Data Structures in Computer Science

A data structure is chosen for efficient access to data and serves as the basis for abstract data types (ADT).

Different types of data structures are suited to different kinds of applications, with some highly specialized to specific tasks.

Efficient data structures are key to designing efficient algorithms.

Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design.

Data structures provide a means to manage large amounts of data efficiently for uses such as large databases and internet indexing services.

The purpose of the dplyr package in R is to provide convenient tools for the most common data manipulation tasks, making tabular data manipulation easier and enabling work with data in tidy data formats.

The tidyverse package is an 'umbrella-package' that installs a series of packages useful for data analysis in R, and dplyr is one of the core packages within tidyverse. It works together well with other packages like tidyr and ggplot2 for efficient data manipulation, modeling, and visualization.

Hadley Wickham defines 'tidy datasets' as easy to manipulate, model, and visualize, with a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. Tidy datasets are significant for their easy manipulability and visualization.

Some additional packages installed with tidyverse are lubridate (for working with dates) and haven (for SPSS, Stata, and SAS data). These packages serve specific purposes and are useful for certain types of data analysis.

dplyr is considered one of the core packages in tidyverse because it provides convenient tools for common data manipulation tasks, making it likely to be used in almost every data analysis alongside other core packages like tidyr and ggplot2.