This is a hands-on introduction to the Python programming language, written for people who have no experience with programming whatsoever. After all, everybody has to start somewhere. This book is NOT introductory. The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied.

This book is designed to introduce students to programming and computational thinking through the lens of exploring data. You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet. This is a simple book to learn the Python programming language, it is for the programmers who are new to Python.

This book describes Python, an open-source general-purpose interpreted programming language available for a broad range of operating systems. This book describes primarily version 2, but does at times reference changes in version 3. The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R.

It is supposed to be organized by task but not by discipline. This defaults to TRUE because back in the old days, if you had data that were stored as strings, it was because those strings represented levels of a categorical variable. For small to moderately sized datasets, you can usually call read. Telling R all these things directly makes R run faster and more efficiently. Reading in Larger Datasets with read. If the dataset is larger than the amount of RAM on your computer, you can probably stop right here.

In order to use this option, you have to know the class of each column in your data frame. A mild overestimate is okay. You can use the Unix tool wc to calculate the number of lines in a file. Can you close any of them? Some operating systems can limit the amount of memory a single process can access Calculating Memory Requirements for R Objects Because R stores all of its objects physical memory, it is important to be cognizant of how much memory is being used up by all of the data objects residing in your workspace.

For example, suppose I have a data frame with 1,, rows and columns, all of which are numeric data. Roughly, how much memory is required to store this data frame? Most computers these days have at least that much RAM. This is usually an unpleasant experience that usually requires you to kill the R process, in the best case scenario, or reboot your computer, in the worst case. So make sure to do a rough calculation of memeory requirements before reading in a large dataset.

However, there is an intermediate format that is textual, but not as simple as something like CSV. The format is native to R and is somewhat readable because of its textual nature. One can create a more descriptive representation of an R object by using the dput or dump functions. The dump and dput functions are useful because the resulting textual format is edit- able, and in the case of corruption, potentially recoverable. For example, we can preserve the class of each column of a table or the levels of a factor variable.

Textual formats can work much better with version control programs like subversion or git which can only track changes meaningfully in text files. In addition, textual formats can be longer-lived; if there is corruption somewhere in the file, it can be easier to fix the problem because one can just open the file in an editor and look at it although this would probably only be done in a worst case scenario!

There are a few downsides to using these intermediate textual formats. The format is not very space- efficient, because all of the metadata is specified. Also, it is really only partially readable. In some instances it might be preferable to have data stored in a CSV file and then have a separate code file that specifies the metadata. Using dput and dump One way to pass data around is by deparsing the R object with dput and reading it back in parsing it using dget.

The output of dput can also be saved directly to a file. The key functions for converting R objects into a binary format are save , save. Individual R objects can be saved to a file using the save function. RData extension when using save.

This is just my personal preference; you can use whatever file extension you want. The save and save. RData are fairly common extensions and you may want to use them because they are recognized by other software. The serialize function is used to convert individual R objects into a binary format that can be communicated across an arbitrary connection.

This may get sent to a file, but it could get sent over a network or other connection. When you call serialize on an R object, the output will be a raw vector coded in hexadecimal format. The benefit of the serialize function is that it is the only way to perfectly represent an R object in an exportable format, without losing precision or any metadata. If that is what you need, then serialize is the function for you. Connections can be made to files most common or to other more exotic things.

Connections can be thought of as a translator that lets you talk to objects that are outside of R. Those outside objects could be anything from a data base, a simple text file, or a a web service API. Connections allow R functions to talk to all these different external objects without you having to write custom code for each object. File Connections Connections to text files can be created with the file function.

The above example shows the basic approach to using connections. Connections must be opened, then the are read from or written to, and then they are closed.

This function is useful for reading text files that may be unstructured or contain non-standard data. The above example used the gzfile function which is used to create a connection to files compressed using the gzip algorithm. This approach is useful because it allows you to read from a file without having to uncompress the file first, which would be a waste of space and time.

There is a complementary function writeLines that takes a character vector and writes each element of the vector one line at a time to a text file. Since web pages are basically text files that are stored on a remote server, there is conceptually not much difference between a web page and a local text file.

However, we need R to negotiate the communication between your computer and the web server. This is what the url function can do for you, by creating a url connection to a web server. This code might take time depending on your connection speed. However, more commonly we can use URL connection to read in specific data files that are stored on web servers. Using URL connections can be useful for producing a reproducible analysis, because the code essentially documents where the data came from and how they were obtained.

This is approach is preferable to opening a web browser and downloading a dataset by hand. Of course, the code you write with connections may not be executable at a later date if things on the server side are changed or reorganized. It can only be used to extract a single element and the class of the returned object will not necessarily be a list or data frame. Its semantics are similar to that of [[.

Subsetting a Vector Vectors are basic objects in R and they can be subsetted using the [ operator. Here we extract the first four elements of the vector. This behavior is used to access entire rows or columns of a matrix. This is a feature that is often quite useful during interactive work, but can later come back to bite you when you are writing longer programs or functions.

Here we extract the first element of the list. Remember that the [ operator always returns an object of the same class as the original. Since the original object was a list, the [ operator returns a list. In the above code, we returned a list with two elements the first and the third.

In those cases, you should refer to the full element name if possible. R Wind Temp Month Day 1 41 7. This allows you to write code that is efficient, concise, and easier to read than in non-vectorized languages. The simplest example is when adding two vectors together. Another operation you can do in a vectorized manner is logical comparisons.

So suppose you wanted to know which elements of a vector were greater than 2. You could do he following. Of course, subtraction, multiplication and division are also vectorized. This way, we can do element-by-element operations on matrices without having to loop over every element. Dates are stored internally as the number of days since while times are stored internally as the number of seconds since I just thought those were fun facts. Date function. This is a common way to end up with a Date object in R.

POSIXct is just a very large integer under the hood. It use a useful class when you want to store times in something like a data frame.

This is useful when you need that kind of information. POSIXlt or as. POSIXct function. I can never remember the formatting strings. You can do comparisons too i. Date", "-. Date, as. POSIXlt, or as. Control structures allow you to respond to inputs or to features of the data and execute different R expressions accordingly.

For starters, you can just use the if statement. If you have an action you want to execute when the condition is false, then you need an else clause. This expression can also be written a different, but equivalent, way in R.

Which one you use will depend on your preference and perhaps those of the team you may be working with. Of course, the else clause is not necessary. You could have a series of if clauses that always get executed if their respective conditions are true. In R, for loops take an interator variable and assign it successive values from a sequence or vector. For loops are most commonly used for iterating over the elements of an object list, vector, etc. The following three loops all have the same behavior.

Nested for loops for loops can be nested inside of each other. Be careful with nesting though. If you find yourself in need of a large number of nested loops, you may want to break up the loops by using functions discussed later.

If it is true, then they execute the loop body. Once the loop body is executed, the condition is tested again, and so forth, until the condition is false, after which the loop exits. Use with care! Sometimes there will be more than one condition in the test. For example, in the above code, if z were less than 3, the second test would not have been evaluated. These are not commonly used in statistical or data analysis applications but they do have their uses.

The only way to exit a repeat loop is to call break. You could get in a situation where the values of x0 and x1 oscillate back and forth and never converge. Better to set a hard limit on the number of iterations by using a for loop and then report whether convergence was achieved or not.

Functions Writing functions is a core activity of an R programmer. Functions are often used to encapsulate a sequence of expressions that need to be executed numerous times, perhaps under slightly different conditions. Functions are also often written when code must be shared with others or the public. The writing of a function allows a developer to create an interface to the code, that is explicitly specified with a set of parameters.

This interface provides an abstraction of the code to potential users. In addition, the creation of an interface allows the developer to communicate to the user the aspects of the code that are important or are most relevant. This is very handy for the various apply funtions, like lapply and sapply. However, they are really important in R and can be useful for data analysis. Your First Function Functions are defined using the function directive and are stored as R objects just like anything else.

The next thing we can do is create a function that actually has a non-trivial function body. The last aspect of a basic function is the function arguments. These are the options that you can specify to the user that the user may explicity set. Hello, world! Obviously, we could have just cut-and-pasted the cat "Hello, world!

But often it is useful if a function returns something that perhaps can be fed into another section of code. This next function returns the total number of characters printed to the console. In R, the return value of a function is always the very last expression that is evaluated. Because the chars variable is the last expression that is evaluated in this function, that becomes the return value of the function.

Note that there is a return function that can be used to return an explicity value from a function, but it is rarely used in R we will discuss it a bit later in this chapter. Finally, in the above function, the user must specify the value of the argument num.

If it is not specified by the user, R will throw an error. Any function argument can have a default value, if you wish to specify it. Sometimes, argument values are rarely modified except in special cases and it makes sense to set a default value for that argument. This relieves the user from having to specify the value of that argument every single time the function is called. The formal arguments are the arguments included in the function definition.

Because all function arguments have names, they can be specified using their name. Functions 60 Argument Matching Calling an R function with arguments can be done in a variety of ways. R functions arguments can be matched positionally or by name.

Positional matching just means that R assigns the first value to the first argument, the second value to second argument, etc. The following calls to the sd function which computes the empirical standard deviation of a vector of numbers are all equivalent. Note that sd has two arguments: x indicates the vector of numbers and na. In the example below, we specify the na. Below is the argument list for the lm function, which fits linear models to a dataset. NULL The following two calls are equivalent.

Most of the time, named arguments are useful on the command line when you have a long argument list and you want to use the defaults for everything except for an argument near the end of the list.

Named arguments also help if you can remember the name of the argument and not its position on the argument list. For example, plotting functions often have a lot of options to allow for customization, but this makes it difficult to remember exactly the position of every argument on the argument list. Function arguments can also be partially matched, which is useful for interactive work.

The order of operations when given an argument is 1. Check for exact match for a named argument 2. Check for a partial match 3. Check for a positional match Partial matching should be avoided when writing longer code or programs, because it may lead to confusion if someone is reading the code. However, partial matching is very useful when calling functions interactively that have very long argument names. In addition to not specifying a default value, you can also set an argument value to NULL.

It is sometimes useful to allow an argument to take the NULL value, which might indicate that the function should take some specific action. Lazy Evaluation Arguments to functions are evaluated lazily, so they are evaluated only as needed in the body of the function. In this example, the function f has two arguments: a and b.

This behavior can be good or bad. This example also shows lazy evaluation at work, but does eventually result in an error. This is because b did not have to be evaluated until after print a. Once the function tried to evaluate print b the function had to throw an error.

Functions 63 The Argument There is a special argument in R known as the Pass ' This is clear in functions like paste and cat. So the first argument to either function is Arguments Coming After the Argument One catch with Take a look at the arguments to the paste function.

When R tries to bind a value to a symbol, it searches through a series of environments to find the appropriate value. When you are working on the command line and need to retrieve the value of an R object, the order in which things occur is roughly 1. Search the global environment i. Search the namespaces of each of the packages on the search list The search list can be found by using the search function.

For better or for worse, the order of the packages on the search list matters, particularly if there are multiple objects with the same name in different packages. Users can configure which packages get loaded on startup so if you are writing a function or a package , you cannot assume that there will be a set list of packages available in a given order.

When a user loads a package with library the namespace of that package gets put in position 2 of the search list by default and everything else gets shifted down the list. The scoping rules of a language determine how a value is associated with a free variable in a function.

An alternative to lexical scoping is dynamic scoping which is implemented by some languages. Lexical scoping turns out to be particularly useful for simplifying statistical computations Related to the scoping rules is how R uses the search list to bind a value to a symbol Consider the following function. In the body of the function there is another symbol z.

In this case z is called a free variable. The scoping rules of a language determine how values are assigned to free variables. Free variables are not formal arguments and are not local variables assigned insided the function body. Lexical scoping in R means that the values of free variables are searched for in the environment in which the function was defined.

Okay then, what is an environment? An environment is a collection of symbol, value pairs, i. The only environment without a parent is the empty environment.

A function, together with an environment, makes up what is called a closure or function closure. How do we associate a value to a free variable? If a value for a given symbol cannot be found once the empty environment is arrived at, then an error is thrown. One implication of this search process is that it can be affected by the number of packages you have attached to the search list.

The more packages you have attached, the more symbols R has to sort through in order to assign a value. Now things get interesting—in this case the environment in which a function is defined is the body of another function! Here is an example of a function that returns another function as its return value. Remember, in R functions are treated like any other object and so this is perfectly valid. What is the value of n here? Well, its value is taken from the environment where the function was defined.

When I defined the cube function it was when I called make. We can explore the environment of a function to see what objects are there and their values. Dynamic Scoping We can use the following example to demonstrate the difference between lexical and dynamic scoping rules.

With dynamic scoping, the value of y is looked up in the environment from which the function was called sometimes referred to as the calling environment. In R the calling environment is known as the parent frame. In this case, the value of y would be 2. When a function is defined in the global environment and is subsequently called from the global environment, then the defining environment and the calling environment are the same. This can sometimes give the appearance of dynamic scoping.

Consider this example. Lexical scoping in R has consequences beyond how free variables are looked up. This is because all functions must carry a pointer to their respective defining environments, which could be anywhere. If you do not have such knowledge, feel free to skip this section. Why is any of this information about lexical scoping useful? Optimization routines in R like optim , nlm , and optimize require you to pass a function whose argument is a vector of parameters e.

The book is divided into various parts, making it easy for you to remember and associate with the questions asked in an interview. It covers multiple possible transformations and data filtering techniques in depth. You will be able to create visualizations like graphs and charts using your data. You will also see some examples of how to build complex charts with this data. This book covers the frequently asked interview questions and shares insights on the kind of answers that will help you get this job.

By the end of this book, you will not only crack the interview but will also have a solid command of the concepts of Data Science as well as R programming. Anyone who wants to clear the interview can use it as a last-minute revision guide. Data Science basic questions and terms 2. R programming questions 3. Statistics with excel sheet. Anyone with basic programming and data processing skills can pick this book up to systematically learn the R programming language and crucial techniques.

What You Will Learn Explore the basic functions in R and familiarize yourself with common data structures Work with data in R using basic functions of statistics, data mining, data visualization, root solving, and optimization Get acquainted with R's evaluation model with environments and meta-programming techniques with symbol, call, formula, and expression Get to grips with object-oriented programming in R: including the S3, S4, RC, and R6 systems Access relational databases such as SQLite and non-relational databases such as MongoDB and Redis Get to know high performance computing techniques such as parallel computing and Rcpp Use web scraping techniques to extract information Create RMarkdown, an interactive app with Shiny, DiagramR, interactive charts, ggvis, and more In Detail R is a high-level functional language and one of the must-know tools for data science and statistics.

Powerful but complex, R can be challenging for beginners and those unfamiliar with its unique behaviors. Learning R Programming is the solution - an easy and practical way to learn R and develop a broad and consistent understanding of the language.

Through hands-on examples you'll discover powerful R tools, and R best practices that will give you a deeper understanding of working with data. You'll get to grips with R's data structures and data processing techniques, as. It is a useful addition to the body of work already available to guide project managers of data science projects. It's also a guide for executives and investors to get maximum value from their investment in AI.

Beginners in data science can also get the most out of this book. Is it not surprising to know when data science and AI are in the top trend? If you are looking for a career in data science or looking for leadership, these insights may disturb you. Each day counts. So as your steps. Step up immediately and begin your journey to your dreams of data science and AI.

In addition, the book covers why you shouldn't use recursion when loops are more efficient and how you can get the best of both worlds. Functional programming is a style of programming, like object-oriented programming, but one that focuses on data transformations and calculations rather than objects and state. Where in object-oriented programming you model your programs by describing which states an object can be in and how methods will reveal or modify that state, in functional programming you model programs by describing how functions translate input data to output data.

Functions themselves are considered to be data you can manipulate and much of the strength of functional programming comes from manipulating functions; that is, building more complex functions by combining simpler functions.

What You'll Learn Write functions in R including infix operators and replacement functions Create higher order functions Pass functions to other functions and start using functions as data you can manipulate Use Filer, Map and Reduce functions to express the intent behind code clearly and safely Build new functions from existing functions without necessarily writing any new functions, using point-free programming Create functions that carry data along with them Who This Book Is For Those with at least some experience with programming in R.

In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis.