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hands-on labFundamentals of R
Beginner
8h
505
3.4/5
Get guided in a real environmentPractice with a step-by-step scenario in a real, provisioned environment.
Learn and validateUse validations to check your solutions every step of the way.
See resultsTrack your knowledge and monitor your progress.
Lab steps
Starting the Fundamentals of R Lab Modules
Lab description

R is a free, open-source environment and language for statistical computing and graphics. R is a valuable tool for data scientists and statisticians having been developed by academics over several decades. R packages are readily available for every common data analysis algorithm making it easy to experiment with. However, to get the most out of R you must learn the fundamentals.

This lab includes several modules for you to work through to cement your R fundamentals proficiency. You will use a virtual machine that has RStudio, the premiere integrated development environment (IDE) for R. installed for you.

Learning Objectives

Upon completion of this lab you will be able to:

  • Develop R code using RStudio
  • Work with data and data structures in R
  • Reuse code by creating functions
  • Implement branching logic with conditional statements
  • Use inputs and outputs in your R code
  • Plot data using R

Intended Audience

This lab is intended for:

  • Anyone interested in getting started with R
  • Data scientists
  • Data engineers
  • Statisticians

Prerequisites

It is highly recommended to complete the courses in the Fundamentals of R learning path before attempting this lab.

Updates

June 24th, 2020 - Enabled direct browser RDP connection for a streamlined experience

About the author
Avatar
Kunal Haria
Data Science Trainer
Students
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Kunal has worked with data for most of his career, ranging from diffusion markov chain processes to migrating reporting platforms.  

Kunal has helped clients with early stage engagement and formed multi week training programme curriculum. 

Kunal has a passion for statistics and data; he has delivered training relating to Hypothesis Testing, Exploring Data, Machine Learning Algorithms, and the Theory of Visualisation. 

Data Scientist at a credit management company; applied statistical analysis to distressed portfolios. 

Business Data Analyst at an investment bank; project to overhaul the legacy reporting and analytics platform. 

Statistician within the Government Statistical Service; quantitative analysis and publishing statistical findings of emerging levels of council tax data. 

Structured Credit Product Control at an investment bank; developing, maintaining, and deploying a PnL platform for the CVA Hedging trading desk.