R – Advanced

Analysis
Data Science
Open Source
On-site courses
Short courses
Face-to-face
Remote/Virtual
English
French

Objectives

 

  • Deepen the tools to represent and manipulate complex data, discover the dplyr and data.table packages to optimize data processing, import data sources (CSV, JSON, XML, SQL), perform a simple or multiple linear regression model with the {stats} package, improve knowledge of graphs and know how to use ggplot2 or plotly.
  • At the end of this training, the participant should be able to import or even merge structured or unstructured data sources, apply advanced processing on quantitative and qualitative data, and build elaborate static or dynamic graphs.a

Prerequisites

  • Attended the R: basics training course, or equivalent

Pedagogical and technical material and resources

  • Dedicated digital training platform (LMS).
  • Sessions with the trainer, training material in digital format, balance between theoretical and practice, concrete cases.

Assessment

  • Practical application and exercises

Results & skills expected at the end of the training

  • Deepen the tools to represent and manipulate complex data, discover the dplyr and data.table packages to optimize data processing, import data sources (CSV, JSON, XML, SQL), perform a simple or multiple linear regression model with the {stats} package, improve knowledge of graphs and know how to use ggplot2 or plotly.
  • At the end of this training, the participant should be able to import or even merge structured or unstructured data sources, apply advanced processing on quantitative and qualitative data, and build elaborate static or dynamic graphs.a

Program

DAY 1

    • Importing external data sources with the{base}, {foreign} and {haven}

packages

  • The tools for optimizing data processing, {data.table} and {dplyr} :
    • Advanced data frame manipulation,
    • Data aggregation,
    • The reshaping,
    • Indexing,
    • The merging of data sources
  • The realization of a simple or multiple linear regression model with the {stats} package:
    • The simple and multiple regression,
    • The testing of regression coefficients,
    • The diagnosis of the model,
    • Point and interval prediction

DAY 2

  • The processing of strings, regex
  • The processing of dates and time series management
  • The functional approach and lazy evaluation
  • The database interface (SQL, NoSQL)
  • The advanced graphical features with the {ggplot2} package:
    • Trellis charts,
    • Statistical distributions,
    • Data presentation with heatmap
  • Building elaborate interactive static or dynamic graphs with the {ggvis} and {plotly} packages
Duration
14 hours
Level
Intermediate
Audience
Anyone wishing to discover the advanced R tools
Participants
8 people maximum
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