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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).

The Centre for Computational Social Sciences, UPE programme (under the UGC scheme), University of Mumbai, is leading the initiative of popularizing R in India which is part of the Free and Open source Software for Science and Engineering Education (FOSSEE) project, supported by the National Mission on Education through ICT of MHRD (http://spoken-tutorial.org/NMEICT-Intro).

The Centre for Computational Social Sciences is using Spoken Tutorials (http://spoken-tutorial.org/) to create learning resources for FOSS. This is the main page for the organization of the scripts required for R spoken tutorials. We invite the R user community to participate in this activity.

R Slide Template                                                                                                                               Glossary


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


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

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