Start Date: 17th Mar, 2021. (17th -20th March 7pm – 8pm eachday)
INTRODUCTION: SESSION ONE
- Brief introduction to the R environment and its IDEs (R base, R studio and Jupyter Notebook)
- Objects, variable types, Arrays and matrices
- Vector and matrix arithmetics (addition, subtraction, multiplication, inverse, determinants eigenvalues and eigenvectors).
- Set working directory and importing of different data formats into R (CSV, Text files, EXCEL, SPSS, STATA, SAS, etc.).
- Attaching and detaching of variables & its effects.
- Exporting data or results from R as a CSV file into desired working directory.
- Practice Tasks.
LOOPS, CONDITIONAL EXECUTION AND USE OF APPLY FUNCTIONS: SESSION TWO
- For loops with conditions (single, double loops, etc.).
- While Loops with conditions.
- Saving results from Loops.
- Use of apply functions (apply, lapply, sapply and tapply).
- Practice Tasks.
CREATING OWN FUNCTIONS AND DATA VISUALIZATION IN R: SESSION THREE
- Basic syntax for functions in R.
- Creating functions around loops and apply functions.
- Returning results of a function as a single object, lists or data frame; and combination of these.
- Parallelizing functions in R (using parLapply for Windows OS and mclapply for Linux OS).
- Data visualizations.
- Practice Tasks.
INFERENTIAL TESTS AND MODELS IN R: SESSION FOUR
- Introduction to Simulations in R (probability distributions: binomial, Poisson, uniform, exponential, normal and lognormal samples).
- Bootstrapping and Jackknife re-sampling schemes/estimations in R.
- Statistical tests (parametric and non-parametric tests).
- Regression (Mixed and fixed effect models) and Time series modelling.
- Practice Tasks.