12.2: R for Statistical Analysis

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Page ID: 48135

Felix Amoruwa
De Anza College

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R for Statistical Analysis

R is a language designed for statistics and data analysis. It has a rich ecosystem of packages (libraries) for data manipulation, visualization, and modeling. The tidyverse collection, in particular, provides a coherent set of packages sharing philosophies and grammar. Key packages include:

dplyr: Grammar for data manipulation (verbs like filter, select, mutate, summarize, group_by).
ggplot2: A system for declarative graphics based on the Grammar of Graphics. It makes it easy to create complex plots by layering.
tidyr: Tools for reshaping data (e.g. pivot_longer, pivot_wider). It helps turn messy data into “tidy” format.
tidyverse: This is a meta-package of core tools (dplyr, ggplot2, tidyr, and more).

Figure \(\PageIndex{1}\): The Tidyverse Ecosystem in R. A hub-and-spoke diagram showing the tidyverse meta-package at the center with its core constituent packages -- dplyr, ggplot2, tidyr, readr, purrr, tibble, stringr, and forcats -- each with a brief functional label. (CC BY 4.0; Felix Amoruwa) Access to a detailed description of Figure \(\PageIndex{1}\)

R also has many specialized libraries for statistics and machine learning (e.g. caret, randomForest), time series, bioinformatics, etc. However, the core workflow often revolves around data wrangling (dplyr), visualization (ggplot2), and modeling (stats or ml packages).

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