Introduction to Dplyr
Recognized as "dee-ply-er", dplyr is a premier tool in R for data manipulation. Mastery of dplyr can significantly reduce the time data scientists spend on data handling and preparation, making their tasks more understandable. Dplyr is frequently engaged in reforming preexisting datasets into a format that's more fitting for analysis or data representation. Released in 2014 in R, dplyr is a primary R tidyverse package. Its founding creator, Hadley Wickham, terms it as the 'grammar of data manipulation', as it comprises a suite of verbs (functions) for defining and executing routine data preparation tasks.
The Grammar of Data Manipulation
One of the main challenges in programming is bridging queries about a data set with specific machine procedures. The availability of a grammar for data manipulation eases this process, as the same language can be utilized for both querying and coding. Distinctively, the dplyr syntax simplifies articulating and performing these tasks:
- Selecting only the necessary columns from a dataset for query resolution,
- Filtering out unnecessary data and preserving only pertinent observations based on stated criteria,
- Altering a data set by incorporating additional attributes (mutate),
- Organizing observations in a specific sequence,
- Summarizing data using aggregation techniques like median, mean, and maximum,
- Merging disparate datasets into a unified table.
Using this language, one can articulate their data querying method or process, and the dplyr will generate code akin to the 'plain English' explanation due to the congruous language used by the functions and procedures. Indeed, many practical queries regarding data sets can be resolved by identifying select rows/columns as 'objects of interest' and carrying out a basic comparison or calculation. While such computations are achievable via base R functions, dplyr functions in R markedly facilitate creating and understanding such code.
Getting Started with Dplyr
To start with dplyr, note that it's an external package that must be installed and loaded in each script where its functions are in use. Installation is a one-time per machine process, while loading is script-specific.
install.packages("dplyr") # once per machine
library("dplyr") # in each relevant script
Its functions can then be utilized like any built-in ones. Other tidyverse collection packages can be installed by importing the tidyverse package.
Key Functions and Extensions
Dplyr simplifies data manipulation by providing a consistent set of verbs to navigate common data manipulation challenges. It introduces new variables that are functions of existing ones with mutate(), selects variables based on their names with select(), filters cases based on their values with filter(), summarizes multiple values into a single one with summarize(), and modifies the rows' order with arrange().