Trending March 2024 # Creating A Jitter Plot Using Ggplot2 In Rstudio # Suggested April 2024 # Top 4 Popular

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The ggplot2 package is the most comprehensive way of building graphs and plots. Firms, like the New York Times and The Economist, are heavily using ggplot2 to create their visualizations. With big companies using this tool, it’s important to have a knowledge base on how to use ggplot2 to create visualizations such as the jitter plot.

In this tutorial, you’ll learn how to create a jitter plot using ggplot2 in RStudio. Once you understand the grammar of graphics in ggplot2, you’ll be able to string together any graph or plot.

A jitterplot is a type of scatter plot used to display the distribution of a set of numerical data points. The “jitter” in the plot’s name refers to the random variation that is added to the position of each symbol along the x- and y-axes.

This variation helps prevent symbols from overlapping and makes it easier to see the distribution of data points in cases there is high density of points in certain areas of the plot.

If you have a densely populated plot, a jitterplot can make your visualization easier to understand. You can also use it to plot distributions by category, which is an alternative to a box plot or a histogram.

For this demonstration, the tidyverse dataset is used.

First, create a scatter plot using the ggplot ( ) function. In this case, the x-axis is the year while the y-axis is the mpg dataset.

When you run the code, you can see that the plot shows points forming a straight line with respect to the y-axis.

Use the geom_jitter ( ) function to add another layer to the graph. When you run the code, you’ll see that the points in the plot shifted. The points will continue to shift every time you run the code.

You can also use the geom_jitter ( ) function for categorical variables.

Using the same argument, let’s change the x-axis to mpg and the y-axis to origin. When you run the new line of code, you can see that instead of showing the data in straight lines, they’re randomly distributed in the plot.

This helps you visualize the individual observations for each category and how they vary. In this case, you can see the typical mileage of one origin versus another.

You can add color to the plot by adding another argument in the aes ( ) function. You can also set the size of the points to a specific data value in your dataset.

In this example, the jitter plot made it easier to identify the origins with the most cars and those that have better mileage.

Because of the size set in the code, the plot looks oversaturated. You can change the size or color of the data points depending on your preference or business requirements.

A jitter plot is one of the ways to bring a new form of insight in your visualizations. It helps users to better understand what’s happening with in data. This plot is a great alternative to the typical histogram or box plot for plotting distributions.

The ability to effectively understand the underlying structure of a dataset makes jitter plots a valuable tool in various fields such as statistics, data analysis, and machine learning. Overall, jitter plots provide a clear and easy-to-understand representation of the distribution of numerical data points, making it a powerful tool for data visualization and analysis.

All the best,

George Mount

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Create A Plot With Multiple Glyphs Using Python Bokeh

Bokeh is a powerful data visualization library in Python that helps to create interactive and unique visualizations for the web. Bokeh supports various rendering techniques and provides a wide range of built-in tools for creating complex visualizations with multiple glyphs. This document will guide you through the process of creating a plot with multiple glyphs using Bokeh. This plot combines different glyphs to display multiple data series in a single plot that provides a more efficient way to understand the relationship between different variables.

Glyphs are graphical representations of characters, symbols, or icons used in typography and graphic design. They are often used in the design and layout of text, and can include letters, numbers, punctuation marks, and other symbols.

Improved legibility − Glyphs can be designed to be highly legible, making it easier for readers to quickly and accurately understand the text.

Enhanced aesthetics − Glyphs can be used to add visual interest and appeal to text, making it more visually appealing and engaging.

Consistency and accuracy − Glyphs can be designed to be consistent in size, shape, and style, ensuring that text is easy to read and visually coherent.

Flexibility − Glyphs can be scaled and modified easily, making it possible to use them in a wide range of contexts and applications.

Internationalization − Glyphs can be used to represent characters and symbols from a wide range of languages and writing systems, making them useful for internationalization and localization.

Overall, glyphs are a powerful tool for typography and graphic design, and can help improve the legibility, aesthetics, consistency, and flexibility of text.

Statistical Significance of these

Glyphs themselves are not subject to statistical significance tests since they are not statistical data. However, the use of glyphs in typography and graphic design may be subject to statistical significance tests if they are used in the context of an experiment or study that involves statistical analysis. For example, if a study is examining the effects of different fonts on reading speed or comprehension, statistical tests may be used to determine whether any observed differences between the fonts are statistically significant.

In general, statistical significance tests are used to determine whether observed differences or effects are likely to be due to chance or random variation, or whether they are likely to reflect a true difference or effect in the population being studied. The specific test used depends on the research question, the type of data being analyzed, and the assumptions made about the data and population.

Therefore, while glyphs themselves are not subject to statistical significance tests, they may be used in the context of experiments or studies that are subject to statistical analysis to determine whether any observed differences or effects are statistically significant.

Prerequisites

Before we dive into the task few things should is expected to be installed onto your system −

List of recommended settings −

pip install pandas, bokeh

It is expected that the user will have access to any standalone IDE such as VS-Code, PyCharm, Atom or Sublime text.

Even online Python compilers can also be used such as chúng tôi Google Cloud platform or any other will do.

Updated version of Python. At the time of writing the article I have used 3.10.9 version.

Knowledge of the use of Jupyter notebook.

Knowledge and application of virtual environment would be beneficial but not required.

It is also expected that the person will have a good understanding of statistics and mathematics.

Creating a basic plot

To create a plot, we first need to import the necessary modules, such as `Figure`, `ColumnDataSource`, and the desired glyphs. Here’s an example code snippet that creates a line plot with a single glyph using Bokeh −

Syntax from bokeh.plotting import figure, output_file, show output_file("line.html") p = figure(title="Line Plot", x_axis_label="X", y_axis_label="Y") x = [1, 2, 3, 4, 5] y = [6, 7, 2, 4, 5] p.line(x, y, line_width=2) show(p) Output

This code will create a line plot with x-axis labeled as “X”, y-axis labeled as “Y”, and a title “Line Plot”. The line plot will display five data points with their corresponding x and y values.

Adding multiple glyphs to the plo

To add multiple glyphs to the plot, we need to use the `Figure` object’s `multi_line()` function. The `multi_line()` function takes multiple sequences of x and y values and creates a line glyph for each of them. Here’s an example code snippet to create a line plot with multiple glyphs −

from bokeh.plotting import figure, output_file, show from bokeh.models import ColumnDataSource output_file("multi_line.html") p = figure(title="Multiple Glyphs", x_axis_label="X", y_axis_label="Y") x1 = [1, 2, 3, 4, 5] y1 = [6, 7, 2, 4, 5] x2 = [1, 2, 3, 4, 5] y2 = [2, 4, 6, 8, 10] source = ColumnDataSource(data=dict(x1=x1, y1=y1, x2=x2, y2=y2)) p.multi_line(xs=[source.data["x1"], source.data["x2"]], ys=[source.data["y1"], source.data["y2"]], line_color=["red", "blue"], line_width=[2, 2]) show(p) Output

Here, we created two sets of x and y values and stored them in a `ColumnDataSource` object. We then passed the two sequences of x and y values to the `multi_line()` function, along with the colors and line widths of the two glyphs. This will create a line plot with two glyphs, one in red color and one in blue color, each with their corresponding x and y values.

Final Program, Code # Basic plot from bokeh.plotting import figure, output_file, show output_file("line.html") p = figure(title="Line Plot", x_axis_label="X", y_axis_label="Y") x = [1, 2, 3, 4, 5] y = [6, 7, 2, 4, 5] p.line(x, y, line_width=2) show(p) # Multiple graphs from bokeh.plotting import figure, output_file, show from bokeh.models import ColumnDataSource output_file("multi_line.html") p = figure(title="Multiple Glyphs", x_axis_label="X", y_axis_label="Y") x1 = [1, 2, 3, 4, 5] y1 = [6, 7, 2, 4, 5] x2 = [1, 2, 3, 4, 5] y2 = [2, 4, 6, 8, 10] source = ColumnDataSource(data=dict(x1=x1, y1=y1, x2=x2, y2=y2)) p.multi_line(xs=[source.data["x1"], source.data["x2"]], ys=[source.data["y1"], source.data["y2"]], line_color=["red", "blue"], line_width=[2, 2]) show(p) Conclusion

In this document, we have seen how to create a plot with multiple glyphs using Bokeh. We started with an introduction to glyphs and then created a basic line plot with a single glyph. We then added multiple glyphs to the plot using the `multi_line()` function of the `Figure` object. With Bokeh, it is easy to create interactive visualizations that can help to understand the relationship between different data points. Bokeh allows you to create beautiful visualizations with minimal effort, allowing you to focus on analyzing the data rather than worrying about the visualization.

The Difficulty Of Creating A Paperless Office

I used to have this annual discussion with the now ex-head of the HP Printer unit VJ. He thought it was funny that after we started moving to the “paperless office” in the 1980s, the effort seemed to accelerate, not decelerate, the use of printers. This continued until last decade, or nearly 30 years from the time we started working towards the goal of getting rid of all the paper.

I just finished hosting a talk with Ombud (a company that specializes in sharing best practices between IT shops and within companies) and DocuSign (the leader in digital signatures) on the Affordable Health Care Act. We discussed how going paperless with digital signatures could help assure compliance and save a ton of money. But shouldn’t we already be using digital signatures?

As a species, we hate change. Our current keyboard layout was designed to keep typewriter keys from jamming (a problem a huge number of people have never seen in their lives). We still use car controls that were outdated decades ago. And we build homes much the same way they did at the beginning of the century, even though we know how to build them to survive the many weather-related events we’ve been having.

But while this change aversion clearly contributed to the issue, the real problem is that we really didn’t have anything that replaced paper well. Tablets did emerge in the 1990s and again a decade later, but they were heavy, expensive things with relatively poor resolution. It wasn’t until the iPad and the Kindle that the market actually started to replace paper with something that was better—at least for consumption. Now companies are pulling back on their printing supplies as more and more folks shift to using their tablets and smartphones for reading reports and filling out documents.

The benefits of digital document management center on three areas: security, tracking and speed. With a paper document, you can’t really track the number of copies (yes there is special paper that can resist copying, but it is hardly foolproof). And if you want the document back, you have to go and get it physically. Once a paper document is out of your control, there really are no limits on who can read it.

As long as a document remains digital and within a document management system, you can track it. You can limit who views it (granted, someone else could be looking at the screen who isn’t authorized). You can reduce the chance of it being copied. And, most important, you can better track document approvals because they stay in system. If you need an approval from someone who is not on-site, they can approve from their cell phone or tablet, moving the process along more quickly.

So while not perfect, electronic documents improve dramatically on the security, tracking and speed surrounding what has traditionally been a highly manual process of shuffling and storing papers.

It has taken a long time to put a stake in paper, but it required that we develop a technology that could actually be as good as or potentially better than a paper document. Tablets, and to a lesser extent, ePaper have arrived to help drive us toward a nirvana of offices without huge filing cabinets and executives who don’t stay awake all night hoping a sensitive document doesn’t show up on the front page of The Wall Street Journal.

As we discovered with my panel, healthcare is aggressively moving to this new paperless model because they have no choice. Privacy regulations and revenue rules are making it critical that they better protect patent information and cut costs in order to survive. Going paperless is one of the many steps they are taking to assure that survival. It has taken us over 30 years, and we clearly aren’t there yet, but finally, I can say, the age of paper is behind us.

Creating Diversity In Your It Staff

Can your IT operations be at their peak without diversity?

Compared to other industries, IT’s record with race-bias lawsuits might

not be the worst, but if CIOs aren’t tracking diversity, their IT

All things being equal in terms of skills and abilities, IT staffs that

are racially, nationally, and gender diverse build better software and

attract more customers than non-diverse organizations.

So why don’t more CIOs make diversity planning a higher priority for

their technology organizations?

It could be the belief that IT organizations don’t discriminate much on

the basis of racial lines. Data from a U.S. Equal Employment Opportunity

Commission (EEOC) investigation released in 2002 found that IT

organizations don’t suffer much from racial bias. Only 2 percent of

race-based complaints to the EEOC were from technology companies.

However, it also could be the belief that IT workers spend more time

communing with their computer screens than they do with their colleagues,

making talent the fundamental basis for staffing and salary decisions.

Whatever the reasoning, say experts, CIOs shouldn’t pay attention to

diversity simply out of fear of a racial-bias lawsuit — such as the $5

billion case brought against Microsoft in 2001 by some of its

African-American workers. CIOs, obviously should never discriminate, but

they also should pay attention to diversity because it makes them more

competitive.

”It’s really hard to find a non-diverse environment that survives,”

says Hamid Alipour, vice president of Technology and Systems at New

York-based ESPN Mobile, which brings ESPN’s content to mobile devices.

That’s because a diverse IT group draws from more cultural perspectives

in creating software to serve an increasingly diverse marketplace.

”It’s definitely very critical… Just imagine if you are all white male

Americans and you were to [focus on] a one-dimensional kind of IT,

serving perhaps that very category or class of society that we have

recruited from,” adds Alipour.

In ESPN’s case, having such a homogenous workplace could turn off

millions of customers. The company has viewers from different races and

nationalities in more than 60 countries with 90 million viewers in the

U.S. alone. Many of ESPN’s viewers (and mobile device users) are

Hispanic and African American, says Alipour. So Alipour wants a diverse

IT group in order to design better user interfaces, for instance, that

will appeal to a demographically diverse audience.

Recognizing the importance of a multi-cultural workplace goes beyond just

corporate America.

Technology membership organizations such as Black Data Processing

Associates (BDPA) and the IEEE-USA are working for diversity because, in

a general sense, many IT shops don’t have the data to support that

they’re doing anything at all about diversity.

”Every company has given good lip service to the idea that diversity is

important,” says Wayne Hicks, BDPA national president, and president and

CEO of Cincinnati Business Incubator. ”What (the BDPA) is hoping is that

companies will recognize that we don’t think your company can be

successful moving into the 21st century if you don’t have this as part of

your culture.”

In IEEE’s case the Washington, D.C.-based organization wants to foster

diversity in corporate America and among its membership.

”Representation of blacks in the IEEE membership is in the single

digits, and in most engineering societies, it’s pretty low,” says Pender

M. McCarter, director of communications and public relations at IEEE-USA.

McCarter works on career and technology enhancement policy for the IEEE,

the world’s largest technology association, and also sits on the

diversity committee of the American Association of Engineering Societies.

What can CIOs do to improve diversity?

”[CIOs] don’t need permission from anyone to [take charge of diversity].

They are in control of their IT operations, including their IT

workforce,” says Hicks. Take leadership and make the managers within the

IT department accountable for creating a diverse workforce.

Create metrics that track diversity. The top five best practices that

encourage diversity, according to a National Urban League Study called

Diversity Practices that Work, conducted by Global Lead Management

Consulting of Baltimore, are:

Market to diverse customers and consumers;

Retain diverse talent;

Recruit diverse talent;

Make sure leadership is committed and involved, and

Watch Designers Creating Prototypes In Apple’s Studios

Apple yesterday announced a $300 coffee table photo book, titled “Designed by Apple in California”, which chronicles 20 years of iconic product designs. Jony Ive, Apple’s Chief Design Officer, discussed the photo book with chúng tôi and Japanese design firm Casa Brutus.

The Casa Brutus interview is accompanied by a rare video which offers a unique glimpse into Apple’s secretive Industrial Design Studio, showing designers at work creating prototypes of Macs, iPhones and other items.

Want to take a peek at how the sausage is made? This is your chance!

The Jony Ive-narrated clip explains how Apple’s designers nurture ideas.

One of the things that we’ve learned is the importance of listening. Because as we all know, the very best ideas can very often come from the quietest voice. Ideas are extremely fragile. Ideas are not predictable in terms of when you’ll have them and how many you are going to have.

And so over the years, we’ve really created at team and an environment that I think really increases the probability of good ideas and when they actually arrive I think nurtures them.

And here’s the video.

The opening shot of Highway 280, running between San Francisco and Cupertino, where Apple is headquartered, is there for a reason: Ive wrecked his Aston Martin a few years back driving home on the 280.

More important than that, the footage shows off Apple’s expensive prototyping machinery and designers who can be seen pouring over product prototypes and sketching future product ideas. For those wondering, they prefer hardbound Cachet sketchbooks by Daler-Rowney, a small U.K. company.

Other scenes show designers using workstations equipped with iMacs and Mac Pros.

In addition the expensive CNC milling machines, we see a well-equipped shop where prototypes are being built, product presentation tables, the CAD room where they create 3D models of prototypes, the so-called “dirty shop” sealed behind glass, the workshop, the paint spraying booth, the color studio and more.

Apple’s tight-knit design shop counts about two-dozen employees. “We’re a small design team who have worked together for 20, 25 years,” Ive says in the video.

One of the scenes shows veteran Apple designers Peter Russell-Clarke and Daniele de Iuliis checking out the parts of a unibody MacBook, as well as Richard Howarth who was named the new head of the Industrial Design Studio after Ive got promoted to Chief Design Officer.

Fun fact: nearly all of the chairs in the design studio are Supporto Chairs from a British company called Hille International. Hungry for more? Check out last year’s 60 Minute episode that went inside Ive’s design bunker at Apple’s Cupertino headquarters.

“This archive is intended to be a gentle gathering of many of the products the team has designed over the years”, Ive said of the new photo book.

“We hope it brings some understanding to how and why they exist, while serving as a resource for students of all design disciplines.”

For those interested, Apple’s product design photo book is now available to purchase through Apple․com in Australia, France, Germany, Hong Kong, Japan, Korea, Taiwan, the United Kingdom and the United States.

The book is priced at $199 for the small version (10.20″ x 12.75″) and $299 for the larger variant (13″ x 16.25″). Both sizes ship in one business day.

Some folks are balking at these prices, but this is no ordinary “book”.

In case you didn’t know, high-end coffee table books cost hundreds of dollars. The James Bond Archives photo book from Taschen is a $200 value while the collector’s edition version of The Making of Stanley Kubrick’s “2001: A Space Odyssey” originally sold for a whopping $1,250.

As Brian Fagioli of BetaNews put it succinctly, this is not some book you will bring into the bathroom to read on the toilet.

“It is intended to be a collectible piece of art,” he wrote. “You don’t evaluate the value of a Picasso painting by adding up the cost of the ink and canvas. This is a collection of Andrew Zuckerman photographs meant to be appreciated beyond raw materials.”

If you think $200 is too high a price to pay for Apple’s book, you probably haven’t bought a photo book before. Besides, how much is your average college textbook?

Source: Casa Brutus

Dynamically Creating Keys In Javascript Associative Array

In this article, we are going to discuss how to create keys in a JavaScript associative array dynamically.

Associative arrays are dynamic objects that are user defined as needed. When you assign values to keys in a variable of types array, then the array is transformed into an object, and it loses the attributes and methods or array. i.e. the length attributes have no effect because the variables are no longer of the type array.

JavaScript associative arrays are same as any other literals. You can add keys to these using the square brackets notation you can add keys to these objects dynamically if the key is a string.

We will demonstrate all that and also see how to add a key method to an object to have the number of items it holds when it becomes an associative array.

Creating an Associative Array Dynamically

We can create the dynamic associative array by simply allocating a literal to a variable. Following is the syntax to do so −

var name_of_the_array = {"key1": value1, "key2": value2, "key3": value3}; Example 1

In the following example, we are trying to create an array. We need to use square brackets in the array but, since these are associative arrays we are using curly brackets instead of square brackets. We can access the contents of an associative array using the key.

var array = {“one”: 1, “two”: 2, “three”: 3}; var val = array[“two”]; document.write(JSON.stringify(val));

Example 2

Following is another example to create an associative array −

let a = { name: ‘Ayush’ }; let key = ‘age’; a[key] = 35; document.write(JSON.stringify(a));

Using Object Methods

An associative array is also an object. So, we can create it with the help of object methods, then assign keys and values.

Example 1

In the following example, we demonstrate how we can create an associative array through the object() method.

var array = new Object(); array[“Aman”] = 22; array[“Akash”] = 23; array[“Rahul”] = 24; var i = 0; for (i in array) { }

Example 2

Let us rewrite the above example using the object’s DOT methods.

var array = new Object(); chúng tôi = 22; array.Akash = 23; array.Rahul = 24; var i = 0; for (i in array) { }

Using for…in loop

Since the associative array is similar to an object we cannot use the for loop. Instead, we can use the for…in loop same as we do to traverse the elements of an object.

Unlike normal arrays, associative arrays do not have any method to get the length of an object. Hence, for this purpose, we need to create a user defined method explicitly.

Example

In the following example, we are calculating the size of an associative array

var array = new Object(); chúng tôi = 22; array.Akash = 23; array.Rahul = 24; var count = 0; for (var key in array) { if (array.hasOwnProperty(key)) { count++; } } document.write(“Size of an Associative array: ” + count);

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