Introduction to R Programming

Accueil » Formations » Data » Introduction to R Programming

In this course you will learn how to program in R and how to use R for effective data analysis.
You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.
The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.
Topics in statistical data analysis will provide working examples.

2300 € HT 4 jours DB-LRB

Programme

  • Overview
  • History of R
  • Advantages and disadvantages
  • Downloading and installing
  • How to find documentation
  • Introduction
  • Using the R console
  • Getting help
  • Learning about the environment
  • Writing and executing scripts
  • Object oriented programming
  • Introduction to vectorized calculations
  • Introduction to data frames
  • Installing packages
  • Working directory
  • Saving your work
  • Variable types and data structures
  • Variables and assignment
  • Data types
  • Data structures
  • Indexing, subsetting
  • Assigning new values
  • Viewing data and summaries
  • Naming conventions
  • Objects
  • Getting data into the R environment
  • Built-in data
  • Reading data from structured text files
  • Reading data using ODBC
  • Dataframe manipulation with dplyr
  • Renaming columns
  • Adding new columns
  • Binning data (continuous to categorical)
  • Combining categorical values
  • Transforming variables
  • Handling missing data
  • Long to wide and back
  • Merging datasets together
  • Stacking datasets together (concatenation)
  • Handling dates in R
  • Date and date-time classes in R
  • Formatting dates for modeling
  • Control flow
  • Truth testing
  • Branching
  • Looping
  • Functions in depth
  • Parameters
  • Return values
  • Variable scope
  • Exception handling
  • Applying functions across dimensions
  • Sapply, lapply, apply
  • Exploratory data analysis (descriptive statistics)
  • Continuous data
  • Categorical data
  • Group by calculations with dplyr
  • Melting and casting data
  • Inferential statistics
  • Bivariate correlation
  • T-test and non-parametric equivalents
  • Chi-squared test
  • Base graphics
  • Base graphics system in R
  • Scatterplots, histograms, barcharts, box and whiskers, dotplots
  • Labels, legends, titles, axes
  • Exporting graphics to different formats
  • Advanced R graphics: ggplot2
  • Understanding the grammar of graphics
  • Quick plots (qplot function)
  • Building graphics by pieces (ggplot function)
  • General linear regression
  • Linear and logistic models
  • Regression plots
  • Confounding / interaction in regression
  • Scoring new data from models (prediction) and Conclusion