There is a shortage of statistical R programmers in India but we are proud to have a team of certified R programmers in our Center of Excellence in India that can assist you with your advanced statistical R programming requirements.

Market Equations offers both statistical R and SAS programming services to clients worldwide with a focus on helping them adopt analytics thereby, promoting a fact based decision making culture in all organizations. Our R programmers in India are certified by the John Hopkins University and are experts in R programming with considerable experience with the techniques and concepts in R.

Outsourcing your R programming requirements to Market Equations can help you save considerable amount of time and cost without compromising on quality.

Flexible Engagement Models

Hourly:

The hourly rate engagement model provides flexibility to clients who have very urgent data analysis and reporting needs that require just a few hours of effort. It is very common in cases where the client may require data to be analyzed and summarized quickly for urgent decision making usually with a TAT of less than 48 hours.

Project :

The project based engagement model is the most commonly used model. In this case project requirements are analyzed and a total project fee is arrived at after careful evaluation. The project may involve an offshore R programmers engagement for a week or even months. This model is preferred for one off projects when there is no recurring analysis need or model updating required.

Monthly:

The monthly engagement model provides clients an option to recruit an outsourced R Programmer for a short duration of 3-6 months or on a yearly contract commitment. In this case, the R Programmer works as an extension of the clients onsite team. This model is preferred in cases where the client is looking at growing their team rapidly with a limited budget.

*** R Programmers can be recruited for half a month or specific weeks in a month when there are fluctuations in work volume.

Our expertise with the techniques used in R programming includes:

• Reading data from various source files
• Evaluate the cumulative distribution function, the probability density function and the quintile function
• Examining the distribution of a set of data: stem and leaf plot
• One or two sample tests: box plot, t-test, F-test, two-sample Wilcoxon test, Two-sample Kolmogorov-Smirnov test
• Grouping, loops and conditional execution: if statements, for loops, repeat, and while loops
• Writing R functions
• Statistical modelling: regression analysis and the analysis of variance, generalized linear models, nonlinear regression models
• Creating data graphics: High-level plotting functions, Low-level plotting functions, Interactive graphics functions
• Accessing and installing R packages
• Debugging
• Organizing and commenting R code

Our knowledge in R programming extends to its comprehensive list of concepts including:

Accessing built-in datasets, Additive models, Analysis of variance, Arithmetic functions and operators, Arrays, Binary operators, Box plots, Character vectors, Concatenating lists, Control statements, Customizing the environment, Data frames, Density estimation, Determinants, Diverting input and output, Dynamic graphics, Eigenvalues and eigenvectors, Empirical CDFs, Generalized linear models, Generalized transpose of an array, Generic functions, Graphics device drivers, Graphics parameters, Grouped expressions, Indexing of and by arrays, Indexing vectors, Kolmogorov-Smirnov test, Least squares fitting, Linear equations, Linear models, Lists, Local approximating regressions, Loops and conditional execution, Matrices, Matrix multiplication, Maximum likelihood, Missing values, Mixed models, Named arguments, Namespace, Nonlinear least squares, One- and two-sample tests, Ordered factors, Outer products of arrays, Probability distributions, QR decomposition, Quantile-quantile plots, Reading data from files, Regular sequences, Removing objects, Robust regression, Search path, Shapiro-Wilk test, Singular value decomposition, Statistical models, Student's t test, Tabulation, Tree-based models, Updating fitted models, Wilcoxon test, Workspace, Writing functions.

Case Study with Statistical R Programming:

Market Equations helps a United Kingdom (UK) based E-Retailer institutionalize Sales and Marketing Analytics by building a correlation model linking Facebook "likes" and "fan" growth to Sales, helping them allocate their marketing spends effectively into channels that maximize returns and reduce costs incurred in holding excess inventory and retain clients by eliminating the possibility of stock outs.

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Market Equations India offers clients a combination of rich Industry experience and a committed group of intellectuals from business, science and mathematics disciplines that are passionate about analytics and are comfortable and current with programming using different statistical software including R, SAS, SPSS and MATLAB.