Data Analysis with R

Learn via : Virtual Classroom / Online
Duration : 2 Days
  1. Home
  2. Data Analysis with R


    R has a rich set of libraries that can be used for basic as well as advanced data analysis tasks. If you have a basic understanding of data analysis concepts and want to take your skills to the next level, this course is for you. The aim of this training is to teach the R language by applying with real data and to prepare those who want to pursue a career in data science in accordance with the needs of the business world. Participants who complete the training will be able to do business both in R language and in data pre-analysis, processing and visualization.


    Delegates will learn
    • Access to the file system with R
    • R variables, memory, workspace
    • Native data types
    • Variable subsetting
    • Flow control
    • Functions
    • Data import and export methods
    • Data visualization with R



    – Those who want to develop their careers in data science.

    – Those who want to deepen and differentiation the data analysis technique in their researches and projects.


Introduction to R

  • Introduction to R language (free software, matrix language, package system, writing R function to other languages)
  • R help system
  • Access to the file system with R
  • R variables, memory, workspace
  • Missing observations
  • Homework with practical exercises

Native data types

  • vector system
  • digital vectors
  • character vectors
  • logical vectors and operators
  • category (factor)
  • data frame – csv files
  • list – json files
  • Homework with practical exercises

Variable subseting (subsetting)

  • subseting with boolean variables
  • subseting with numeric insets
  • subseting with observation names
  • deleting observation
  • assigning to variable subset
  • Homework with practical exercises

 Flow control

  • Conditional flow (if)
  • Matrix loops (with index and apply*)


  • Modular programming
  • Rules for writing consions in R
  • R’s “formula” system
  • Homework with practical exercises

Data import and export methods

  • Csv
  • Json
  • Xml
  • pulling data from http APIs
  • Homework with practical exercises

Data visualization with R

  • principles of creating charts for data analysis
  • base R graphics system
  • Homework with practical exercises


There is no prerequisite for this course.