Dplyr (pronounced “dee-ply-er”) is the leading data manipulation tool in R. Data scientists may shorten and better comprehend the time spent on data preparation and administration by learning and using dplyr.

  • Data scientists often use dplyr to convert pre-existing datasets into a more suitable format for research or data visualization.

2014 saw the introduction of dplyr in R, one of the core R packages of the tidyverse. Hadley Wickham, the founding developer of the dplyr package, calls it a “language of data manipulation”. This is due to the fact that the package includes a collection of verbs (functions) to define and execute frequent data preparation operations. Mapping inquiries about data collection to particular computer operations is one of the primary problems of programming. Since you can use the same language for both questioning and coding thanks to the availability of data manipulation grammar, this procedure runs more smoothly. Specifically, the dplyr language makes it easy to discuss and execute these tasks:

  • Filter a dataset down to only the columns you need to answer a question – Select.
  • Filter irrelevant data and retain only relevant observations (rows) depending on stated criteria.
  • Modify a data collection by adding more characteristics (columns) – Mutate.
  • In a certain order, arrange the observations (rows).
  • Summarize data using aggregates such as the mean, median, and maximum.
  • Merge separate data sets into one comprehensive table.

Using these terms, you may describe the technique or process for querying data, and dplyr will generate code that closely matches your “plain English” description due to the similarity between the language used by the functions and processes. Indeed, many practical queries regarding data collection may be answered by isolating select rows/columns as “items of interest” and then executing a simple comparison or calculation. Although equivalent computations are doable using basic R functions, the dplyr functions in R make it considerably simpler to create and comprehend such code.

Starting dplyr

Since dplyr is an external package, it must be installed (once per computer) and loaded in each script where the functions are to be used:

  • install.packages(“dplyr”) # once per machine
  • library (“dplyr”) # in each relevant script

Once the package is loaded, its functions may be used in the same way as any other built-in ones. Moreover, if you want to install other programs from the tidyverse collection you may do so by importing the gathered tidyverse package.

  • Dplyr is a data manipulation language that provides a consistent collection of verbs to help you overcome the most frequent data manipulation difficulties.

Dplyr summarizes:

  • mutate() introduces new variables that serve as functions for existing variables.
  • select() chooses variables according to their names.
  • filter() selects cases according to their values.
  • summarize() summarizes numerous values into a single value.
  • arrange() modifies the row’s ordering.

All of these naturally integrate with a group by(), allowing you to do any action “by a group”. In the vignette, you may discover more about them (“dplyr”). In addition to the verbs listed above that only need one table, dplyr also offers a wide selection of verbs that require two tables, as described in the following vignette (“two-table”).

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Dplyr not only simplifies and speeds up work with data frames and tibbles, but also with a wide variety of additional computational backends. Here are alternative backends:

  • dtplyr for huge datasets in memory. Transforms your dplyr code into data with good speed table code.
  • dbplyr for relational database-stored data. The SQL translation of your dplyr code.
  • sparklyr for storing very huge datasets in Apache Spark.