R is a programming language and environment for statistical computation, data analysis, and scientific research. It is amongst the most widely used languages for retrieving, cleaning, analysing, visualising, and presenting data by statisticians, data analysts, researchers, and marketers. It has gained popularity in recent years due to its expressive syntax and user-friendly interface. As a result, the course is also in high demand among several universities in Australia and worldwide. But the most difficult task comes in when it comes to making assignments and submitting them altogether! The language might be new for you or you might be learning; hence, asking for R code assignment help nothing but a good idea to achieve HD grades! You can call or text us anytime of the day!
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As per our R code assignment help experts:
R is available for free download underneath the terms of the GNU General Public License. You can peek at the source code to discover what's going on behind the scenes. Furthermore, most R packages are distributed under the same licence, allowing you to use them in business projects without consulting a lawyer.
Every year, the IEEE produces a list of the most popular programming languages. In 2016, R came in 5th rank, which is a 1-step jump from its earlier ranking no.6 in 2015. It's a significant deal for a domain-specific language like In terms of popularity, R is expected to exceed a general-purpose language like C#. This shows not only R's growing popularity as a programming language, but also R's expanding appeal in domains outside than programming like Data Science and Machine Learning.
R distributions are available for all main platforms, including Mac, Linux, and Windows. R code written on one platform can be readily moved to another with no problems. In today's computing environment, cross-platform interoperability is a must-have feature; in fact, Microsoft is making its renowned .NET platform accessible on every platform after discovering the benefits of technology that runs on all systems.
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The “sexiest job of the twenty-first century,” according to Harvard Business Review, is data scientist. It was named the "best job of the year" by Glassdoor in 2016. Data science is a discipline that has no other way to go but up, with the introduction of IoT devices providing terabytes and terabytes of data that can be used to make better decisions. Simply described, a data scientist is a statistician who also knows how to programme computers. Data scientists may collect data in real time, do statistical and predictive analysis, produce visualisations, and convey actionable insights to stakeholders using programming languages like R.
Among statisticians, R is the most widely used programming language. In reality, it was originally designed by statisticians for statisticians. It has an extensive package repository, with over 9100 packages covering all statistical operations. R's expressive syntax enables researchers – including those without a background in computer science – to swiftly import, clean, and analyse data from a variety of sources.
R also offers charting features, allowing you to plot data and generate visually appealing visualisations from any dataset.
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As per our R code assignment help experts, R isn't the only statistical computing and graphics language available. The following are some of the most popular R programming alternatives:
Python— Python is a high-level, object-oriented programming language with a straightforward and easy-to-understand syntax. Python is a famous programming language among data scientists and researchers. The majority of R packages have Python comparable libraries. While statisticians and mathematicians favour R, professional programmers prefer to implement new algorithms in a programming language they are already familiar with.
SAS— While R is the clear champion in academia and research, SAS is the most widely used commercial analytics software. However, R and Python are gaining traction in the sector, and businesses are attempting to migrate to open-source technology. It will be interesting to see if SAS maintains its supremacy or if R/Python takes over.
SPSS— PSS is yet another prominent statistical tool. It is used most extensively in the social sciences and is regarded as the simplest to understand among enterprise statistical techniques. SPSS is popular with non-statisticians since it is similar to Excel, making it simple to use for people who are already familiar with it. SPSS has the same drawback as SAS in terms of cost. IBM purchased SPSS in 2009 for a reported $1.2 billion.
With unmatched data exploration and experimentation packages, R programming is better suited for statistical learning. Python is a better option for machine learning and large-scale applications, such as data processing in web apps.
The first rule of package development is that all R code should be placed in the R/ directory. You'll learn how to organise your functions into files, keep a consistent style, and recognise the tougher requirements for functions in a package in this chapter (versus in a script).
You can open the R console by entering 'R' in the terminal and pressing Return/Enter if R is correctly installed. The R console with the default ">" prompt is the first thing you'll see when you start R. You can type commands right into the prompt and push return to run them.
If you've previously mastered an object-oriented programming language like Java or C++, it'll be easier to master than R. Python is also easier to develop large-scale, maintainable, and robust code than R because it is an object-oriented programming language.
The backslash "\" is a special character in Python strings, sometimes known as the "escape" character. The characters "\t," "\n," and "\r" are used to indicate various whitespace characters: "\t" is a tab, "\n" is a newline, and "\r" is a carriage return.
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