R

Introduction to R

R ( http://www.r-project.org/) is an open source software- a well organized and sophisticated package- that facilitates data analysis, modeling, inferential testing and forecasting. It is a user friendly software which allows to create new function commands to solve statistical problems. It runs on a variety of UNIX platforms(and similar systems such as LINUX), Windows and Mac OS.

R is a free, useful software package to anybody who wishes to undertake extensive statistical computations (a user community that includes students, researchers and professionals belonging to various disciplines).

Basic Level

Introductory sessions in R

1. Installing R
   • Downloading and installing R
2. Basic operations in the R console
   • To open the R console
   • To run commands in R
   • To correct errors made in the R commands
   • To save work done in R
   • To quit the R console
3. Documentation and Packages in R
   • To access installed documentation and packages in R
   • To install and load packages in R

   Data structures

4. Variables and Vectors in R
   • Creation and deletion of variables and vectors
   • Listing the vectors
   • Modifying vectors
   • Creating row and column vectors
5. Vector Algebra and Matrices in R
   • Vector algebra
   • Creating matrices
   • Matrix operations
6. Sequences, lists, strings and dates in R
   • Creation of sequences and lists
   • Modifying lists, selecting elements from a list
   • Modifying strings, substrings
   • Date-string conversion
   • Other functions related to dates
7. Handling data in R
   • Creating and modifying data frames
   • Reading data stored in files of different formats

   Basic computations in R

8. Elementary operations in R
   • Arithmetic
   • Higher powers and roots of a number
   • Logarithms and exponentials
   • Operations on complex numbers
9. Measures of central tendency and dispersion
   • Mean, median and mode
   • Variance, standard deviation and quantiles

   Probability distributions

10. Discrete probability distributions:Binomial,Poisson and Geometric
    • Binomial,Poisson and Geometric densities, distribution and quantile functions, random variables
11. Discrete probability distributions:Negative Binomial and Hypergeometric
    • Negative Binomial and Hypergeometric densities, distribution and quantile functions, random variables
12. Continuous probability distributions
    • Normal, Chi squared, F and t densities, distribution and quantile functions, random variables
    • Log-normal, logistic, exponential and gamma densities, distribution and quantile functions, random variables
    • Beta, cauchy and weibull densities, distribution and quantile functions, random variables

    Graphical representation of information using R

13. Histograms, barcharts and box plots
    • Creating histograms, addding density estimate to a histogram
    • Creating and colouring bar charts, adding confidence intervals
    • Creating box plots
14. Scatter diagrams, regression lines and Q-Q plots
    • Plotting a scatter digram, adding title, label, grid and a legend
    • Graphing a function, a regression line (superimposing on scatter plot)
    • Creating Q-Q plots

    Econometrics in R

15. Simple and multiple linear regression
    • OLS, log-linear, log-log and semi-log regressions
    • Dummy variable regression, regression through the origin and with standardised coefficients
16. Regression Analysis
    • Confidence intervals
    • P-values and power functions
17. Tests for heteroskcedasticity
    • Park test
    • Goldfeld-Quandt test
    • Breusch-Pagan-Godfrey test
    • White’s General Heteroskcedasticity test
18. Tests for autocorrelation and specification errors
    • Durbin Watson test
    • Ramsey reset specification test

ADVANCED LEVEL TUTORIALS

1. Models of microeconometrics
2. Bayesian Econometrics
3. Time series Econometrics
4. Programming your own analysis

Contributors and Content Editors

Manivel, Nancyvarkey, Sudhakarst