Data analytics power pack using R programming
Introducing the programming language "R" used in statistics and data analysis. Along with Python, the R language is a language used in the fields of machine learning and data mining.
In this article, you will understand the features and merits of the R language, job offers, and annual income. If you read it to the end, you will have a deeper understanding of the R language.
Features of R language
The R language is strongly influenced by the S language of statistical analysis, which is the base, and the Scheme of the data processing part, but it also has very unique characteristics, so let's take a look.
In the R language, vector processing can be described flexibly and simply. A vector has a slightly different meaning from a vector in mathematics and is sensuously close to a list.
Of course, mathematical vectors can be included in this vector, but not only that but also sets and time series to data frames with complex data can be put into variables without any special declaration.
You can also nest vector elements in different tables or arrays. This allows you to manage even complex data structures.
Analysis environment suitable for statistics
The R language was developed as a language for statistical analysis. Therefore, it is outstandingly excellent in statistical analysis and data analysis. Since it is OSS (open-source software), anyone can install and use it for free.
Research results such as cutting-edge technologies and algorithms are often published in libraries called packages. One of the major features of the R language is that it has a function to analyze data from the beginning without any effort.
Graph function with excellent visualization
In the R language, you can draw a line graph just by substituting appropriate values for x and y and making a plot (x, y) like Gnuplot of the graph drawing application. In other words, it is easy to handle programming languages as if they were dedicated applications.
There are various graph drawing packages in the R language, and you can easily create complicated graphs by using such packages.
You can easily read the data from an external stats app. For example, it is convenient because there are functions that can read various text files as well as CSV format files that are also used in Excel.
Furthermore, since binary data can be handled directly, it is also possible to read and process images. It can also directly handle IBM statistical analysis packages SPSS and SAS original binaries.
CRAN is an abbreviation of "Comprehensive R language archive network" and refers to a site where you can download various packages in addition to the R language itself.
Can be used from educational sites to practical / research sites
When using various software as well as languages in the educational field, the cost of purchasing and updating is a bottleneck.
In addition, the R language is open source, constantly undergoing new development, and is widely used in practice as a professional tool. Since it has become a common tool in all education, research, and practice, it can be said that the skills acquired in education can be used immediately in practice using the R language.
|Course||Data Analytics Power Pack using R Programming|