Reflections on The IS Program

Navigating the Information Studies (IS) coursework has been an intellectually stimulating journey that has broadened my understanding of the ever-evolving world of information science and technology. The program’s comprehensive curriculum has equipped me with the tools and techniques necessary to collect, clean, analyze, and visualize data, enabling me to extract meaningful insights from vast datasets.

Exposure to a diverse range of IS subfields, including data management, data mining, machine learning, and business intelligence, has ignited my curiosity and solidified my passion for harnessing the power of data to drive informed decision-making. Among these areas, predictive analytics holds particular appeal, as it offers the potential to forecast future trends and inform strategic planning, transforming businesses of all sizes.

The IS program has played a crucial role in preparing me for a career in predictive analytics. The rigorous coursework has instilled in me a deep understanding of statistical methodologies, data modeling techniques, and the ethical considerations surrounding data usage. Additionally, hands-on projects have allowed me to apply theoretical knowledge to real-world scenarios, honing my problem-solving and analytical skills.

Despite its strengths, the IS program could benefit from incorporating more in-person elements to foster a sense of community and facilitate deeper learning. Expanding elective course offerings to include more specialized topics in advanced data analytics techniques, such as deep learning and natural language processing, would further enrich the program.

Internships and industry collaborations would provide valuable real-world experience and opportunities to build professional networks. Recognizing the demand for skilled data scientists, I plan to pursue a master’s degree in data science to deepen my understanding of theoretical underpinnings and gain specialized skills to tackle complex data challenges.

With my academic background and future endeavors, I am confident in my ability to make significant contributions to the field of predictive analytics. I am eager to leverage my expertise to help businesses harness the power of data to make informed decisions, optimize operations, and gain a competitive edge.

In summary, the IS program has provided me with a solid foundation in data analytics, preparing me for a successful career in this dynamic field. Incorporating more in-person elements, expanding specialized electives, and fostering industry collaborations would further enhance the program’s effectiveness. I am committed to pursuing further education and professional development to remain at the forefront of data science innovation.

Event Report: The Rise of AI

Introduction

Artificial intelligence, or AI, is making big waves in our world, and it affects many parts of our lives. At a recent event hosted by The Washington Post, we learned about AI from three different angles. In the first part, Congressman Don Beyer shared his own story about how AI is changing jobs, the economy, and our national security. He emphasized the need for continuous learning in the AI age.

The second part had Barbara Humpton and Brian Harrison talking about how AI can help industries, like factories and businesses, become more efficient. They even discussed this exciting idea called the “industrial metaverse” and how companies like Siemens and NVIDIA are working together on it.

In the last part, Jay Lee and Victoria Espinel talked about how we should make rules and agreements about AI. They said it’s essential to work with other countries to make sure AI is used safely and fairly. Together, these three parts show that AI is a big deal, not just for experts but for all of us. It’s changing how we work, how industries run, and how countries cooperate, and it’s a topic that affects us all.

Segment 1: AI Revolution and Its Impact

In the first segment of the Washington Post Live event, Congressman Don Beyer, the vice chair of the Congressional AI Caucus, shed light on the profound impact of artificial intelligence (AI) on various aspects of our lives. As a Democrat from Virginia, Beyer highlighted how AI has the potential to transform the job market, the economy, and even national security. He provided a personal anecdote that resonates with many in this age of AI – his decision to return to school to pursue a master’s degree in machine learning. His journey underscores the importance of continuous learning as a means to adapt and thrive in the ever-evolving landscape of AI.

Beyer’s insights remind us that AI is not merely a technological trend; it’s a force reshaping our world. It’s not only about job displacement but also job creation, where new skills and expertise in AI are in high demand. By sharing his educational journey, Beyer emphasized the significance of staying updated and embracing lifelong learning as we navigate this AI revolution. His words serve as an inspiration to individuals from all walks of life to engage with AI, acknowledging both its challenges and opportunities.

Segment 2: Industrial AI and Its Potential

The event’s second segment featured Barbara Humpton, the CEO of Siemens USA, and Brian Harrison, the senior director of Omniverse Digital Twins at NVIDIA. They engaged in a deep discussion about the strategic application of AI in addressing industrial challenges and bolstering productivity. One of the most intriguing aspects they delved into was the concept of the industrial metaverse, an innovative space that merges the physical and digital worlds. Humpton and Harrison highlighted the collaborative efforts between Siemens and NVIDIA in developing solutions for this evolving landscape.

Their conversation spotlighted how AI is not just a technology for tech enthusiasts; it’s a practical tool that can revolutionize industries. By integrating AI into various sectors, we can optimize processes, enhance efficiency, and drive innovation. The industrial metaverse serves as a visionary example of how AI is more than just lines of code; it’s a transformational force that connects people and industries. The partnership between Siemens and NVIDIA underscores the importance of collaboration in realizing the potential of AI, and it offers a glimpse into the exciting future where AI and industry meet.

Segment 3: AI Policy and Regulation

In the final segment, Jay Lee, the director of the Industrial AI Center at the University of Maryland, and Victoria Espinel, the president and CEO of BSA, provided valuable insights into the current state of AI policy and regulation in both the United States and the global arena. They emphasized the significance of regulation in the context of AI’s rapid expansion. Lee and Espinel discussed the role of the National AI Advisory Committee, a pivotal body driving AI policy discussions. Moreover, they anticipated the upcoming UK AI Summit, highlighting the growing international significance of AI.

Their discussion revealed the complexities and challenges surrounding AI policy and regulation. As AI becomes increasingly integrated into our lives, there’s a pressing need for a clear and comprehensive regulatory framework. Lee and Espinel stressed the importance of international cooperation in developing these regulations to ensure that AI benefits everyone and remains ethical and accountable. Their insights underscore the intricate web of policy and cooperation that surrounds the AI landscape, reflecting the growing realization that AI’s potential can only be harnessed responsibly through effective governance on a global scale.

Conclusion

In summary, the Washington Post Live event provided a comprehensive look at the far-reaching influence of artificial intelligence (AI). Congressman Don Beyer’s personal journey highlighted the need for continuous learning in the AI era, while Barbara Humpton and Brian Harrison’s discussion showcased the practical applications of AI in industries and the promising concept of the industrial metaverse. Jay Lee and Victoria Espinel emphasized the importance of international cooperation in shaping responsible AI policies.

These insights underscore that AI is not just a buzzword; it’s a transformative force reshaping jobs, industries, and global cooperation. As AI continues to evolve, embracing it with a spirit of adaptability and cooperation will be essential to harness its full potential and ensure its responsible and equitable use.

References

Washington Post Live. (2023, October 16). Transcript: Futurist on the Rise of AI. https://www.washingtonpost.com/washington-post-live/2023/10/16/transcript-futurist-rise-ai/

Information Technology Events in the News

Introduction: The Future of Automotive Technology

The automotive industry is swiftly advancing, promising a future where vehicles transcend their conventional roles, becoming sophisticated platforms of technology. This transformation, driven by rapid technological progress, holds the potential to create safer, smarter, and profoundly interconnected automobiles. However, as we step into this era of technological abundance, we must confront the accompanying challenges in information policy. The digitization and automation of vehicles, the focal point of this essay, represents a promising frontier for innovation while demanding a meticulous approach to security and privacy. This narrative delves into the heart of this transformative journey, exploring potential implications, evaluating key stakeholders, and advocating for vital policy measures to balance progress with the imperative of safeguarding both data and individuals.

Body: Unveiling the Technological Evolution of Vehicles

The rapid advancements in technology are steering a transformative wave within the automotive industry, presenting a compelling vision of safer, smarter, and more connected cars. Central to this transformation is the digitization and automation of vehicles, integrating advanced hardware, software, and wireless technologies to elevate their performance, functionality, and connectivity. Modern vehicles, as described by cybersecurity expert Abhishek Bansal in “The Future of Cars: Safer, Smarter and More Connected,” have evolved into highly digitized computers capable of extensive data collection, processing, and seamless interaction with other vehicles, infrastructure, and devices. Notably, some vehicles now boast self-driving capabilities, leveraging sensors, cameras, artificial intelligence, and machine learning to navigate the roads autonomously.

At the core of this policy event lies the pressing concern for the security and privacy of digitized and autonomous vehicles. These vehicles are increasingly vulnerable to a myriad of cyber threats that could compromise their functionality, safety, and overall integrity. Bansal’s research provides insight into potential threats, including ransomware, phishing attacks, brute force attacks, insider threats, leaked databases, open ports, and exploited keyless entry systems. The implications of these attacks are dire, ranging from loss of data and financial assets to identity theft, fraud, and even physical harm.

The implications extend to policy, necessitating the establishment and enforcement of industry standards and regulations encompassing encryption protocols, authentication methods, data protection laws, breach notification rules, and liability frameworks. Equally crucial is an educational initiative directed towards drivers and consumers, enlightening them about the advantages and risks of digitized and autonomous vehicles, along with their rights and responsibilities concerning data collection, sharing, and usage. Furthermore, fostering collaboration and coordination among various stakeholders, including manufacturers, suppliers, service providers, regulators, and civil society groups, is paramount for the responsible development and deployment of digitized and autonomous vehicles.

Understanding the Dynamics: Balancing Innovation and Security

Understanding the essence of this policy event or topic area is of paramount importance. The digitization and automation of vehicles represent a complex and dynamic phenomenon, offering a spectrum of benefits while raising significant concerns in the domain of information policy. On one hand, these advancements hold immense promise, including improved vehicle performance, enhanced efficiency, reduced accidents, and the potential to stimulate economic growth within the automotive industry. On the flip side, these advancements present formidable challenges pertaining to data security, privacy, and ethical responsibilities. This dichotomy necessitates a careful, comprehensive, and collaborative approach to information policy.

In essence, policy measures for digitized and autonomous vehicles are an urgent need, requiring concerted efforts among stakeholders to ensure that benefits are maximized, rights are protected, responsibilities are clearly defined, issues are effectively addressed, and opportunities are seized. Striking this balance is imperative for paving the way towards a safe and progressive future within the realm of automotive technology.

References

Bansal, A. (2023, October 16). Security issues in digitized cars: What you need to know. IEEE-USA InSight. https://insight.ieeeusa.org/articles/security-issues-in-digitized-cars-what-you-need-to-know/

Ethical Case Study Analysis

ethical-case-study-analysisDownload

Introduction

The Internet of Things (IoT) is a game-changer in technology, connecting devices seamlessly and transforming how we live and work. It offers the promise of making our lives more efficient, convenient, and sustainable (Ed Ted, n.d.). However, this transformation comes with important ethical and policy challenges. The vast amount of data being collected and shared through IoT raises concerns about privacy, security, and its impact on society (Singer & Perry, 2015). In this analysis, we’ll explore these ethical and policy dimensions of IoT, using insights from the “Ed Ted” video, as well as scholarly articles from the Brookings Institute and Intellectual Property & Technology Law Journal. Our goal is to illuminate the various aspects of IoT, emphasizing the need for clear ethical guidelines and strong policy frameworks to guide its development responsibly in this ever-evolving digital era. We’ll examine different viewpoints, support our analysis with reliable sources, and propose recommendations for a future where IoT coexists in harmony with ethical values and policy requirements. This analysis will thoroughly explore ethical implications and potential solutions, steering us towards a balanced IoT landscape that values individual rights, societal well-being, and technological progress.

Analysis

The “Ed Ted” video explains the Internet of Things (IoT), a way device can connect and communicate smoothly, changing how we live. It shows the potential of IoT to make things more efficient, convenient, and sustainable in various industries (Ed Ted, n.d.). However, it brings up ethical and policy challenges. The huge amount of data collected and shared in IoT raises concerns about privacy, security, and how it affects society (Singer & Perry, 2015). This analysis explores these ethical and policy aspects of IoT, using insights from the “Ed Ted” video and articles from the Brookings Institute and Intellectual Property & Technology Law Journal. Our aim is to shed light on different sides of IoT and stress the need for clear rules and strong policies to guide its responsible development in this digital age. We’ll look at different opinions, back our analysis with reliable sources, and suggest ways to ensure a future where IoT coexists with ethics and policy requirements. This analysis will deeply examine ethical considerations and possible solutions, moving us towards a balanced IoT world that values personal rights, societal welfare, and technological progress.

Examining the articles from the Brookings Institute and the Intellectual Property & Technology Law Journal gives us valuable insights into the different views on IoT. The Brookings Institute article, called “Alternative Perspectives on the Internet of Things,” shares detailed thoughts on how IoT affects privacy, security, and competition. It stresses the need for clear rules to handle privacy worries and encourage innovation (Schatz, 2016). Similarly, the Intellectual Property & Technology Law Journal article by Singer and Perry (2015) focuses on the privacy policies linked with wearables, a crucial part of IoT. The authors stress the importance of having clear and well-defined privacy policies to lessen risks linked with wearable technologies.

To understand the ethical problems and policy implications around IoT, we need to consider these different views. The video underlines the importance of dealing with privacy and security issues, hinting at possible consequences for society if not regulated properly. The Brookings Institute and Intellectual Property & Technology Law Journal articles support this understanding, emphasizing the need for strong policies to balance innovation with privacy and security. These sources stress the need for proactive actions to ensure IoT grows responsibly and is used responsibly.

To deal with the ethical and policy worries linked with IoT, we need a multi-sided plan. First, all parties involved need to work together to create clear regulations that support innovation while also protecting people’s privacy and security. This means setting clear rules on how data is collected, stored, and used in IoT systems. Second, companies need to be actively involved in creating and sticking to ethical principles that put user consent, data protection, and openness first. Lastly, educating the public through awareness campaigns and educational programs can help people make smart choices about their involvement with IoT technologies.

Making these recommendations a reality will require a combined effort from governments, technology companies, and individuals. Governments should create and enforce rules that make organizations accountable for acting ethically within the IoT world. Tech companies, on the other hand, need to think ahead and include ethical thinking in how they make and sell products. At the same time, people need to be informed consumers, understanding their rights and standing up for responsible IoT practices. Constant monitoring, regular policy reviews, and adjustments based on how technology changes are all vital parts of making sure IoT is ethical and responsible.

In conclusion, we need to be proactive to handle the possible ethical and policy issues from the Internet of Things. By recognizing how complex IoT is and considering various views, we can make a path for a future where innovation and privacy, security, and societal well-being all coexist happily. Teamwork, education, and clear rules will be essential in finding this important balance.

Conclusion

The Internet of Things (IoT) has the potential to change how we live and work, making things more efficient and eco-friendlier (Ed Ted, n.d.). However, our analysis shows that this great potential also raises ethical and policy concerns, especially about privacy, security, and how it impacts society (Singer & Perry, 2015). We stress the need for a well-rounded approach that carefully considers these concerns, balancing progress with privacy and security (Schatz, 2016).

Dealing with these IoT challenges requires a group effort. Governments, tech companies, and people need to work together to set clear rules and guidelines. This will lead to a responsible IoT system where progress and the rights of individuals, along with the welfare of society, are all in sync. By taking proactive steps and making informed choices, we can create a future where IoT’s potential is used in a way that is fair and environmentally responsible.

References

Ed Ted. (n.d.). The Internet of Things: A short introduction. Retrieved from http://ed.ted.com/on/VGdKwYzz#watch

Schatz, D. (2016, March 25). Alternative perspectives on the Internet of Things. Brookings Institute. TechTank. https://www.brookings.edu/blog/techtank/2016/03/25/alternative-perspectives-on-the-internet-of-things/

Singer, R. W., & Perry, A. J. (2015). Wearables: The well-dressed privacy policy. Intellectual Property & Technology Law Journal, 27(7), 24-27. https://link.gale.com/apps/doc/A420929651/AONE?u=tamp44898&sid=bookmark-AONE&xid=74b7983c

An Implementation Plan for a University Software Tracking Database

Introduction

Software is crucial for any academic institution, enabling various functions like teaching, research, administration, and communication. Managing software licenses, installations, and usage, especially in large and diverse organizations like universities, can be complex and costly. Hence, a reliable and efficient system for tracking and optimizing software resources across the university is essential.

The purpose of this project is to design and develop a database that will store information about software licenses, installations, and usage by the university’s faculty and staff. This database will help the university monitor and control software expenditures, ensure compliance with license agreements, prevent unauthorized or illegal use of software, and enhance software performance and security. Additionally, the database will offer valuable insights and reports on software trends, needs, and preferences within the university community.

The scope of the database includes all software products purchased, installed, or used by the university’s faculty and staff on their computers or devices. It excludes software used by students or external partners, embedded software, and free, open source, or public domain software.

Main points

Addressing the main challenges and risks is crucial for the successful implementation of our University Software Tracking Database. First and foremost, we need to make sure the data is good—accurate, complete, consistent, and valid data collected from various sources and systems. Equally important is keeping the data safe, making sure it’s not accessed, changed, deleted, or shared by unauthorized people, all while following privacy and protection laws. Moreover, blending this system seamlessly with what we already use for managing software in the university is key. Lastly, the database needs to handle a lot of data and be ready for our software collection to grow, meeting our future needs and changes in our academic environment.

Methodology and Tools

• Data Model: We’ll use a relational data model, organizing data into tables with rows and columns. Each table represents an entity related to software tracking, such as software product, license, installation, and usage. Relationships between tables are established using primary and foreign keys.
• Database Management System: MySQL, a popular and reliable DBMS, will be used for creating, maintaining, and manipulating the database. It supports essential features like SQL, stored procedures, triggers, views, indexes, and transactions, ensuring efficiency and data integrity.
• Programming Language: Python, a widely used high-level language, will be employed for interacting with the database. Libraries like SQLAlchemy, Flask, Pandas, and Pytest provide functionalities for efficient database interaction, testing, and data analysis.
• Testing Framework: Unit testing, integration testing, system testing, and user acceptance testing will be conducted using Pytest and Flask to ensure the quality and functionality of the database and software.

Roles and Responsibilities

• Project Manager: Leads and coordinates the project team, defines project scope, objectives, deliverables, timeline, and budget, monitors project progress and quality, and ensures successful project completion.
• Database Developer: Designs and develops database schema, tables, queries, procedures, triggers, views, indexes, etc., using MySQL and SQL.
• Database Administrator: Installs, configures, maintains, secures, backs up, restores, optimizes, and troubleshoots the database system using MySQL.
• Software Developer: Writes and tests Python code that interacts with the database using SQLAlchemy, Flask, Pandas, Pytest, etc.
• Software Tester: Creates and executes test cases and test scripts for the database and software using Pytest and Flask.
• Web Designer: Designs and creates the web interface for the software tracking database using HTML, CSS, JavaScript, Bootstrap, etc.
• Web Developer: Implements and integrates the web interface with the database and software using Flask.

Conclusions

In conclusion, the successful implementation of this University Software Tracking Database is poised to revolutionize how our academic institution manages software resources. With a comprehensive database capturing critical information on software licenses, installations, and usage, we anticipate enhanced control over expenditures, compliance with licensing agreements, and safeguarding against unauthorized software use. Moreover, the insights generated from this database will guide strategic decisions by revealing software trends, needs, and preferences, aligning our technological resources with the evolving demands of our university community.

As we move forward, the collaboration and dedication of our project team, led by a competent project manager, will be pivotal. The seamless integration of a well-designed relational data model, a robust MySQL database management system, and a versatile Python programming language, supported by an effective testing framework, is instrumental for a successful deployment. With clear roles and responsibilities assigned, we are well-equipped to overcome the challenges of data quality, security, integration, and scalability. Ultimately, this project promises to deliver a system that optimizes software management, contributing to the efficient functioning and growth of our esteemed academic institution.

Time Series Analysis of Coffee Production Around the World

Final project for Visualization Class

Dominic Pepper

Link to GitHub

Problem description: The problem is to analyze coffee production trends by country over time and identify any patterns or insights in the data.

Related work: This problem is related to previous visual analytics work that has been done to analyze trends in agricultural production, such as crop yields and exports. One example of an existing visual analytics tool that addresses this is the Food and Agriculture Organization of the United Nations (FAO) Crop Production Dashboard. This image is the source of my inspiration, it mimics the nature of my project in commodity research. However, I wanted to expand upon it by making an animated version that can show changes happening over time so potential trends could be gleamed more intuitively.

Solution: To solve the problem of analyzing coffee production trends, we used an animated bar chart to show how coffee production has changed by country over time. We used the gganimate package in R to create the animation, which allowed us to easily transition between years and visualize the changes in coffee production. We also used the position_dodge() function to separate the bars for each year, making it easier to compare production levels across countries. Additionally, we used annotations and labels to provide additional information and context for the data, such as the Crop_year value and the country names.

In terms of methodology, we used a time series analysis approach to examine how coffee production has changed over time. By animating the bar chart, we were able to see how production levels have fluctuated over the years and identify any trends or patterns in the data. We also used a part-to-whole approach by showing how each country’s production level contributes to the total global production.

Overall, the use of an animated bar chart allowed us to effectively analyze and communicate trends in coffee production over time. This methodology can also be applied to other types of agricultural production data to identify patterns and insights in the data.

The data shows that Brazil is the largest coffee producing country in the world, with production increasing steadily from 2000 to 2010, then remaining relatively stable until 2017. Colombia, Vietnam, and Indonesia are also major coffee producers, with production increasing significantly over the past two decades.

However, some countries have experienced declines in coffee production. For example, production in Ethiopia, the birthplace of coffee, has decreased since the early 2000s. Similarly, production in Mexico and Peru has also declined over the past decade.

One interesting trend that emerges from the data is the increasing diversification of coffee production. In the past, Arabica coffee was the dominant variety, accounting for more than two-thirds of global production. However, over the past decade, the share of Robusta coffee has increased significantly, particularly in Vietnam, where it now accounts for more than 90% of coffee production.

The data also shows that coffee consumption is closely linked to coffee production. Countries that produce the most coffee also tend to consume the most. For example, Brazil, Colombia, and Vietnam are among the top coffee consuming countries in the world.

In a Giffy with R

In this post I will attempt to have a go with animation using the R programming language. It seems like an arduous task no doubt, given the toolkit our output will be very simple yet still data sciency in spirit.

Here I will be utilizing the built in PlantGrowth data set within the R library.

The PlantGrowth data set is a collection of data from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions. The levels of group are ‘ctrl’, ‘trt1’, and ‘trt2’. I hope this summary helps! 😊

And without further ado. Some code:

library(ggplot2)
library(gganimate)
library(gifski)
library(ggdark)

data("PlantGrowth")

p <- ggplot(PlantGrowth, aes(x=group, y=weight)) +
  geom_boxplot(aes(color = group)) +
  scale_color_manual(values = c("red", "blue", "green")) +
  dark_theme_dark() +
  transition_manual(group) +
  labs(title = 'Treatment: {Fertilizer Effect On Plant Growth}')

animate(p, renderer = gifski_renderer())

anim_save("temp.gif", animation = last_animation(), path = "C:/Users/Nunya/Business")

And the output.

R Markdown

---

title: "Module 12" author: "Dominic Pepper" date: "2023-04-03" output: html_document

knitr::opts_chunk$set(echo = TRUE)
library(ggplot2)

R Markdown example with Iris data set

Here we will be using the built in Iris data set to go over the functionalities of R Markdown

data(iris)
summary(iris)

Sepal.Length Sepal.Width Petal.Length Petal.Width
Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
Median :5.800 Median :3.000 Median :4.350 Median :1.300
Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
Species
setosa :50
versicolor:50
virginica :50

Including Plots

You can also embed plots, for example:

Here’s an example of how you can create a scatter plot of petal length versus petal width using ggplot

plot(iris$Petal.Length, iris$Petal.Width)

Here’s an example of how you can create a scatter plot of sepal length versus sepal width

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) + geom_point()

Dot-dash plot in lattice

Here I am trying to emulate a plot done in the style exhibited by this link. Or colloquially known as Tufte style, where an emphasis on minimalism and accuracy are put into focus.

I chose to use the Iris dataset to visually present the relationship between sepal width and sepal length(in cm).

Here is the code below to recreate such a graphic:

library(lattice)

xyplot(Sepal.Width ~ Sepal.Length, data = iris,
       xlab = "Sepal Length (cm)", ylab = "Sepal Width (cm)",
       par.settings = list(axis.line = list(col="transparent")),
       panel = function(x, y,...) { 
         panel.xyplot(x, y, col=1, pch=16)
         panel.rug(x, y, col=1, x.units = rep("snpc", 2), y.units = rep("snpc", 2), ...)
       })

Visual Multi Variances Analysis

For this assignment I will be using the builtin Titanic data set within R. The four variables include: Class, Sex, Age, and Survival status.

It would be interesting to see visually how these variables effect the outcomes of survival within the tragic accident that happened over 100 years ago.

Below is the code to create a multivariable bar plot where each section is divided by class, paired by sex, and distinguished with their survival rate by “red” being no and “green” yes.

library(ggplot2)
library(titanic)

df <- as.data.frame(Titanic)

ggplot(df, aes(x = Sex, y = Freq, fill = Survived)) +
  geom_bar(stat = "identity", position = "fill") +
  facet_wrap(~ Class) +
  labs(title = "Survival rate by class and sex",
       x = "Sex",
       y = "Proportion",
       fill = "Survived") +
  scale_fill_manual(values = c("No" = "red", "Yes" = "green"))

From a quick glace we can see that females tend to always have a better survival rate compared to the men regardless of their passenger class. However passengers of the first class on average tend to have a much higher survival rate compared to the others overall.