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In this world of information overload and data explosion, there is a dire need to leverage this data and make sense of it all. R is rapidly becoming the leading programming language for effective data analysis and statistics. It is the tool of choice for many data science professionals in every industry. Star R Programming is an all-inclusive training program that aims at building a skill-set to tackle real-world data analysis challenges as a data engineer. It is a guide to understand how to program in R and how to use R for effective data analysis.

The program delves into intricacies of calculations, co-relations, and statistical probabilities and teaches the learners the fundamental understanding of programming with R, detailing all aspects of the language such as understanding and process data structures, and mining information through data analysis that can suit a wide variety of purposes, and sectors as varied as finance, defense, health, education, etc. Further, the program dives deeper into the graphical capabilities of R, and helps you create your own stunning data visualizations.

R Programming Course Objectives

In this course, you will learn about:

  • The basics of statistical computing and data analysis
  • How to use R for analytical programming
  • How to implement data structure in R
  • R loop functions and debugging tools
  • Object-oriented programming concepts in R
  • Data visualization in R
  • How to perform error handling
  • Writing custom R functions

Course Content

  • Interface of R and RStudio
  • Data types and indexing (assignments, objects, vectors, matrices, data frames, lists)
  • R built in functions and syntax
  • Working directory
  • Packages
  • Importing data from CSV, Excel, .txt, SPSS, Stata files etc…
  • Exporting data as CSV
  • Preparing data for analysis (renaming variables, variable types, missing values)
  • Computing, transforming and recoding variables
  • Subset datasets by row and by columns
  • Descriptive statistics, frequency tables, cross tabulation tables
  • Graphics (boxplot, histogram, scatterplot, partitioning window)
  • Basic analyses (t-test, correlation, ANOVA, regression, chi-square)
  • Sub-setting datasets
  • Sorting datasets
  • Reshaping datasets
  • Merging and appending datasets
  • Aggregating datasets (statistics by group, using the suite of apply functions)
  • User written functions (syntax, if/else statements)
  • Loops (for loop, while loop, repeat loop)
  • Graphs for quantitative data (boxplots, scatterplots, bar graphs, histograms etc…)
  • Changing graph elements: titles, point size, colors
  • Extensive use of ggplot2

About the instructor


BI Solution Architect

Hi I am Dipu Maharjan. I am working as BI Solution Architect. I have experience more than 10 years as Data Analyst, Database Programmer, Developer and Trainer.

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    Course Features

  • Course duration35 hrs