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Course Data Analysis with R

Course Data Analysis with R
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  • Modules
  • Course
  • Content
  • General information
    General
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  • 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
    Cheatsheets
    R Syntax
    R Objects
    ggplot2
    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
    Aesthetics
    Changing Plot Types
    Labels
    Themes and Layout
    dplyr
    R Functions
    Functions for Numeric Data
    Scoping Rules
    mutate
    arrange
    group by
    summarize
    select
    filter
    joining
    dataframe

    Module 4 : Presentation

    Module 5 : Data Cleaning

    Module 6 : Date Times

    rmarkdown
    Reproducible research
    Reporting
    Sharing results
    Repetitive Tasks
    Family of apply Functions
    apply Function
    lapply Function
    sapply Function
    tapply Function
    tidyr
    spread
    gather
    seperate
    unite
    Logical Data
    Missing Data
    Character Data
    Duplicate Values
    NA’s
    Time and Date Variables
    lubridate
    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
    Data.Frames
    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
    dbplyr
    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
    boosting
    overfitting
    Optional material :
    Interactive dashboards with Shiny
    Web Scraping
    Writing packages
    Spark
    Functional programming
  • Audience Course Data Analysis with R

    Data Analysis with RThe 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 Course Data Analysis with R

    Experience with programming is beneficial for a good understanding but is not required.

    Realization Training Data Analysis with R

    The theory is treated on the basis of presentations and examples. The concepts are explained with demos. The theory is interspersed with exercises and there is ample time to practice. R Studio is used as a development environment. The course times are from 9.30 to 16.30.

    Certification Data Analysis with R

    The participants will receive an official certificate Data Analysis with R after successful completion of the course.

  • Course Data Analysis with R

    In the course Data Analysis with R you will learn programming in the language R and how you can use R for effective data analysis and visualization. R has become a standard platform for data analysis and making graphs and you can perform a huge set of statistical procedures that are not available in other statistical programs. You first learn how to install and configure R. Next you will learn how to quickly gain insight into the data by means of graphs and transformations. The input of data from different sources is treated. Also data types of R, such as vectors, arrays, matrices, lists, data frames and factors are discussed. As well as control flow in R with the family of apply functions. The course also discusses statistical analysis models such as linear and non-linear models, variable transformations and regressions. Finally, attention is paid to how to present results through graphs, reports or interactive dashboards. All this is explained with many practical examples and can also be applied to cases that are brought in by the course participants.

  • Course Forms

    All our courses are classroom courses in which the students are guided through the material on the basis of an experienced trainer with in-depth material knowledge. Theory is always interspersed with exercises.

    Customization

    We also do custom classes and then adjust the course content to your wishes. On request we will also discuss your practical cases.

    Course times

    The course times are from 9.30 to 16.30. But we are flexible in this. Sometimes people have to bring children to the daycare and other times are more convenient for them. In good consultation we can then agree on different course times.

    Hardware

    We take care of the computers on which the course can be held. The software required for the course has already been installed on these computers. You do not have to bring a laptop to participate in the course. If you prefer to work on your own laptop, you can take it with you if you wish. The required software is then installed at the start of the course.

    Software

    Our courses are generally given with Open Source software such as Eclipse, IntelliJ, Tomcat, Pycharm, Anaconda and Netbeans. You will receive the digital course material to take home after the course.

    Lunch

    The course includes lunch that we use in a restaurant within walking distance of the course room.

    Locations

    The courses are planned at various places in the country. A course takes place at a location if at least 3 people register for that location. If there are registrations for different locations, the course will take place at our main location, Houten which is just below Utrecht. A course at our main location also takes place with 2 registrations and regularly with 1 registration. And we also do courses at the customer’s location if they appreciate that.