'RStudio' as of recently offers the option to define addins and assign shortcuts to them. This package contains addins for a few most frequently used functions in a data scientist's (at least mine) daily work (like str(), example(), plot(), head(), view(), Desc()). Most of these functions will use the current selection in the editor window and send the specific command to the console while instantly executing it. Assigning shortcuts to these addins will save you quite a few keystrokes.

Provides functionality that assists in tabular description and statistical comparison of data.

Various methods for the identification of trend and seasonal
  components in time series (TS) are provided. Among them is a data-driven locally
  weighted regression approach with automatically selected bandwidth for
  equidistant short-memory time series. The approach is a
  combination / extension of the algorithms by
  Feng (2013) <doi:10.1080/02664763.2012.740626> and Feng, Y., Gries, T.,
  and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598> and a brief
  description of this new method is provided in the package documentation.
  Furthermore, the package allows its users to apply the base model of the
  Berlin procedure, version 4.1, as described in Speth (2004) <https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile>.
  Permission to include this procedure was kindly provided by the Federal
  Statistical Office of Germany.

A 'shiny' application that enables the user to create a
    prototype UI, being able to drag and drop UI components before being
    able to save or download the equivalent R code.

Intelligently assign samples to batches in order to reduce batch effects.
    Batch effects can have a significant impact on data analysis, especially
    when the assignment of samples to batches coincides with the contrast
    groups being studied. By defining a batch container and a scoring function
    that reflects the contrasts, this package allows users to assign samples in
    a way that minimizes the potential impact of batch effects on the
    comparison of interest. Among other functionality, we provide an
    implementation for OSAT score by Yan et al. (2012,
    <doi:10.1186/1471-2164-13-689>).