Introduction to R

Introduction to R



- Anyone can join the training
- Course outline as presented on the website
- Small groups, 3-10 people


Price set individually

- Training carried out on an individual order
- Training workshop just for your team
- Course outline tailored to your needs

About the training

R is currently one of the most popular tools for data analysis. It is also a tool that helps automating data analysis. You can use R to create your own functions, do complicated calculations, transform and aggregate data, build statistical models or use data mining techniques and in the end present your results on practical and esthetic charts and reports. Since R is an open source language and software there is a large community involved in developing and improving it. Thanks to that R is continuously updated and contains packages using the latest and most advanced techniques in data analysis and statistics. R is widely popular among companies, banks, universities and public institutions.

Who is this training for?

Introduction to R is aimed at people who would like to use programming to automate and enhance their statistical analyses and become comfortable with handling data:

  • Analysts
  • Consultants
  • Managers
  • Statisticians
  • Researchers
  • Students

What will I learn?

After completing the training, participants will be able to:

  • Use R and R Studio with ease
  • Manipulate and aggregate data to extract key information
  • Import and export data to and from XLS, CSV, SAS, SPSS files
  • Use R to build statistical models
  • Create analysis and reports that you can easily re-run with new data
  • Use loops and conditional execution to automate your work
  • Create visual and appealing charts

Course outline

  1. Introduction
    • Downloading and installation of R
    • Making R more friendly, R and available GUIs
    • The R environment
    • Related software and documentation
    • R and statistics
    • Using R interactively
    • An introductory session
    • Getting help with functions and features
    • R commands, case sensitivity, etc.
    • Recall and correction of previous commands
    • Executing commands from or diverting output to a file
    • Data permanency and removing objects
  2. R Objects
    • Data structures
    • Numbers and vectors – creating, modifying and calculations
    • Arrays and matrices – creating, modifying and calculations
    • Factors – creating and working with factors
    • Lists – creating, aggregating and working with lists
    • Data frames – creating, aggregating and working with data frames
    • Exercises
  3.  Reading data from files
    • Reading data from command line
    • Data import from CSV
    • Data import from TXT
    • Data import from XLS
    • Data import from SAS, SPSS, STATA files
    • Data import using URLs
    • Importing data from databases
    • Exercises
  4. Data processing
    • Subscripts and indices
    • Filtering and sorting data
    • Data aggregation
    • Creating new variables
    • Group operations
    • Changing data types
    • Working with large files
    • Exercises
  5. Basic programming
    • Logical operations – TRUE, FALSE, OR, AND, XOR
    • Conditional code execution – if, ifelse, switch
    • Loops – for, while, repeat, replicate
    • Functions – creating and calling functions, multiple and optional arguments
    • Good practices in programming
    • Exercises
  6. Charts
    • One variable charts
    • Multiple variable charts
    • Graphical parameters
    • Saving charts as png, jpg, pdf files
    • Advanced charts
    • ggplot2 and lattice packages
    • Exercises
  7. Statistical models in R
    • Descriptive statistics
    • Probability distributions
    • Correlation
    • ANOVA
    • T-student test
    • Linear regression
    • Generic functions for extracting model information
    • Exercises
  8. Reproducible analysis
    • Creating scripts
    • Making reports containing R code using RMarkdown
    • Good practices

Course Curriculum

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