Course Code: bspkr
Duration: 21 hours
Prerequisites:
  • An understanding of ___.
  • Experience with ___.
  • ___ programming experience.

Audience

  • ___
  • ___
  • ___
Course Outline:
  1. Introduction to R
  • Making R more friendly, R and available GUIs
  • The R environment
  • Related software and documentation
  • R and statistics
  • Using R interactively
  • Getting help with functions and features
  • R commands, case sensitivity, etc.
  • Recall and correction of previous commands
  • Data permanency and removing objects
  1. R Basics
  • Logical variables
  • Comparison operators
  • Vectors (defining a vector, naming entries, accessing elements, subsetting vectors, vector calculations)
  • Creating sequences
  • Character Strings
  • Factors
  • Converting between different data types (numeric, integer, logical, character, factor)
  • Matrices (creating matrices, naming rows & columns, accessing elements, submatrices, inverse & transpose, eigenvalues & eigenvectors)
  • Object classes
  • Handling missing values
  1. Data Representation
  • Arrays
  • Lists
  • Data frames
  • Tibbles
  1. Data Management using Tidyverse Packages
  • Pipeline operators
  • Reading and writing different types of data files using the readr package
  • Data manipulation using the dplyr package
    • Filter different observations/rows
    • Select variables/columns
    • Create new variables/columns
    • Sort observations/rows
    • Group observations by a variable
    • Calculate summary statistics
    • Merging data sets
  • Reshaping data using the tidyr package
  • Refer to classical R commands used for data management
  1. R Graphics
  • Plot() function
  • Customising a plot (title, x-axis & y-axis labelling, legends)
  • Bar charts, pie charts, box plots, histograms & density plots
  • Scatterplots
  • Adding text/labels to a chart
  • Arranging plots in a grid using par()
  • 3D & image plots
  • Using the ggplot2 package for advanced graphics
  1. Control Structures & Functions
  • If statements
  • Loops
    • For loop
    • Nested loops
    • While loop
    • Breaking loops
  • Functions
    • Creating new functions
    • Named arguments and defaults
    • Returning objects
    • Scoping
    • Warnings & Errors
    • Debugging
  1. Statistical Analysis in R
  • Examining the distribution of a data set
  • One- and two-sample tests
  • Linear regression models
  • Generalized linear models