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Learn programming in R: courses


Learn programming in R: courses

R is a programming environment and language commonly used in statistics, data analysis, and scientific research.

R is a programming environment and language commonly used in statistics, data analysis, and scientific research. It is one of the best-loved programming languages in the world today. Its powerful features make it an excellent choice for data scientists, statisticians, and anyone who needs to process large amounts of data quickly and easily.

The R language is the main competitor to Python for those involved in statistics and data analysis. It is used in social and economic sciences to search for causal relationships, compare samples, create visual reports and graphs.

The language was developed by scientists from the Department of Statistics at the University of Auckland. At first it was an internal tool, but then it was made available to everyone - it turned out to be very successful.

This is an important point: R was developed by statisticians for statisticians - it already has popular statistical tests, data analysis methods, convenient tools for plotting. Not all popular general-purpose languages ​​have such features.

And the specialized language R is confidently winning its place under the sun: from the 18th place in the TIOBE rating in 2016, it rose to the 8th line in January 2021. You can install the interpreter and working environment on any modern operating system - MacOS, Linux, Windows.

Programming in R

R is open-source and free

R is free to download as it is licensed under the terms of the GNU General Public License. You can look at the source to see what’s happening under the hood. There are many R packages available under the same license, so you can use them, even in commercial applications without having to call your lawyer.

R is getting more popular

IEEE publishes a list each year of the most popular programming language. R was ranked fifth in 2016, up from sixth in 2015. It is an important milestone for a domain-specific programming language like R to be rated higher than a general-purpose programming language like C. Not only does this show the growing popularity of R as a programming language; it also demonstrates the increasing importance of data science and machine learning, two fields where R is frequently used.

R runs on all platforms

You can find distributions for all popular platforms - Windows, Linux and Mac OS X. Code that you write on one system can easily be ported to others without any issues. Cross platform interoperability is an important aspect of modern computing - even Microsoft realizes the benefits of technology that works on all systems.

Learning R will help you to get a job in Data Science

According to the 2015 O'Reilly survey, data science salaries range from $80k to $150k depending on location. In the United States, the average salary is $120k. Of course, knowing how to code in R won't guarantee you a job right away, but it does give you an edge over others who may not know how to program.

Learn R - Data science programming language

R is a programming tool used for statistics, data visualization, and data analysis. It is an open source project released under the GNU General Public Licence. R is written in the C programming language and is cross platform compatible. R is widely used in academia, industry, government, and education.

Statistical programming in R

R is a programming language and environment for statistical computation and graphics. It is an open source software project released under the GNU General Public License. R is based on S, another open source programming language and environment developed by John Chambers and colleagues at Bell Labs. R differs from S in that it is primarily intended to provide a complete system for statistical computing and graphics rather than being just a language.

R provides a wide range of statistical methods, including linear regression, logistic regression, Poisson regression, survival analysis, generalized additive models, multivariate adaptive regression splines, generalized boosted models, neural networks, support vector machines, k-nearest neighbors, decision trees, random forests, gradient boosting, and others. These methods can be applied to both continuous and discrete data types. R also includes tools for exploratory data analysis, visualization, and data mining.

One of R‘s strengths is the ease of producing publication quality plots, including mathematical formulas where needed. Great care was taken over the defaults for minor design choices, but the user retains complete control.

R programming course

Is R programming easy to learn?

Many researchers have learned R as their first language because they wanted to solve their data analysis problems. That's the power of the R Programming Language, it is simple enough for beginners to learn as they go. All you need are data and a clear intent for drawing conclusions based on analysis on that dataset.

In fact, R is build on top of the language SAS that was originally intended as an educational tool for students to learn how to program while working with data. However, programmers who come from a Python, Java or PHP background may find R quirky and confusing.

The syntax that R utilizes is a bit different from the other popular programming languages. While R does have all of the capabilities of a programming languages, you won't find yourself writing a lot if if statements or loops while coding in the R language. You can also manipulate data in bulk through the use of vectors, lists, frames and data tables.

Applications of R Programming

R for Data Science

Data scientists are statisticians who also happen to know how to write code. They use R to analyze data and build models that can predict future outcomes. The best part? You don’t need any formal training to get started. Just download the software, open your data, and start coding!

R for Statistical programming and computing

R is an open source software environment for statistical computing and graphics. It provides a wide range of tools for data manipulation, visualization and statistical analysis. R is used by statisticians, mathematicians, physicists, economists, biologists, chemists, social scientists, and others to perform exploratory data analysis (EDA), predictive modeling, and data mining. R is widely used in industry because it is easy to install, fast to run, and powerful for data analysis.

R programming for Machine Learning

I think that R is a great tool for anyone who wants to get started with data science. R can be used to perform any kind of statistical analysis, including linear regression, logistic regression, k-means clustering, and much more. R is also very easy to understand if you know how to code in another language. R is not just for statisticians; it is also used in business, economics, engineering, medicine, and many other areas. R is an open source software package developed by Ross Ihaka and Robert Gentleman at the University of Auckland. It is freely available under the GNU General Public License (GPL).

Coursera R programming

Open course description

In this course you'll learn how to program in R and how to use R for effective data analysis. We'll start with installing and configuring software necessary for a statistical computing environment and then we'll cover basic programming language concepts as they're implemented in a high- level statistical language. The course will cover practical issues in statistical computing including programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and documenting R code. Topics in statistical analysis will provide working examples and exercises.

WHAT YOU WILL LEARN: You will leave with an understanding of how to configure statistical programming software, how to make use of R loop functions, and how to collect detailed information using R profilers.

Week 1: Background, Getting Started, and Nuts & Bolts

This week covers the essentials to get you started in R. The background materials contain information about course mechanics and videos on installing R. Week 1 videos cover the historical background of R and S, explain the basic data types in the language, and show how to read and write data. I recommend that readers watch the videos in the order they are presented, but watching them out of order won't spoil the story.

Week 2: Programming with R

This week, we have taken the gloves off, and covered some very important topics including loops, conditionals, and functions. We also introduced the first programming assignment for this course, which is due by the end of the week!

Week 3: Loop Functions and Debugging

We have now entered Week 3 of R Programming, which is also half way through. The lectures this week include loops and debugging tools in R. Both of these aspects of R make R very useful for both interactive work as well as writing longer code, and thus they are commonly used in practise.

Week 4: Simulation & Profiling

This week covers how you can simulate data in R, so you can do simulation studies. You'll learn about the profiler in R, which helps you understand where your code is spending the most time. You'll also learn about the str function, which is one of my favorite functions in R.

Codecademy R

Open course description

In this course, we'll start off by learning about basic programming concepts in R. We'll then move onto organizing, modifying and cleaning data frames. Data frames are a very useful data structure in R because they allow us to store multiple variables in one place. Next, we'll learn how to create data visualization to showcase insights in our data. Finally, we'll wrap up by learning how to perform statistical tests and hypothesis testing to become an expert in data analysis.

Codecademy R course syllabus:

  • Learn R Programming: Introduction
  • Learn R Programming: Data Frames
  • Learn R Programming: Data Frames
  • Learn R Programming: Fundamentals of Data Visualization with ggplot2
  • Learn R Programming: Aggregates
  • Learn R Programming: Joining Tables
  • Learn R Programming: Mean, Median, and Mode
  • Learn R Programming: Variance and Standard Deviation
  • Learn R Programming: Quartiles, Quantiles, and Interquartile Range
  • Learn R Programming: Hypothesis Testing

R for Data Science book

This book will teach readers how to do data science in R. You'll learn how to get your dataset into R, how to tidy it, transform it, visualize it, and model it. In addition, you'll learn how to work with data frames, how to manipulate them, and how to make them interactive. You'll learn how R can help you clean data, how to write code that's easy to read, and how to create reproducible results. Finally, you'll learn how R can be used to explore data, how to create beautiful visualizations, and how to share your findings.

The R Programming language environment

The term ‘environment’ is intended to characterize it a fully planned and coherent systems, rather than an incremental addition of very specific and infelxible tools, as is often the case with other data analytics software. R, like S, was designed around a true computer langauge, and it allows users tadditional functionality by defining new functions, much of the system is itself writen in the R dialect of the S programming language, which makes it easy fo users to follow the algorithimic choices made.

For computational intensive tasks, C, C ++ and Fortran code can blinked and called at runtime. Advanced users can write ccode to manipulate R objects directly, and they can also create custom classes to represent complex data structures.

The term ‘environment’ is intended to characterize it not as a fully planned and cohesive system, but as an incremental accretion of various specific and inflexible toolkits, as is often the case with other data analytics software. Many users think of R not as a statistics package, but as a general purpose programming language. We prefer to think about it as an environment within the context of which statistical techniques are implemented; R can easily be extended via packages.

There are approximately eight packages provided with the R distribution and countless more are available through the Comprehensive R Archive Network (CRAN), covering a very broad range of modern statistics. R has its own LaTex-like documentation format, used to provide comprehensive documentation, both online in a variety of formats and in hard copy.

R programming: what's under the hood

R is an interpreted object-oriented programming language. What does it mean? Functions or tables for it are objects that belong to a certain class (data type), and the finished program is executed immediately - line by line. You don't need to compile the code into an executable file before running it.

The syntax of the R language is simple and includes a minimal set of primitive data types: character, numeric, boolean, and complex. Primitive types are combined into more complex ones. For example, the vector type is, in fact, a list of several objects (numbers, strings, and others). Numeric variables can also take special values: NaN (not a number - not a number), Inf (infinity - infinity) and NA (not available - not available).

The most popular command in R is to read a file, because you have to constantly open and explore datasets. Here's what it looks like:

data <- read.csv("input.csv", sep = ',')

Here data is a variable where the file will be saved, <— is an assignment operator, read.csv is a function for reading .csv files, and the sep (comma) attribute is the type of separator between data in the source file. It is necessary for the table to display correctly.

In addition to the command line interface, there are graphical user interfaces and interactive tools for R: they make work easier and more enjoyable, are available for free, and are distributed under the free GNU GPL.

What can be done with R programming

  • Process, clean and transform data for research. For example, you want to see how many users downloaded your mobile app on average each summer and fall month. R allows you to exclude winter and autumn from the graph and group them by month for further calculations.
  • Conduct statistical tests. Let's say you want to know if the average life expectancy of men and women differs. To do this, you can run a t-test - its results will show whether there are statistically significant differences between the data.
  • Perform exploratory analysis. The data must be checked for normality, because many statistical methods (for example, the same t-test) require a normal distribution in the sources. The normal distribution assumes that most of the data clusters around the mean, and there are much fewer other values. Such a distribution is often found in life: people of average height in the world are the most, and tall and short are few. R has tools for checking normality with graphs and tests.
  • Work with spreadsheets of different formats. This feature is useful for analysts: for example, to combine data from several .csv and .xlsx tables and process them as one file.
  • Draw an interactive graph and adjust its parameters - values ​​along the axes and the like.
  • Create an interactive application. The result is a nice looking web page with a graph, filters and data sorting. It can be sent to colleagues or published as part of an article. This is how they track the incidence of coronavirus around the world (the code is open and available on GitHub).
  • Analyze regression models. Regression analysis is a technique that allows you to identify the relationship between the dependent and independent variables. For example, an analyst wants to understand why some stores in a chain have higher sales than others. The dependent variable will be the volume of sales, and there are several independent variables - this is the income and age of the residents of the area, the distance from the store to bus stops. As a result, you can find out which of these factors most affects the revenue of stores.

Many of these functions are connected using libraries. There are about 20 of them in the standard package: for example, stat for statistical tests and graphics for simple visualization. Additional libraries can be downloaded from the CRAN server - in 2020 there were more than 16 thousand of them. These include plotly for interactive charts and tidyr for data cleansing, which helps you fill in missing values ​​in columns and make each column correspond to only one variable.

Comparing R to other data science programming languages

R's main competitors are code-free data analysis tools - Excel, Google Sheets, SPSS, Tableau, Power BI, and more. As well as programming languages ​​- Python and Julia.

R, Python and Julia

Python is a universal language, you can create full-fledged applications on it, and R is stronger at working with statistics, so it is loved in academia. Analysts in companies love both languages, although Python is still more popular and the barrier to entry is lower.

Supporters of Julia tipped their favorite to be the killer of Python. But while this is a rather young language - it does not have such a strong community, there are much fewer ready-made recipes, libraries and documentation.

R programming language and ready-made software packages

The strength of R and other programming languages is their flexibility. Programs like Excel and Tableau have limitations: if you lack functions, you will have to wait for the mercy of the developers, and an R specialist can quickly make custom reports and graphs, compare the necessary data. Another plus is that the programming language allows you to fully work with Big Data and build machine learning models.

To summarze

R is not just a programming language, but a whole infrastructure and specialized environment for working with data. Many statistical methods and visualization options are already built into it.

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