Course Data Analysis with R

In the course Data Analysis with R you will learn programming in the R language and how you can use R for data analysis and visualization. R has become a standard platform for data analysis and data visualization and can perform a huge range of statistical procedures. In the course Data Analysis with R a series of coherent R packages are used, known as the tidyverse. These packages share an underlying design philosophy, grammar and data structures and are especially suitable for data science.

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  • Modules
  • General
  • Reviews
  • Certificate
  • Course Data Analysis with R : Content

    R Intro

    The course Data Analysis with R starts with the installation of R and the R Studio development environment. The basic syntax of R and the installation of R packages are also discussed.

    Plotting in R

    Next you will learn how you can quickly gain insight into the data with the ggplot2 package by means of plots. The different plot types, themes and layouts are discussed as well.


    Then it is time for the dplyr package with which common data transformation problems such as filtering, sorting, summation and grouping can be solved.

    Data Cleaning

    Presenting data with the rmarkdown package is also covered. As well as tidying raw data with the tidyr package, where columns become variables and rows become observations.

    Date and Times

    Time series occur in many data sets. The processing of these time series is addressed with the lubridate package that has many useful functions for processing dates and time.

    Data Import

    Part of the course program is also the import of data from CSV files and file formats from other statistical packages such as SPSS or SAS. Reading from and writing to databases is also treated.

    Statistical Analysis

    Finally the course Data Analysis with R deals with statistical analysis models such as linear and non-linear models, variable transformations and regressions. All this is supported with many practical examples and can also be applied to cases that are brought along by the students.

  • Course Data Analysis with R : Training

    Audience Course Data Analysis with R

    The course Data Analysis with R is intended for Big Data analysts and scientists who want to use R to analyze their data and to make static analyzes.

    Prerequisites Data Analysis with R

    Experience with programming is beneficial to good understanding but is not required.

    Realization Training Data Analysis with R

    The theory is discussed on the basis of presentations and examples. The concepts are explained with demos. Then there is time ample to practice with it yourself. R-Studio is used as a development environment. Course times are from 9:30 am to 16:30 pm

    Certification Course Data Analysis with R

    After successful completion of the course the participants receive an official certificate R Programming.

    Data Analysis with R
  • Course Data Analysis with R : Modules

    Module 1 : Intro R

    Module 2 : Graphics and Plots

    Module 3 : Transformations

    Overview of R
    History of R
    Installing R
    The R Community
    R Development
    R Studio
    R Console
    R Style
    Using R Packages
    R Syntax
    R Objects
    Graphics Devices and Colors
    High-Level Graphics Functions
    Low-Level Graphics Functions
    Graphical Parameters
    Controlling the Layout
    Changing Plot Types
    Quick Plots and Basic Control
    Changing Plot Types
    Themes and Layout
    R Functions
    Functions for Numeric Data
    Scoping Rules
    group by

    Module 4 : Presentation

    Module 5 : Data Cleaning

    Module 6 : Date Times

    Reproducible research
    Sharing results
    Repetitive Tasks
    Family of apply Functions
    apply Function
    lapply Function
    sapply Function
    tapply Function
    Logical Data
    Missing Data
    Character Data
    Duplicate Values
    Time and Date Variables
    Setting a datetime
    Getting values from a datetime
    strftime Command
    strptime Command
    as.Date function
    Datetimes Calculations
    difftime Command
    Time Series Analysis

    Module 7 : Data Import

    Module 8 : Linear Models

    Module 8 : Non-Linear Models

    R Datasets
    Importing CSV Files
    Import from Text Files
    Import from Excel
    Import from Spss or SAS
    Connecting to a database
    Connecting to a cluster
    Databases and ODBC
    What is a model?
    Statistical Models in R
    How to evaluate a model?
    How to use a model?
    Simple Linear Models
    logistic regression
    linear regression
    R squared
    p values
    confidence intervals
    Decision Trees
    random forest
    Optional material :
    Interactive dashboards with Shiny
    Web Scraping
    Writing packages
    Functional programming
  • Course Data Analysis with R : General

    Read general course information
  • Course Data Analysis with R : Reviews

  • Course Data Analysis with R : Certificate