R Programming Flashcards

Flashcards covering key concepts in R programming for data science.

R

An open-source programming language specifically designed for statistical computing and data visualization.

Primary purposes of R in data science

Data manipulation, statistical modeling, and graphical representation.

Atomic vectors

The simplest data structures in R; they are homogeneous.

Types of atomic vectors

Logical, Integer, Double, Character, Complex, Raw.

List in R

A versatile data structure that can contain elements of different types.

Difference between a list and an atomic vector

Unlike atomic vectors, lists allow for heterogeneous elements.

Data frame in R

A table-like structure where each column can contain different types of data.

Matrix in R

A two-dimensional array that can only contain one data type throughout.

Packages in R

Collections of R functions, data, and compiled code bundled together.

How to install and load packages in R

They are installed using the install.packages("package_name") command and loaded using library(package_name).

ggplot2

R package used for creating high-quality, customizable visualizations using the grammar of graphics framework.

How ggplot2 improves data visualization

Allows users to construct plots layer by layer, making the visualizations more intuitive and informative.

dplyr

An R package designed for data manipulation.

Common dplyr functions

Functions like filter(), select(), mutate(), and summarise() help streamline data analysis workflows.

RStudio

An integrated development environment (IDE) for R that provides a user-friendly interface.

Features of RStudio

It includes features like a script editor, console, environment viewer, and file browser.

R Markdown

A file format that allows users to combine narrative text, code, and outputs in a single document.

Use of R Markdown

Creating dynamic reports, presentations, and reproducible research.

Tidyverse

A collection of R packages that share an underlying philosophy and common data structures for data science.

Key packages in the Tidyverse

ggplot2, dplyr, tidyr, readr, purrr, and tibble.