Hire Top Freelance R developers with UltraGenius

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Hire only the best freelance R developers among the 20K+ engineers who have applied to UltraGenius


Freelance R developer

Rishabh is a software engineer having 7+ years of experience designing real-time statistical software using R programming language for stat and volatility modeling.

Expert in


Core Java




10 Years


Full Time


Freelance R developer

Mahesh is a software engineer having 3+ years of experience designing real-time statistical software using R programming language for stat and volatility modeling.

Expert in


Core Java




10 Years


Full Time


R freelance developer

John is a software engineer having 10+ years of experience designing real-time statistical software using R programming language for stat and volatility modeling.

Expert in


Core Java




10 Years


Full Time

Hire top talented freelance R developers with UltraGenius

R is a high-level statistical programming language for Big data analysis and data science. R is developed to answer the problems of statistics, data science, machine learning and serves as a data analysis tool. R programming language also allows freelance R developers to integrate with other programming languages such as C or C++. Using R, computing the “Measures of Central Tendency” has become much simpler. 

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Want to hire freelance R developers on your own? Here are the skills you must look for while hiring the best freelance R developer

Hiring freelance R developers might be an intricate task for you if you are a non-technical manager. R is a great statistical analysis tool to work with if the freelance R developer. With the help of R, data visualization and manipulation, data predictive modelling and forecasting becomes much easier for R freelance developers. But, hiring the most skilled R developers among several R freelancers is a challenging task for anyone. UltraGenius understands your job requirements and gets you only the top developers who are excellent R programmers.

The following skills you must look for while hiring a freelance R developer – 

Steadfast knowledge of R syntax and data structures

R developer should have conceptual knowledge working with R programming language. There are three ways of variable assignment in R – using equal to operator which copies data from right to left, using leftward operator which tells that data is stored from right to left, using rightward operator for copying data left to right. The R freelance developer should also know about various data structures and their usages in R like Vectors, Matrices, Arrays, Lists, Data Frames, and Factors.

Familiarity with R's Condition Handling

Check whether the R developer knows about Error Handling and Condition Handling concept in R. There is a try() function which gives a freelance R developer the ability to continue executing the code even if an error occurs in the program. If a failure occurs, then tryCatch() specifies what action should be taken by embedding the handler functions inside it which implement different logic.

withCallingHandlers() is another function which acts as an alternative for tryCatch(), only the difference is withCalling Handlers() registers the local handlers while tryCatch() establishes the existing handlers.

Experience using different packages of R programming language

A package is a way of storing sets of similar code and R functions. Each package performs a different role. Check if the developer knows difference between the library and package, as the former is a command used for loading a package and indicates the location where the package is stored on the computer. There are several packages namely dplyr package which provides a set of functions and tools for efficiently managing datasets in R, Shiny package for designing interactive applications, Grid and Lattice package for implementing graphical functions and develop circle, rectangle, histogram, bar graphs for representing the data.

Knowledge of Data Munging

Freelance R developers should have knowledge of data munging which is a very useful technique of transforming data from erroneous form / unusable form to usable form. The data is not ready for downstream computation if data munging is not performed up to a few extent. Data munging can be performed throgu hfollowing ways like  plyr package, dplyr package, apply() family and aggregate() method.

Experienced in working with Version Control System (VCS)

Freelance R developers need to be conversant with various version control systems, like Git, SVN, TFS and Mercurial. They use Git for their work most of the time. It is a version control system that helps them collaborate and organize code to handle changes in codes or scripts as well as maintain good commit history on old codes. Along with this knowledge there are other skills like add/push/pull commands which allow developers to make changes independently on different branches; merging allows them to combine two separate branches without any conflicts at all.

freelance developer

Knowledge of lexical and dynamic scoping in R

Lexical or Static scoping in R tells that the value of any variable is searched in the environment in which the function is defined. When you define a function in the global environment, and is subsequently invoked from the invoking environment, then the calling and the defining environment becomes the same. This generates the concept of dynamic scoping. With the dynamic scoping, the variable is bound to the most recent assigned value.

Experience working with RStudio

RStudio is the popularly used Integrated Development Environment (IDE) for R programming language. RStudio includes a syntax highlighting text editor and a console which provides the feature of direct code execution. RStudio also has tools for plotting the data, analyze the history, debugging, and managing the workspace. R provides the interactive and implementive debuggers to determine, diagnose, and fix errors. With the help of RStudio, R developers can easily manage multiple working directories in a single project.

Experience working with Data Visualization

Data visualization is a technique used for representing the data graphically. The graphical representation can be in the form of Pie Chart, Bar Graph, Histogram, Scatter Plot, Heat Map, Correlogram, Box Plot, and Area Maps. Check if the developer knows about Choropleth Map, which is used in coloring and shading different areas according to the value of statistical variable represented in that map.

Knowledge of Database Design

Freelance R developers must have the knowledge of organizing data according to different data models and must know how to design a database. The developers must also aware of defining data model with YAML manually, extracting data models from R data frames, and facilitate the development and maintenance of data at a large scale. R freelancers must also be aware of data mining and data engineering. 

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Familiarity using databases and RMySQL

RMySQL is an interface for database and the MySQL driver for R programming language. Comprehensive R Archive Repository (CRAN) is the core official repository containing all the packages including RMySQL. Connecting to a database through RMySQL interface becomes much easier. Just R programmers need to install the RMySQL package and load the library. 



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Connect with the top 1% freelance R developers of the world at the lowest prices

R is widely used in the realm of statistics and graphics. Banks often use R with Statistical Analysis System (SAS) software for bank data analysis and report writing. R is greatly used by research to predict the result of statistical analysis. But, hiring the freelance R developers is not an easy task when a large number of R freelancers are trying to grab the job opportunities. So, here UltraGenius comes to assist you in hiring only the top 1% R developers among 20000 developers who have applied on our platform.

Top Interview Questions to ask while hiring a freelance R developer

Here are the data structures available in R programming language -

  • Vector - A vector is a sequence of homogeneous data elements like an Array in C++. The data elements in a vector are called Components.
  • List - A list is a heterogeneous data type in R consisting of numbers, vectors, string or may be another list inside one list. 
  • Matrix - A matrix is a two-dimensional data structure which is used to bind the vectors of same size. All the matrix elements of the matrix should be of same data type.
  • DataFrame - A data frame is an extended form of matrix where different columns can have different data types like numeric, string, character, etc. A dataframe combines the matrices and lists feature such as a rectangular list.

R Markdown is a type of file format for designing dynamic documents with R programming language. Markdown is a light-weight text-to-HTML conversion tool which allows developers to write and read the document easily using a plain-text editor. R Markdown is designed and developed using some chunks of R code and Markdown.

R Markdown is a better choice than LaTex for R programmers as it allows the insertion of the results of R code inside the formatted documents directly.

A confusion matrix in R is an N x N matrix used for evaluating the performance of a classification model, where N represents the number of target classes. The confusion matrix compares the values predicted by the machine learning model with the actual values.

Data imputation in R is the mechanism of replacing the missed or inconsistent values with the predicted data values based on the information that is available to the R freelance developers and researchers. The estimated values are mean or median of the massive datasets, for example, if there are x1, x2, x3, x4, x5, x6, ....xk variables and the value of x1 goes missing then its value is regressed on variables from x2 to xk and substituted. Once all missing / inconsistent values are replaced with estimated values, then data analysis can be started using the standard methods and techniques. 

Here is the list of packages used for imputing data in R -

  • Amelia
  • MICE
  • Hmisc
  • missForest
  • Mi
  • imputeR

Here are the steps used to design and evaluate a R linear regression model -

  • First, divide the data into train and test sets as the linear regression model is designed on train sets and evaluated on test sets. This can be done using function sample.split() which is available in "catools" package in R. It provides the option of split-ration which an R developer can set according to the requirements.
  • Once the data is split into train and test sets, then start working on building the model over train set. The lm() function from DAAG package is used for building a model.
  • Finally, for predicting the results on test sets, use predict() function.

Random Forest is a non-linear classification algorithm used in R for building and combining multiple decision trees to generate more accurate estimations. This approach is called "Random Forest" because it is used randomly at the training time and multiple trees are used to predict the results. The Random forest algorithm always gives more accurate results than decision trees as it combines multiple decision trees for predicting the results.

Random forest takes random sample from a set of observations and predicts the result in the following manner -

  • Select any n samples from train data and draw a bootstrap sample.
  • Make a decision tree from the bootstrap sample. Choose d features randomly at each node of the tree.
  • Next, split the node according to an objective function, for example,  again maximize the information and using variables (or features). 
  • If m is the number of trees that need to be created using subset of observation samples, then steps 1st and 2nd are repeated m times.
  • Combine the prediction generated by each tree for assigning a new data point to the group that is selected by majority of trees.

The rbind() and cbind() functions combine matrices, vectors, or data frames of same size by rows and columns respectively. The cbind() function combines specified matrix, data frames, or vectors by columns whereas the rbind() function combines any specified matrix, vectors, or data frames by rows.

For example, v1 = 1:5, v2 = 6:10


The output will be - 

[1, ] 1 6

[2, ] 2 7

[3, ] 3 8

[4, ] 4 9

[5, ] 5 1

On the same vectors' set, if rbind is applied -

rbind(v1,v2), then output will be - 

   [  ,1] [  ,2] [  ,3] [  ,4] [   ,5] 

v1  1       2       3      4     5

v2 6       7       8      9        10

Principal Component Analysis (PCA) in R is an important and useful technique to reduce the dimensionality of datasets, increasing the data interpretability without losing any information. PCA does this by creating new variables which are uncorrelated and maximize variance.

Steps to implement PCA - 

  • Calculate the n-dimensional mean of the given data set.
  • Calculate the co-variance matrix of the variables(or features).
  • Calculate the eigenvalues and eigenvectors of the covariance matrix.
  • Sort / Rank the eigenvectors in descending order by eigen values.
  • Select x eigenvectors with the largest eigenvalues.

Frequently Asked Questions

UltraGenius is one of the leading platforms for hiring remote talent and connecting freelance and part-time developers with Silicon Valley businesses. We focus on finding the best talents who will perform extremely well and will be easily integrated into your teams. We filter out only the top 1% most skilled freelance developers among the 20K+ developers who have applied on our platform. Candidates have to prove their self-reported experience by giving Ultragenius’ s skill tests.

UltraGenius first tests the developer’s skill set by conducting a two and half hour hiring test. Our hiring test judges a candidate on all aspects like aptitude, case study analysis, verbal and reasoning, coding questions based on data structures and algorithms, software engineering, system design, and more. Then, there is another round for the candidates who are selected from this round called “Higher-level Assessment Skill Test”, which is a video round that deeply analyzes R freelance developers’ major skills and asks questions about the projects they have worked upon.

Fill up the form which is on every hiring freelancers’ page and we will inform you once we select the top 1% freelance R developers matching your job requirements.  After analyzing the candidates based on their resumes and two assessment tests, we provide you the feedback quickly. And if the freelancers selected by our team are fit for your job role, then we also provide the onboarding.

UltraGenius offers you only the most skilled freelance developers who are top 1% among the 20K+ freelance developers who have applied on our platform. After a rigorous selection and testing process, we sort out only the top candidates for you. You can check out UltraGenius’ s selection process for hiring freelance R developers on https://www.ultragenius.club/hire-r-freelancer/.

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