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dplyr is a powerful R-package that transforms and summarizes tabular data with rows and columns. The package contains “verbs'' or a set of functions that perform standard data manipulation challenges, like filtering rows, selecting specific columns, reordering rows, adding new columns, summarizing data, etc. dplyr package also expedites the data transformation process through a consistent collection of functions. Additionally, when organizations hire dplyr developers, the computational back-end becomes more accessible and productive. Most dplyr verbs/functions use tidy evaluation in some way.
Developers who are familiar with R, are also familiar with base R functions such as split(), subset(), apply(), sapply(), lapply(), tapply() and aggregate(). Compared to base functions in R, dplyr functions are easier to work with, more consistent in the syntax, and more targeted for data analysis around data frames.
The consistency of dplyr functions improves usability and facilitates users to connect transformations to form data pipelines. These pipelines can be seen as a high-level query language, as well as, it is even possible to translate these data pipelines to other back-ends including databases. By hiring dplyr developers, businesses can manipulate their data to better explore and interpret what they have.
dplyr programmers use dplyr functions to perform some specific operations, however, there are even more ways to use the verbs, and also other functions in the package as well.
But, it is often difficult to recruit experienced dplyr developers as thousands of businesses compete to hire top dplyr developers from a limited pool of skilled professionals. The shortage of experienced dplyr programmers also means hiring quality developers is a costly and time-consuming affair.
So, what’s the solution? Is it possible to hire cost-effective dplyr developers quickly without compromising on quality?
The answer is yes.
Turing helps companies in hiring the best remote dplyr developers pre-vetted for a Silicon Valley bar at half the price. We vet the best remote dplyr developers after testing their expertise in R language, advanced data science capabilities, end-to-end data import, processing and management of a huge volume of data inflow, data handling, data structure, data analysis & data visualization, as well as knowledge in statistical software e.g. STATA, Big Data space, visualizing data analytics, etc. We also ensure our vetted developers are excellent team players and have a good grasp on soft skills, including communication skills, good verbal and written skills, problem-solving and decision-making skills, understanding user demands, etc.
Companies can now build and grow their best remote dplyr developers team in just a few days with Turing.
Including top companies backed by: