R Programming Training Course

R programming training course exposed to fundamental programming concepts in R. After the basics, you will learn how to organize, modify and clean data frames, a useful data structure in R. Then you will learn how to create data visualizations to showcase insights in data.

R is a widely used statistical programming language that’s beloved by users in academia and industry. R works well with data, making it a great language for anyone interested in data analysis, data visualization, and data science. In this course you will learn how to program in R and how to use R for effective data analysis.

You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.

R programming training course introduces R environment and basic statistical analysis. It extends the learning curve by teaching techniques used for data manipulation and the overview of basic data structures.

Statistical applications using R programming and exploration of data using box plots, histograms, correlation coefficients will also be illustrated. Our course offers an expert's-eye overview of how these advanced tasks fit together in R as a whole along with practical examples.

Through hands on exercises and in-depth coaching you will get a thorough understanding of R, its data structures, data processing functions and data summarizing with R.

What Will I Learn?

• Clear understanding of Statistical programming and R environment
• In-depth knowledge of basic features, functions, operators available with R
• Comprehensive information about programming statistical graphics
• Ways of using simulation and numerical optimization
• Extract data from R objects, perform reading and writing of Data, and handle databases
• Use subscripting, character manipulation, and reshaping of data
• Find probability, distributions, regression and correlation
• Significance of sample size and its calculation. Advance data handling techniques