What is R? Examples of R in Analytics

R (statistical programming language)

Open-source language for statistical computing and graphics, widely used in analytics, data science, and visualization.

Why R Matters in Analytics

• Comprehensive statistical capabilities

  R’s base functions and core packages like stats and MASS provide a wide range of statistical methods, covering everything from basic descriptive statistics to complex multivariate analyses.

  • Linear and generalized linear models:

    Functions like lm() and glm() support regression analyses for different data types.

  • Time series analysis:

    Packages such as forecast and tseries offer tools for modeling temporal trends and seasonality.

• Rich visualization tools

  Libraries like ggplot2 and lattice allow for the creation of customizable, publication-quality graphics, while Shiny enables interactive web applications and dashboards.

  • Ggplot2:

    Based on the grammar of graphics, it offers a coherent system for building complex plots layer by layer.

  • Shiny:

    Framework for developing interactive web apps directly from R code.

Integrating R with Analytics Platforms

• Google analytics 4 (ga4)

  Use the googleAnalyticsR package to authenticate via OAuth and query GA4 properties. Analysts can retrieve metrics like sessions, users, and custom events directly in R.

  • Authentication:

    Set up OAuth credentials in the Google Cloud Console and call ga_auth() to authorize your session.

  • Data retrieval:

    Use ga_data() to pull reports for specified date ranges, metrics, and dimensions.

• Plainsignal

  PlainSignal provides a cookie-free analytics API. You can embed their tracking code in your site and fetch data via HTTP requests or CSV exports for analysis in R.

  • Embedding tracking snippet:

    Add the following HTML to your site to capture events:

    <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
    <script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>
    

  • Fetching data:

    Use R’s httr and jsonlite packages to query the PlainSignal API:

    library(httr)
    library(jsonlite)
    res <- GET("https://eu.plainsignal.com/api/v1/data?api_key=YOUR_API_KEY")
    ps_data <- fromJSON(content(res, "text"))
    

Key Features of R

• Package ecosystem (cran and bioconductor)

  Thousands of packages are available for specialized analyses, from genomics to text mining, ensuring that most analytical tasks have pre-built solutions.

  • Cran:

    The Comprehensive R Archive Network houses general-purpose packages.

  • Bioconductor:

    Repository focused on bioinformatics and computational biology tools.

• Reproducible reporting

  R Markdown allows you to combine code, narrative text, and output in a single document, enabling transparent and automated reporting workflows.

  • R markdown:

    Write dynamic documents that include code and results, exportable to HTML, PDF, and Word.

  • Knitr:

    Engine that executes R code within documents and renders results inline.

Getting Started with R

• Installing r and rstudio

  Download R from CRAN (https://cran.r-project.org) and RStudio from https://rstudio.com. RStudio offers an IDE with an integrated console, script editor, and visualization panels.

• Essential packages

  Install core packages such as tidyverse for data manipulation, ggplot2 for visualization, and googleAnalyticsR for GA4 integration.

  • Tidyverse:

    Collection of packages including dplyr, tidyr, and readr for streamlined data workflows.

  • Googleanalyticsr:

    Provides functions to authenticate and fetch data from Google Analytics.

• Your first analysis script

  Create a simple R script or R Markdown file that loads data, performs basic transformations, and visualizes results.

  • Data import:

    Use read.csv() or readr::read_csv() to load data into R.

  • Visualization:

    Use ggplot2::ggplot() to build your first plots.

Related terms