It seems that Python is enough for everything. You can write a script for the server, analyze the data, and train a neural network. Moreover, it has a lot of libraries for statistics and data analysis — you can use any of them.
However, there is another language — R — for analytics and working with statistics. Many students have to deal with this programming language when studying statistics. Our crazy life rhythm often makes us adapt to circumstances and study, do home tasks, or even work from our phones. If you have ever wondered, “how can I do my r homework on a smartphone” keep reading.
What is R?
This is a programming language used by statisticians and data collectors for statistical calculations and graphics. The first version of the R language appeared in 1993, two years later than Python. At the time, Python wasn’t yet as popular and didn’t have as many libraries for data analysis as it does now. So scientists in the Department of Statistics at Oakland University created a language for their internal tasks. And because their names were Ross and Robert, they named the language after the first letter in their name, R.
R was originally developed as an internal tool in the faculty to solve their statistical problems. But at the time, it was a good thing for scientists to share their work with everyone, so they opened up the language’s source code so that everyone could improve it or add something useful. Since then, the language has grown from a faculty project to a globally popular statistical tool.
Characteristics of R as a programming language
Since this language was invented for scientific purposes, the authors didn’t try to make it intuitive. They assumed that it would be used by people familiar with mathematical analysis, statistical methods, and probability variation. That is why R may seem like a very complicated language, although it is very simple and logical on the inside.
What is R used for?
The main use of R is to analyze data and draw conclusions from it:
- visualizing data in any way;
- collecting and analyzing data from different sources;
- working with statistics, finding anomalies in data;
- searching for patterns and outliers in the data;
- testing and confirmation of hypotheses.
A separate direction in R is machine learning and neural networks. Since the R language was originally designed to process huge amounts of data, it is easy to organize a deep learning model or make a new neural network.
What can you do with R
- Process, cleanse, and transform data for research. For example, you want to see how many students, on average, attended the library each winter and autumn month. R allows you to exclude spring and summer and group them by month for further calculations.
- You can transform your results into a web app. It will be fully interactive, offering filters, graphs, and even a data sorter. You can send it to your professor or publish it as a part of your paper. This is how they track the incidence of Coronavirus worldwide (the code is open and available on GitHub).
- Run statistical tests. Suppose you want to know if the IQ level of two genders differs. A t-test can help you with this. The test will display the statistical difference between the received data, if any.
- You can conduct an exploratory analysis. As many statistical methods need distribution in raw data, you must check it for normality. What is a normal distribution? It’s when the majority of data is grouped around the mean value. The rest of the values are significantly smaller. You can see this distribution in life: there are more people with average height than those who are tall or short. R offers instruments to check normality with graphs and tests.
- Mix various tables’ formats. You can finally use various table formats and unite two of them into one document to analyze the data.
- You can present your data in interactive charts, adjusting all the parameters (axis values, etc.).
- Conduct regression analysis and create regression models. This analysis helps distinguish the relationship between the dependent and independent variables. Let’s say you want to figure out why some beauty studios on the same street have more sales than others. The number of sales will be the dependent variable. The independent variables would include the social status and age of neighborhood residents and the pricelist of each studio for the same procedures. This way, you can find out which of these factors affects store sales more than others.
Pros of R
- Unlimited set of functions for data analysis thanks to the connection of libraries.
- The ability to work with huge tables and databases that programs can’t handle.
- Advanced interface customization: graphical user interface or command line interface.
- Completely free ecosystem — components are distributed for free under the GNU license.
- Available for most operating systems: Windows, macOS, FreeBSD, Solaris, various versions of Unix, and Linux.
- Rich visualization capabilities: you can create applications, build graphs of different types, including interactive ones, as well as edit their elements.
- Lots of information and an active community: a blog, discussions of R and RStudio, lessons, and conferences.
- Extensive and clear documentation: there are descriptions of all libraries and examples of use.
Cons of R
- A person without programming experience and knowledge of the basics of statistics can find it challenging.
- The narrow scope: it is ideal for data analysis, but it is not suitable for software development. But that is its strength. A true UNIX-way and a godsend for scientists, journalists, data scientists, analysts — anyone who wants to work with data.
How to deal with R homework on your phone?
We have already mentioned applications with libraries that your can use for working with R are available on all OS. But what about smartphones? There is a way of using RStudion on your phone, not through a special application but through its open-source server. You can get it via any web browser. Thus, you just need to run the RStudion server on your computer or laptop and access it on your phone. This is a great and simple way to work on your R home assignments at any location and time.