Advanced R Programming

Accueil » Formations » Data » Advanced R Programming

Advanced R course teaches students more sophisticated R skills, including using advanced regular expressions, machine learning, random effects modeling, Bayesian Inference, advanced R time series, and much more.

1650 € HT 3 jours DB-LRA

Programme

Workflow:

  • Using R Studio
  • Using RStudio projetcs
  • Using a simple files organisation method
  • Using rmarkdown files
  • Using a Version Control
  • Setting Global Options

 Import data sets:

  • How to import csv file?
  • How to import text file?
  • How to import zip file?
  • Note:
  • To go further

Cleaning and validation:

  • Checking importation
  • Checking data structure
  • The useful format for tabular data: tidy
  • Manage problems of data structure
  • Rename variables
  • Rename levels
  • Management of missing values (NA)
  • Identify absurd values
  • Detecting outliers

 Working with data :

  • The package dplyr
  • Select rows with filter()
  • Select columns with the select() function
  • Create new variable using mutate() function
  • Summarise data with summarise_*’ functions
  • Use the group_by() function
  • The arrange() function

 Combine rows and join columns:

  • Combining rows
  • Joining columns

• Descriptive analysis:

  • Interesting pacakages
  • Data
  • Missing, zeros and infinite values
  • Descriptive analysis for factors
  • Descriptive analysis for numeric variables

• Dates and times with R :

  • Introduction
  • Standardised format for dates times
  • Modification of the dates/times format and class
  • Keep only date
  • Rounding
  • Manipulate date data
  • Other things

 Data visualisation :

  • About ‘ggplot2’
  • Principle of ggplot2
  • Making scatterplots with ggplot2
  • Make barplots with ggplot2
  • Facetting
  • Back to the concept of mapping with the aes() function
  • Plotting time series
  • More things

 Regression analysis:

  • Introduction
  • The data: the cysfibr dataset
  • Data management
  • Assessment of linearity assumption
  • Exploring multicollinearity
  • Fitting the complete model
  • Checking assumptions
  • Results interpretation
  • Adjustement of pvalues
  • Selection of variables
  • Plotting your results

 Regression analysis with time (chronological data / longitudinal data):

  • Introduction
  • Data
  • Fit the linear regression model
  • Assessment of the residuals independce assumption
  • Adding and AR1 correlation structure
  • Assessments of residuals normality and homogeneity assumptions
  • Plot your model

 Principal Component Analysis :

  • Principle
  • How to run a PCA with R?
  • Plot your results
  • Ressources