R and Its Role in Data Science

R is a well known statistical programming language used for tidying data, data analysis and constructing insightful visuals. A large number of industries such as healthcare, education, sports, marketing, finance, research and many more utilize R. There are many useful tools in R that help industries discover important insights and assist these industries in making decisive decisions. There are numerous different statistical software programs available to users such as (SPSS, SAS, and Python), but I believe R is the best choice for Data Analysts and Data Scientists because it is free, easily accessible, up to date and the program offers a wide variety of packages/libraries for users to utilize.

R provides a wide array of statistical analysis functions such as Hypothesis Testing(T-Test, ANOVA), Regression Analysis(Linear Regression, Multiple Regression, Logistic Regression), Descriptive Statistic(Mean,Median,Mode,Frequency, and Standard Deviation) which are all beneficial for performing a comprehensive data analysis task. To add on R contains a package called ggplot2 and it enables users to create meaningful visuals derived from their dataset. R offers a wide variety of visualizations options such as histograms, pie charts, box plots, line graphs, scatter plots, and many more. Graphic visuals are a key component in Data Science because they highlight important patterns, relationships, and trends between variables in a dataset. In Data Science analysts are always working with large and complex datasets that are usually dirty and cleaning the data is an essential step before they start their analysis. R offers plenty of packages that Data Analysts can use to clean/manipulate their data. Some commonly used packages for data cleaning are dplyr, tidyr, data.table.

I firmly believe that R is a valuable statistical program that all Data Analysts should use, not only because of the diverse statistical packages it provides, but also because of its simplicity in importing data and the added benefit of its markdown and notebook features. R is designed to read and load data from a variety of formats such as CSV, Excel,Stata, SAS Transport and SPSS. R Markdown is beneficial because it gives users the opportunity to write code and take notes all on the same document which is a very effective way to track progress. Another reason why R markdown is valuable is because it can be easily converted to other formats such as a Word Doc, or PDF by simply clicking on the knit button in R.