Have you ever wondered who is taking care of the massive data traffic on the internet and who is handling the huge datasets of the companies?
Well if your answer is Data Scientist’s than then you are absolutely right my friend, but the question is how do they do it !!! Does this idea fascinate you, handling of an enormous amount of data could not be a layman’s job, well if this question fascinates you then this is a must-read blog for you? Well, Data scientist handles the data basically using two languages which are python and R respectively, both of these languages are largely supported by inbuilt libraries and extended features which provide a good hand in managing of huge chunks of data. Whereas python puts more emphasis on the deep learning and neural network part and R language have huge support to data science branch because of its statistical packages.
Table of Contents
Introduction to R
R is a language and environment for statistical computing and graphics. It is a GNU project( GNU is composed wholly of free software, most of which is licensed under the GNU Project’s own General Public License.) which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R, defining of functions and variables in R is similar to that in Python.
The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. R allows integration with the procedures written in the C, C++, .NET, Python or FORTRAN languages for efficiency.
Why R when we have python and other languages?
R provides excellent visualisation features and statistical functions, which are essential to explore the data before submitting it to any automated learning, as well as assessing the results of the learning algorithm. Many R packages for machine learning are available off the shelf in its inbuilt libraries and many modern methods in statistical scope are implemented in R as part of their development.
Working and defining functions and variables is really simple in the R language, the same code which takes about 4 lines in any other language can be written in one line in R language. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology and R provides an Open Source route to participation in that activity which means you can build a library or a module for yourself for your own self-interest.
In contrast to other programming languages like C and java in R, the variables are not declared as some data type. The variables are assigned with R-Objects and the data type of the R-object becomes the data type of the variable. There are many types of R-objects.
The frequently used ones are as follows:
Working with Data Frames in R
Load data, read files, save data
Working with data frames is really simple and reliable in R as compared to other languages. The read_delim function, from the readr library, offers a lot of tools to work with different types of files.
Data Frames are created using the data.frame() function.
# Create the data frame.BMI <- data.frame(gender = c(“Male”, “Male”,”Female”), height = c(152, 171.5, 165), weight = c(81,93, 78), Age = c(42,38,26))print(BMI)
Here BMI is the data frame and columns are gender, height, weight, age the simple intuition of syntax of R is impressive and this makes it unique.
For example reading of file:
Read_dlim to read a dlim file
read_delim(file, delim, quote = “\””, escape_backslash = FALSE, escape_double = TRUE, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c(“”, “NA”), quoted_na = TRUE, comment = “”, trim_ws = FALSE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), progress = show_progress(), skip_empty_rows = TRUE)
Read_csv to read a csv/excel file
read_csv(file, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c(“”, “NA”), quoted_na = TRUE, quote = “\””, comment = “”, trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), progress = show_progress(), skip_empty_rows = TRUE)
Data Frames are created using the data.frame() function.
# Create the data frame.BMI <- data.frame( gender = c(“Male”, “Male”,”Female”), height = c(152, 171.5, 165), weight = c(81,93, 78), Age = c(42,38,26))print(BMI)
I know by looking at these functions you must be thinking that’s a lot of code and I shouldn’t use R well roll back guys it’s just a documentation example most of the things are default cases and are not even passed trust me I am a developer from three years and I have found R as an excellent language for the data handling purpose. After reading my full article if you choose R than you are going to thank me later.
Exploring the R Environment:
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes the following:
- An effective data handling and storage facility,
- A suite of operators for calculations on arrays, in particular matrices,
- A large, coherent, integrated collection of intermediate tools for data analysis,
- graphical facilities for data analysis and display either on-screen or on hardcopy, and
- a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as it can be easily concatenated with other software’s like SQL, Tableau, Domo, Good Data etc.
In R you can make a environment specific project ,so you won’t need to install the libraries and repositories to your whole system in spite things could be project specific this will enhance the performance of your system too.
Features of R
- R is a well-developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities.
- R has an effective data handling and storage facility,
- R provides a suite of operators for calculations on arrays, lists, vectors and matrices.
- R has a vast array of packages. With over 10,000 packages in the CRAN repository, the number is constantly growing. These packages appeal to all the areas of industry
- R comes in handy to those who want to gain a better understanding of the underlying details and build something truly innovative.
- R provides a large, coherent and integrated collection of tools for data analysis.
- R provides graphical facilities for data analysis and display either directly at the computer or printing at the papers.
R is the world’s most widely used statistics programming language. It’s the # 1 choice of data scientists and supported by a vibrant and talented community of contributors. R is taught in universities and deployed in mission-critical business applications.
Well, and that’s it for the ultimate introduction to R if you have a heartful desire to work in the data science field than R is something really made for you the enormous powerful language can really shape your future in the data science industry.
I hope this was a straightforward introduction to R, I believe progress is made through manipulation and experimentation. Hopefully, ou liked it and you learned something! Please share your first experiences with R in the comments and also if you liked this blog please share with gratitude and spread the flame of knowledge.
To read more about Machine Learning, click here.
By Rohit Chauhan
