Course Outline

Day One

  1. Introduction to R & Rstudio (2 hours)
    • Making R more friendly, R and available GUIs
    • Rstudio
    • Scripting in Rstudio
    • Navigation, sections and code folding
    • Troubleshooting and code debugging in RStudio
    • Related software and documentation
    • Getting help with functions and features
    • Projects in RStudio
    • Creating analytical reports with RStudio
    • Keyboard shortcuts and useful features
  2. Importing/Exporting data (1 hour)
    • Flat files – txt, csv
    • Spredsheet files – xls, xlsx
    • SPSS, SAS and other formats data
    • Accessing data from SQL data sources
    • SQL database connectivity and operations
  3. Organising data (2 hours)
    • Data types and classes
    • Data storage in R – Rdata format
    • Objects structure
    • Numbers and vectors
    • Matrix and table
    • Factors
    • Lists
    • Data Frames
    • Date and time
  4. Tabular representation (3 hours)
    • Overview of packages for data tables – dplyr, tidyr, data.table
    • Indexes and subscripts
    • Selecting, subsetting observations and variables
    • Filtering, grouping
    • Recoding transformations
    • Reshaping data
    • Merging data
    • Character manipulation, stringr package
    • Regular expressions

Day Two

  1. Related software and documentation (1 hour)
    • Rstudio and GIT - versioning
    • Markdown
    • Reports and presentations with LaTeX
    • Shiny web applications
  2. R and Statistics (2 hours)
    • Probability and Normal Distribution
    • Random numbers
    • Descriptive Statistics
    • Standarization and Normalization
    • Confidence Intervals
    • Hypothesis Testing
    • ANOVA
    • Qualitative data analysis
  3. Linear regression (2 hours)
    • Correlation coefficient and interpretation
    • Simple and multiple linear regression
    • Estimation methods – Least squares
    • Model validation – tests for violation of assumptions
    • Selecting variables – different approaches
    • Regulatizations – ridge and lasso regression
    • Generalized least square – nonlinearity
    • Logistic regression
  4. Graphical procedures (2 hours)
    • Basic plots for 1 variable
    • Visualizations for 2 and more variables
    • Graphical parameters
    • Special plots
    • Exporting plots to png, pdf and jpeg files
    • Extending graphical capabilities of R with ggplot2
  5. Help in R (1 hour)
    • Searching through documentation of R
    • R packages and documentation
    • R Cran Task View – search for problem solution

Requirements

There are no specific requirements needed to attend this course.

  14 Hours
 

Number of participants


Starts

Ends


Dates are subject to availability and take place between 09:30 and 16:30.
Open Training Courses require 5+ participants.

Testimonials (1)

Related Courses

Related Categories