Data Science using R Programming

Data Science using R Programming

Today, data is omnipresent and the need to analyze and manage data is vital for businesses to be successful. Data science is a domain that employs scientific theories and techniques to access, collect, classify, clean, study, and evaluate structured or unstructured data. It derives concepts and techniques from other broader disciplines like statistics, computer science, and Mathematics. There is a growing demand for data science due to new and emerging technological advancements.

Why R for Data science?

R is a simple, free, and powerful programming language used for data mining and statistical analysis. It provides a large number of customized libraries for a wide range of computational and data science tasks. It is one of the preferred languages among data scientists due to its simplicity, availability of developer communities, and support from online forums. R is used by large corporations across the globe, namely Twitter, Facebook, Bank of America, The New York Times, and many other enterprises. Numerous online courses are available for various levels of R programmers and data scientists. R programming skill is one of the highly sought competencies in technology companies.

R Resources for Data science

R has ample packages and libraries for data science and assists data scientists in a wide range of techniques such as linear and non-linear modeling, clustering, time-series analysis, and so on. Some of the popular and preferred R data science libraries are:

  • reshape2 is a flexible data restructuring and aggregation package available in R programming. It is used to convert data from wide to long format and vice versa. It helps you in getting the right data format to analyse it. The “melt” function is used to convert wide data to long data format and the “dcast” function is used to convert the data from long to wide format.
  • sqldf is used to run SQL queries on R data frames. With basic SQL knowledge you can seamlessly perform data manipulation tasks using this package. You can import and analyse large .sql files using sqldf.
  • plyr is an R programming package that provides a set of functions to split, apply, and combine data. The plyr functions split a data structure into groups, apply a specific function on each data group, and provide the results in a data structure format.
  • forecast is the library of choice for many data scientists to perform time-series and linear model analysis. It is used for displaying and analyzing forecasts using time series techniques such as ARIMA, Exponential smoothing, AR and so on.
  • ggplot2 is a data visualization library which is useful in producing classic graphs, plots, and advanced graphics. It is a flexible and powerful package that uses “Grammar of Graphics” standards to produce elegant visualizations. It is widely used in exploratory data analysis.
  • Quality Control Charts (qcc) is an R programming library for statistical quality control. Using qcc you can generate professional charts to monitor and track database changes, website traffic, transaction frequencies and volumes, and other operations.

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