pujapdas
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2 months ago Puja Das M.Des. by Research, 24M2526
Complete 'Introduction to R Base Graphics' Presentation
R Base Graphics
R is an open-source software environment for statistical computing and graphics. R Base Graphics is the default visualization system built into the R language.
Introduction
- Things you can do with R: Clean Data (Changing Variable Name and Type, Filter Rows, Recode Data), Manipulating Data, Visualizing Data, Analyze your Data (Hypothesis Testing, T-Test, ANOVA, Linear Model), etc.
- There are three main systems for producing graphics in R; base R graphics,
latticegraphics andggplot2. - R Base Graphics allows for quick, customizable, and straightforward plots.
- Provides a procedural approach, where each component of the graph is added step by step.
- Suitable for creating line plots, scatter plots, histograms, bar charts, and more.
- Plots can be saved in a variety of formats (pdf, png, tiff, jpeg, etc.) by clicking on the ‘Export’ button and selecting your desired format.
Installation
To get started:
R Base Graphics is already included with R, so no additional installation is required.
Demonstration
Complete 'Introduction to R Base Graphics' Presentation
Possibilities
- Full control over plot customization: colors, lines, shapes, fonts.
- Can create a variety of plots: scatter plots, bar charts, histograms, etc.
- Efficient and fast for creating quick visualizations.
- Procedural approach allows step-by-step control over plot elements.
Limitations
- Steeper learning curve compared to more modern tools like
ggplot2. - Default aesthetic may appear outdated.
- Limited interactivity: not suitable for dynamic or interactive visualizations.
- Need to make many decisions about how you want your plot to look rather than rely on the software to make these decisions for you.
- Once created, difficult to modify individual components without redrawing the entire plot.
References
- Douglas, A. (n.d.). An Introduction to R. An Introduction to R. Retrieved 17 September 2024, from https://alexd106.github.io/intro2R/
- Getting started with Data Visualization in R. (2020, April 12). GeeksforGeeks. https://www.geeksforgeeks.org/data-visualization-in-r/
- Holtz, Y. (n.d.). Base R Graph Parameters: A Cheatsheet. The R Graph Gallery. Retrieved 17 September 2024, from https://www.r-graph-gallery.com/6-graph-parameters-reminder.html
- R Get Started. (n.d.). W3Schools. Retrieved 17 September 2024, from https://www.w3schools.com/r/r_get_started.asp
- R Graphics—Bars. (n.d.). W3Schools. Retrieved 17 September 2024, from https://www.w3schools.com/r/r_graph_bars.asp
- R Graphics—Plotting. (n.d.). W3Schools. Retrieved 17 September 2024, from https://www.w3schools.com/r/r_graph_plot.asp
- R Graphics—Scatter Plot. (n.d.). W3Schools. Retrieved 17 September 2024, from https://www.w3schools.com/r/r_graph_scatterplot.asp
- R Introduction. (n.d.). W3Schools. Retrieved 17 September 2024, from https://www.w3schools.com/r/r_intro.asp
- R: What is R? (n.d.). The R Project for Statistical Computing. Retrieved 17 September 2024, from https://www.r-project.org/about.html
- ScottPlot 4.1 Cookbook. (n.d.). ScottPlot.NET. Retrieved 17 September 2024, from https://ScottPlot.NET
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In this assignment each group will explore a dataviz tool (download, install, try out, and use it to create something), and present a brief demonstration/overview of the tool to the rest of the class (max 10 minutes).
The objective is to introduce the tool to the class, and highlight its possibilities & limitations so the audience gets a good overview of the tool.
In your documentation, include links to the resources you used (if any) in your presentations, capturing your personal insights about the tool and related resources.
Observable -- This is a good tool to build interactive exploration and visualization to quickly answer questions and nurture understanding from data. Chart.js -- Simple, flexible JavaScript charts. RAW Graphs -- Copy/paste the relevant data directly from your spreadsheet program into RAW, choose a data visualization type, and set your parameters using a drag-and-drop interface. Each individual parameter or visual metric can be adjusted, and the interface is clean and intuitive, making it ideal for beginners. Tableau Public -- A free platform to explore, create and publicly share data visualizations online. Voyant Tools -- A web-based reading and analysis environment for digital texts. Timeline.js -- This is a powerful free tool developed by Northwestern University’s Knight Lab that helps you create engaging, timeline-based visuals to show off your data. Requires no coding. Circos -- Circos visualizes data in a circular layout which makes it ideal for exploring relationships between objects or positions. . Mapbox -- Powerful geo visualization tool for custom maps Datawrapper -- Powerful tool that requires no coding. Leaflet -- Lightweight open-source JavaScript library for mobile-friendly interactive maps. Google Looker Studio -- Free tool from Google and easy to set up if you have a Gmail account. Tangle -- Tangle is a JavaScript library for creating reactive documents. Your readers can interactively explore possibilities, play with parameters, and see the document update immediately. Excellent for instruction design and process explanation projects. P5.js -- P5.js is a JavaScript library that sits on top of the Processing visual programming language. As with most JavaSript libraries, Processing.js is web oriented and lets you bring the Processing power to your web pages. Plotly -- Good suite of visual analytics products. Gephi -- Open source tool for link an network data analysis and visualization. OpenRefine -- Formerly Google Refine, OpenRefine is a powerful tool for working with messy data -- cleaning it; transforming it from one format into another; and extending it with web services and external data. Flourish Studio -- Flourish is a digital tool that helps to create interactive graphs, charts, and unique visualizations from uploaded datasets.
There are more tools here and here. If you want to pick a tool that is not listed above, discuss with me.
For how to document your work, take a look at what the previous batches did with this assignment (https://github.com/info-design-lab/DE705-Interactive-Data-Visualization/issues/3, https://github.com/info-design-lab/DE705-Interactive-Data-Visualization/issues/5, https://github.com/info-design-lab/DE705-Interactive-Data-Visualization/issues/8, https://github.com/info-design-lab/DE705-Interactive-Data-Visualization/issues/10, https://github.com/info-design-lab/DE705-Interactive-Data-Visualization/issues/20).