world = FileAttachment("countries-110m.json").json()

countries = topojson.feature(world, world.objects.countries)

score = new Map((await FileAttachment("rusers_rank2015.csv").csv()).map(d => [d.id, +d.score]))

rank = new Map((await FileAttachment("rusers_rank2015.csv").csv()).map(d => [d.id, +d.rank]))

city = [{ name: "Auckland",  lat: -36.848461, lon: 174.763336 }]

angleScale2 = d3.scaleLinear()
  .domain([0, 6])
  .range([-180, 180])
  .clamp(true)

angle2 = angleScale2(
  (crTriggerIndex != null ? crTriggerIndex : -1)
    + crTriggerProgress)

You Curious About ? Here’s Everything You Need To Know!

If you are seeking a programming language that combines powerful statistical analysis, elegant data visualization, and flexibility, is a good choice for you.

But have you ever wondered how becomes the go-to tool for data scientists and statisticians worldwide?

Hence, let’s explore the journey of from a niche tool to a global phenomenon. Whether you’re a beginner or just curious about its story, this has everything you need to know to get started with

has become one of the most widely used programming languages across the globe, thanks to its power in data analysis, visualization, and statistical computing. The number of users has grown exponentially, solidifying its position in academia, research, and industry.

This map shows the popularity score of worldwide, which is the scale average of the number of the R Foundation members and the R User Groups members, the R conference attendees, the R-bloggers.com visits and the CRAN downloads per 1,000 people using data up to 2015. users are primarily located in countries across Europe, North America, and Oceania.

What many may not know is that actually originates from Auckland - the capital of New Zealand.

was created by two professors, Ross Ihaka and Robert Gentleman, from the University of Auckland in the early 1990s.

Inspired by S programming language, this pioneering duo developed a language to teach introductory statistics at the University of Auckland and named it after the initial of their first names.

Their vision of a free and open-source platform for statistical computing has since empowered countless data enthusiasts and professionals worldwide, creating a vibrant community that continues to innovate and expand the boundaries of .

One of the key drivers behind this innovation is the package ecosystem of . But what exactly is an R package? Think of it as an extension or add-on for , created by the community to solve specific problems or enhance functionality. Each package bundles together reusable functions, data, and documentation, making it easier for users to tackle tasks without reinventing the wheel.

With over 20,000 packages available on CRAN (Comprehensive R Archive Network), the functionality of is virtually limitless. Let’s explore some game-changing and interesting packages.

tidyverse is one of the most popular packages, which is a collection of packages designed for data science, including: ggplot2, readr, tidyr, purrr, dplyr, stringr, tibble. All packages follow a consistent design philosophy, grammar, and data structures.

tibble is a modern take on classic data frame in . Tibbles keep what works and fix what doesn’t. For example, tibbles don’t automatically change variable names or data types, and they flag potential issues early. This helps you write cleaner, more reliable code. Additionally, tibbles come with an improved print() function, making it easier to work with large datasets or complex objects.

ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

readr is designed to make importing data into fast and hassle-free. It works great with delimited files such as comma-separated values (CSV) and tab-separated values (TSV) and provides helpful feedback if something goes wrong. Whether you’re working with messy data or clean tables, readr simplifies the process.

tidyr package helps organize data into a “tidy” format, where:
- Each variable is a column; each column is a variable.
- Each observation is a row; each row is an observation.
- Each value is a cell; each cell is a single value.
Tidy data makes the analysis easier, more intuitive, and compatible with the rest of the tidyverse tools.

purrr expands the functional programming (FP) toolkit in by offering a complete and consistent set of tools for working with functions and vectors. It’s great for working with functions and vectors. One of its standout features is the map() family of functions, which can replace repetitive loops with simpler and more comprehensive code.

forcats is a powerful tool for managing and manipulating factors, which is the way of handling categorical data in . It simplifies tasks like reordering, renaming, and reorganizing factor levels, making it easier to analyze or present categorical variables effectively. Whether it’s cleaning messy data or fine-tuning visualizations, forcats simplifies the process of handling categories with ease and precision.

dplyr is a toolkit for data manipulation, providing a consistent set of functions that help handle common data wrangling tasks. Its clean syntax ensures the readability and efficiency of the codes.

stringr streamlines working with text data in , providing a consistent set of functions for tasks such as searching, replacing, and splitting strings. When dealing with messy text data, stringr efficiently reduces effort and saves time.

As an interpreted language, operates through a native command-line interface (CLI), offering users direct access to its powerful capabilities for data analysis and statistical computing. While the CLI is effective for experienced users, it can be challenging for beginners or those working on more complex projects. This is where RStudio, an integrated development environment (IDE) for , comes in. RStudio enhances the experience of using by providing a user-friendly interface with a suite of tools that streamline the workflow for data analysis.

One of RStudio’s standout features is its robust support for R Markdown, which allows users to create dynamic, reproducible analyses and share results in a variety of formats, such as HTML, PDF, and Word. RStudio’s integration of these tools has enhanced the ability to organize, visualize, and analyse data, which promotes the efficiency, reproducibility and transparency in research and professional work.

But is more than just a language or a tool, it is a community. enthusiasts connect through global groups, conferences, and meetings such as posit::conf, R-Ladies, and useR!. This collaborative and innovative spirit has encouraged a culture of knowledge sharing and mutual support, driving the continuous evolution of .

So you ready to unlock the full potential of ? Let’s install today and become part of a dynamic community that’s driving the future of data science!

Plot.plot({
  projection: { type: "orthographic", rotate: [angle2, -10] },
  color: {
    type: "quantize",
    scheme: "blues",
    label: "Popularity Score",
    legend: true
  },
  marks: [
    Plot.geo(countries, {
      fill: d => score.get(d.id),
      tip: true,
      channels: {
        Country: d => d.properties.name,
        Rank: d => rank.get(d.id)
      }
    }),
    Plot.sphere(),
     Plot.dot(city, {
      x: "lon",
      y: "lat",
      fill: "red",
      paintOrder: "stroke",
      r: 0.75
    }),
    Plot.text(city, {
      x: d => d.lon + 2,
      y: d => d.lat + 2,
      text: "name",
      fill: "#1A2954",
      paintOrder: "stroke",
      fontSize: 10,
      textAnchor: "start"
    })
  ]
});

Ross Ihaka

Robert Gentleman