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R Programming training Pune

R as a programming Language


R is a computer programming language which is particularly well suited to handling and sorting the large datasets associated with Big Data projects.
R Programming training Pune
R is a programming language designed for data analysis and machine learning. It’s an interpreted language in the sense that it executes the commands directly, so it is more user friendly than other programming languages.
Although its learning curve is steeper compared to some commercial software, R is easier to learn compared to other programming languages with the help of our experts at R Programming Training available at Pune.R Programming Training Pune
If you need proof, look no further than R’s growth, which is reflected in a number of independent lists; it has bounced around in the top 20 languages in the Tiobe Index of Programming Language Popularity for the last several years. In 2015, IEEE listed R at 6 in the top 10 languages of 2015. Additionally, as the amount of data-intensive work increases, the demand for tools like R for processing, data-mining and visualization will also increase.R Programming Training Pune
Almost without reservation, I recommend that you learn R as your first “data science programming language.” While there are exceptions (e.g. if you have a specific project need), I think that R is the best choice when you’re getting started with the help of our experts at R Programming Training available at Pune.

R Job Prospects


Technology is fun, sure, but most of us who enjoy it also do it for a living. Fortunately, R is not only a pleasure to use, but its demand in business often equates to higher salaries for its practitioners. The Dice Technology Salary Survey conducted last year ranked R as a highest-paying skill. The most recent O’Reilly Data Science Salary Survey also includes R among the skills used by the highest paid data scientists.
R programming training Pune
R is Popular with Employers
In two recent studies including one of over 17,000 technology professionals, R was the highest paid technical skill with an average salary of 115,531 (Read more on this here).
R job prospects are rapidly increasing comparing R against a host of alternative software.  The search for R is complicated by the difficulty of its ambiguous name.  Graph (borrowed from  Robert A. Meuchen’s blog).
But why do we care how popular R is?  Programming languages (which all statistical software worth their salt have) are highly dependent upon their user base in order to develop.  How fast they develop, how powerful they are, and how long they expect to be supported is entirely based on how widely they are used with the help of our experts at R Programming Training available at Pune.
R is fun

And, of course, R is FUN! Initially, I was drawn to R for its ability to generate charts and plots in very few lines of code.; tasks that would require several hundred lines of code in another language could be accomplished in only a few lines. R Programming training PuneWhile it’s considered quirky when you compare it with many popular languages, it includes powerful features specifically geared toward data analysis. For example, if you run the following snippet at the R prompt:
R is worth learning for these reasons and more. Its growth and maturity have led to widespread adoption and many resources for learning.
And now with Microsoft stepping up and including R in more of its offerings, you can expect to hear more about R in the months and years to come.
Programmers are usually attracted to learn R programming because of its extraordinary capabilities to generate plots and charts with just few lines of code which would otherwise require several 100’s of lines of code in any other language. R language does have a steep learning curve but when programmers start learning R they really enjoy the powerful features it provides which are geared towards complex data analysis.
Who uses R?

R is in heavy use at several of the best companies who are hiring data scientists. Google and Facebook – who I consider to be two of the best companies to work for in our modern economy – both have data scientists using R.
“R is also the tool of choice for data scientists at Microsoft, who apply machine learning to data from Bing, Azure, Office, and the Sales, Marketing and Finance departments.”
Beyond tech giants like Google, Facebook, and Microsoft, R is widely in use at a wide range of companies including Bank of America, Ford, TechCrunch, Uber, and Trulia.
R isn’t just a tool for industry. It is also very popular among academic scientists and researchers, a fact attested to in a recent profile of the R programming language in the prestigious journal Nature.
R’s popularity in academia is important because that creates a pool of talent that feeds industry.R Programming training Pune
With the growing popularity and functionality of R language, it is going stay for long as organizations like Google, Pfizer, Bank of America, Merck, Oracle widely adopt its usage for complex business analytics. A powerful community, strong partners and a promise of providing easy-to-integrate solutions, R language is capitalizing big data analytics revolution.
R in business
R originated as an open-source version of the S programming language in the 90s. Since then, it has gained the support of a number of companies, most notably RStudio and Revolution Analytics which created tools, packages, and services related to the language. But it isn’t limited to these more specialized companies; R also has support from large companies that power some of the largest relational databases in the world. Oracle, for one, has incorporated R into its offerings . Earlier this year Microsoft acquired Revolution Analytics and is including the language in SQLServer 2016.  SQLServer administrators and .NET developers now have R at their fingertips, installed with their standard platform tools.
R in higher education
Here’s a fun fact: R originated in academia. Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand created it, and it’s been widely adopted in graduate programs that include intensive statistical study. R Programming PuneR has also been used in Massive open online courses (MOOCs) such as the Coursera Data Science Program . Folks taking graduate studies that involve crunching data are bound to encounter R, and like many other technologies, its introduction in schools leads naturally to its wider adoption in industry. R’s presence in higher education is confirmation of the demand for these skills in business settings.
Top 5 features you’ll want to know about
As soon as you become more familiar with R, you will realize that there are a wide variety of things that you can do with it. This section will teach you all about the most commonly performed tasks and the most commonly used features in R. The code shown in these sections will be slightly longer and more complex compared to the one shown previously, as sometimes comments will also be included in the code. Remember that comments in R come after the symbol #.
Data input and output
As you have already noticed, most of the potential of R is in data analysis and data manipulation. When working with data, you will sometimes need to save data to a file and/or read it from files. In this section, you will find an introduction on writing and reading data files. Additional information on this subject can also be found in the R Data Import/Export manual included in the software.
Writing data to a file
Most often, you will have to write datasets to a file, and the important functions that allow you to do that are write.table() and write.csv(). The first one is a more general function that can be used to create files in different formats, such as .txt. The second one is basically a call to the first one with some specific arguments for the .csv files. Such a format is particularly useful since it can be easily read and created with software such as Excel. Remember that the files that you create will be located in the current working directory. In order to check in which folder the working directory is, or to change the folder of a working directory, you can check the section Quick Start. Let’s create some examples using such functions and the dataset Orange, which we have already used in the previous example.
The basic call to the function write.table() would be:
> write.table(Orange,”orange.txt”)
Reading data from a file
The most convenient way of reading data in R is using the function read.table(). This function requires the data to be in the ASCII format, which will be created by any plain text editor. The way of using this function is very similar to write.table(), as explained previously. In R, the result of the function read.table() is a data frame, in which R expects to find the same type of data in each column (for example, character or numeric). Each element in each row is expected to be separated by some form of separator or a blank space. The first line of the file may contain a header giving the names of the variables (highly recommended). Let’s assume that you created a datafile .txt of the dataset Orange using the default separator (you can use the code reported in the previous section Writing data to file.)
You would be able to import this dataset as a data frame within R using the following code:
> read.table(file=”orange.txt”, header=TRUE)
Flow control (for, if…else, while)
Flow control expressions are programming constructions that allow the conditional execution of a portion of code. In this section, you will find a description of the main flow-control expressions in R.
The if…else loop
The R language is a true programming language that allows conditional execution and programming loops as well. It is, for instance, often useful to force the execution of some piece of code to depend on a certain condition. This can be done using the if..else expression, which follows the following structure:
if (logical.expression) {
expression.1

} else {
expression.2
}
expression.1 will be executed if logical.expression is TRUE and expression.2 is FALSE. In this construction, the else statement may also be omitted; in this case, if logical.expression is FALSE, nothing will be executed.
Braces { } are used to group together one or more expressions. If there is only one expression, the braces are optional. Several if statements may also be nested, creating complex conditional code. Since the else statement is optional, you will get an error if the else statement is not on the same line of the brace defining the end of the if statement, since R will assume that the code was complete with the first statement.
Consider the following simple example:
> x <- 1
> if(x %% 2 == 0) print(“x is even”) else print(“x is odd”)
[1] “x is odd”
> x <- 2
> if(x %% 2 == 0) print(“x is even”) else print(“x is odd”)
[1] “x is even”
In this simple example, we assigned a value to a variable and then we checked if the value is even or odd. This is done using the modulus operator %%. So if the modulus is 0, the code will print on the console the message “x is even”, otherwise it will print “x is odd”. You will also notice the use of the operator ==(equal to), and how the braces can be omitted with statements containing only one command.
In R, there is also an alternative (more concise) option available for the if…elsestatement, the ifelse() function. This function has the general form ifelse(test, yes, no), where test is the logical expression that is evaluated as yes and is executed if test is TRUE, and as no if otherwise. The previous example would look like the following if coded using this alternative function:
> x<-1
> ifelse(x%%2==0, print(“x is even”), print(“x is odd”))
The for loop
The for loop is one of the methods that can be used to repeat a certain portion of code. The underlying idea is that you request that an index ,i, takes on a sequence of values, and that one or more lines of commands are executed many times as there are different values of i. An important aspect of such looping is that the variable i will take a different value at each loop, and that value is usually used in the code. The general syntax of the for loop is the following, where i is a simple variable and myVector is a vector:
for (i in myVector) {
expression.1

}
When executed, the for command executes the group of expressions within the braces { }, once for each element of the vector. The variable i will take the value of each element of the vector myVector. You can find a very simple example of a for loop in the following code, in which the for construct is used to print the square root of each element of a vector:
> for (i in c(3,4,9,5)) print(sqrt(i))
[1] 1.732051
[1] 2
[1] 3
[1] 2.236068
The while loop
In some situations, we do not know beforehand how many times we will need to go around a loop, so each time we go around the loop, we will have to check some condition to see if we are done yet. In these situations, we use a whileloop, which has the following general syntax:
while (logical.expression) {
expression.1

}
When while is executed, the value of the value of logical.expression is evaluated first. If it is TRUE then the group of expressions in braces { } is executed. After that, the execution comes back to the beginning of the loop; if logical.expression is still TRUE, the grouped expressions are executed again, and so on. Clearly, for the loop to stop, the value of logical.expression must eventually become FALSE. Achieving logical.expression usually depends on a variable that is altered within the grouped expressions. Remember that the key point is if you want to use whilefor avoiding infinite loops, it is advised to set up an indicator variable and change its value within each iteration. The while loop is more fundamental than the forloop, as we can always rewrite a for loop as a while loop. Following is a simple example of a while loop that will print on the console the numbers from 1 to 10.
You can notice how the variable x is increased at each run of the loop.
> x<-1
> while(x<10){
+   print(x)
+   x <- x+1 # Counter which will increase at each run of the loop
+ }
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
A slightly more complex example of the while loop is the generation of the Fibonacci series. In this series, each number is equal to the sum of the two preceding numbers, so you will have 1,1,2,3,5,8, and so on. In the following example, we will define a variable, n, representing the number of elements of the Fibonacci series that we intend to obtain, and two variables, a and b, which will be used to start the generation of the series:
> a<-1
> b<-0
> n<-10
> while(n>0){
+          c <- a
+          a <- a+b
+          b <- c
+          n<-n-1
+          print(b)
+ }
[1] 1
[1] 1
[1] 2
[1] 3
[1] 5
[1] 8
[1] 13
[1] 21
[1] 34
[1] 55
Within the loop, we decrease the value of n by 1 at each run, bringing the loop to an end as soon as we have n elements of the series. In the loop we also need to define a new variable, c, the only function of which is to avoid losing the value of the variable a. In fact, when we replace a by a+b on line six, we lose the original value of a. If we had not stored this value in c, we could not have set the new value of b to the old value of a on line seven.
R Data Structures – The Core of R
R has a variety of data structures. Here, we will sketch some of the most frequently used structures to give you an overview of R before we dive into the details. This way, you can at least get started with some meaningful examples, even if the full story behind them must wait.
Vectors, the R Workhorse
The vector type is really the heart of R. It’s hard to imagine R code, or even an interactive R session, that doesn’t involve vectors.
The elements of a vector must all have the same mode, or data type. You can have a vector consisting of three character strings (of mode character) or three integer elements (of mode integer), but not a vector with one integer element and two character string elements.
Scalars
Scalars, or individual numbers, do not really exist in R. As mentioned earlier, what appear to be individual numbers are actually one-element vectors. Consider the following:
> x <- 8
> x
[1] 8
Recall that the [1] here signifies that the following row of numbers begins with element 1 of a vector—in this case, x[1]. So you can see that R was indeed treating x as a vector, albeit a vector with just one element.
Data Frames
A typical data set contains data of different modes. In an employee data set, for example, we might have character string data, such as employee names, and numeric data, such as salaries. So, although a data set of (say) 50 employees with 4 variables per worker has the look and feel of a 50-by-4 matrix, it does not qualify as such in R, because it mixes types.
Instead of a matrix, we use a data frame. A data frame in R is a list, with each component of the list being a vector corresponding to a column in our “matrix” of data. Indeed, you can create data frames in just this way:
> d <- data.frame(list(kids=c(“Jack”,”Jill”),ages=c(12,10)))
> d
kids ages
1 Jack  12
2 Jill  10
> d$ages
[1] 12 10
Typically, though, data frames are created by reading in a data set from a file or database.
Classes
R is an object-oriented language. Objects are instances of classes. Classes are a bit more abstract than the data types you’ve met so far. Here, we’ll look briefly at the concept using R’s S3 classes. (The name stems from their use in the old S language, version 3, which was the inspiration for R.) Most of R is based on these classes, and they are exceedingly simple. Their instances are simply R lists but with an extra attribute: the class name.
For example, we noted earlier that the (nongraphical) output of the hist() histogram function is a list with various components, such as break and count components. There was also an attribute, which specified the class of the list, namely histogram.
> print(hn)
$breaks
[1]  400  500  600  700  800  900 1000 1100 1200 1300 1400$counts
[1]  1  0  5 20 25 19 12 11  6  1


attr(,”class”)
[1] “histogram”
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