dplyr

One of the core packages of the tidyverse in the R programming language, dplyr is primarily a set of functions designed to enable dataframe manipulation in an intuitive, user-friendly way. Data analysts typically use dplyr in order to transform existing datasets into a format better suited for some particular type of analysis, or data visualization.[1][2]

dplyr
Original author(s)Hadley Wickham
Initial releaseJanuary 7, 2014 (2014-01-07)
Stable release
1.0.0 / June 1, 2020 (2020-06-01)
Written inR
LicenseGPLv2
Websitedplyr.tidyverse.org//

For instance, someone seeking to analyze an enormous dataset may wish to only view a smaller subset of the data. Alternatively, a user may wish to rearrange the data in order to see the rows ranked by some numerical value, or even based on a combination of values from the original dataset.

Authored primarily by Hadley Wickham, dplyr was launched in 2014.[3] On the dplyr web page, the package is described as "a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges."[4]

The five core verbs

While dplyr actually includes several dozen functions that enable various forms of data manipulation, the package features five primary verbs:[5]

filter(), which is used to extract rows from a dataframe, based on conditions specified by a user;

select(), which is used to subset a dataframe by its columns;

arrange(), which is used to sort rows in a dataframe based on attributes held by particular columns;

mutate(), which is used to create new variables, by altering and/or combining values from existing columns; and

summarize(), also spelled summarise(), which is used to collapse values from a dataframe into a single summary.

Additional functions

In addition to its five main verbs, dplyr also includes several other functions that enable exploration and manipulation of dataframes. Included among these are:

count(), which is used to sum the number of unique observations that contain some particular value or categorical attribute;

rename(), which enables a user to alter the column names for variables, often to improve ease of use and intuitive understanding of a dataset;

slice_max(), which returns a data subset that contains the rows with the highest number of values for some particular variable;

slice_min(), which returns a data subset that contains the rows with the lowest number of values for some particular variable.

Built-in datasets

The dplyr package comes with five datasets. These are: band_instruments, band_instruments2, band_members, starwars, storms.        

References
  1. Yadav, Rohit (2019-10-29). "Python's Pandas vs R's Tidyverse: Who Comes Out On Top?". Analytics India Magazine. Retrieved 2021-02-06.
  2. Krill, Paul (2015-06-30). "Why R? The pros and cons of the R language". InfoWorld. Retrieved 2021-02-06.
  3. "Introducing dplyr". blog.rstudio.com. Retrieved 2020-09-02.
  4. "Function reference". dplyr.tidyverse.org. Retrieved 2021-02-06.
  5. Grolemund, Garrett; Wickham, Hadley. 5 Data transformation | R for Data Science.
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