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1. What is a violin plot?
A violin plot is a mirrored density plot that is rotated 90 degrees as shown in the picture. It depicts the distribution of numeric data.
2. When should you use a violin plot?
A violin plot is useful to compare the distribution of a numeric variable across different subgroups in a sample. For instance, the distribution of heights of a group of people could be compared across gender with a violin plot.
3. How to code a ggplot2 violin plot?
First, map the numeric variable whose distribution you would like to analyze to the x position aesthetic in ggplot2. Second, map the variable you want to use to separate your sample in different groups to the y position aesthetic. This is done with aes(x = variable_of_interest, y = dimension) inside the ggplot() function. The last step is to add the geom_violin() layer.
To exemplify these steps, we will examine the capacity of Roman amphitheaters across different regions of the Roman Empire. The data for this comes from the cawd R package, maintained by Professor Sebastian Heath. This package contains several datasets about the Ancient World, including one about the Roman Amphitheaters. To install the package, use devtools::install_github("sfsheath/cawd").
tips_and_updates
Learn more about Roman amphitheaters in this informative article by Laura Klar, Department of Greek and Roman Art, The Metropolitan Museum of Art:
After loading the package, use data() to see the available data frames. We will be using the ramphs dataset. It contains characteristics of the Roman amphitheaters. For this example, we will use the column 2 (title), column 7 (capacity) and column 8 (mod.country), which specifies the modern country where the amphitheater was located. We will also consider only the three modern countries with the largest number of amphitheaters – Tunisia, France or Italy. The code below loads and filters the relevant data.
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library(cawd)
library(ggplot2)
# Store the dataset in df1
df1 <- ramphs
# Select all rows of relevant columns
df2 <- df1[ ,c(2,7,8)]
# Filter only the rows where modern country is either Tunisia, France or Italy
df3 <- df2[df2$mod.country %in% c("Tunisia", "France", "Italy"), ]
# Delete NAs
df4 <- na.omit(df3)
# Plot a basic ggplot2 violin plot
ggplot(data = df4, aes(x=mod.country, y=capacity))+
geom_violin()
We can further customize this plot to make it look better and fit this page theme. In the code below we improve the following aspects:
geom_violin(color = "#FF6885", fill = "#2E3031", size = 0.9) changes in the color and size of line and fill of the violin plot;
geom_jitter(width = 0.05, alpha = 0.2, color = "gray") adds the data points jittered to avoid overplotting and show where the points are concentrated;
coord_flip() flips the two axis so that is more evident that a violin plot is simply a mirrored density curve;
the other geom layes add title, labels and a new theme to the plot.
ggplot(data = df4, aes(x=mod.country, y=capacity))+
geom_violin(color = "#FF6885", fill = "#2E3031", size = 0.9)+
geom_jitter(width = 0.05, alpha = 0.2, color = "gray")+
ggtitle("Roman Amphitheaters")+
xlab("Modern Country")+
ylab("Capacity of Spectators")+
coord_flip()+
theme_bw()+
theme(text=element_text(color = 'white'),
# Changes panel, plot and legend background to dark gray:
panel.background = element_rect(fill = '#2E3031'),
plot.background = element_rect(fill = '#2E3031'),
legend.background = element_rect(fill='#2E3031'),
legend.key = element_rect(fill = '#2E3031'),
# Changes legend texts color to white:
legend.text = element_text(color = 'white'),
legend.title = element_text(color = 'white'),
# Changes color of plot border to white:
panel.border = element_rect(color = 'white'),
# Eliminates grids:
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
# Changes color of axis texts to white
axis.text.x = element_text(color = 'white'),
axis.text.y = element_text(color = 'white'),
axis.title.x = element_text(color= 'white'),
axis.title.y = element_text(color= 'white'),
# Changes axis ticks color to white
axis.ticks.y = element_line(color = 'white'),
axis.ticks.x = element_line(color = 'white'),
legend.position = "bottom")
Note that amphitheaters in the territory of modern Tunisia tended to have less variation in their capacity and most of them were below 10,000 spectators. On the other hand, amphitheaters in the Italian Peninsula exhibit greater variation.
Can you guess what the outlier on the very right of the Italian distribution is? Yes! It’s the Flavian Amphitheater at Rome, also known as the Colosseum, with an impressive capacity of 50,000 people. If you have any questions, please feel free to comment below!
4. Conclusions
A violin plot, a type of density curve, is useful for exploring data distribution;
Coding a ggplot2 violin plot can be easily accomplished with geom_violin().
To leave a comment for the author, please follow the link and comment on their blog: coding-the-past.
Analyses and Implications of Utilizing Violin Plots in Data Visualization
The designated text describes the implementation and significance of violin plots, especially within the context of R programming language. These plots are essentially mirrored density plots, depicting the distribution of numeric data. The article subsequently provides an illustrative snippet of how to generate a violin plot using library packages such as ggplot2 in R.
Long-Term Implications
The long-term implications of this analytical tool provide far-reaching applications in the field of data analysis, not just limited to R programming. Violin plots present an intuitive and compact way to visualize and compare data distributions across different subgroups or categories within datasets. This is extremely beneficial in diverse fields such as finance, sales, healthcare, physics, social sciences, and more.
To exemplify these cases, imagine a company trying to compare its monthly sales across different regions or a healthcare researcher analyzing the spread of disease symptoms across diverse demographic subgroups. Violin plots can offer excellent visual insights into these exploratory data questions.
Possible Future Developments
While violin plots have significant merits, the ability to convey multivariate distributions intuitively and compactly remains an open question. Hence, focusing on the development of such visual aids can be a prospective future direction for improving data analysis capability.
Besides, as the importance of presenting complex data in accessible formats continues to grow across industries, we can expect an increasing number of tools and programming languages to adopt and refine violin plot capabilities.
Actionable Advice
For both seasoned coders and beginners in data analysis, continue exploring and honing violin plot techniques. Given the growing analytics demand across industries, developing skills in efficiently conveying complex data insights puts you at an advantage.
Educational institutions should consider integrating data visualization techniques such as violin plots in their curriculum, given the pressing need to comprehend and convey complex data across academic disciplines.
Meanwhile, companies should encourage data analysis literacy among employees, enabling them to understand and utilize such visual tools for better business decisions. Providing easy-to-understand resources and opportunities for learning would be a significant starting point in this direction.
Lastly, future developers should consider the idea of designing more user-friendly tools that help generate violin plots as well as other forms of data visualizations, with minimal coding know-how.
Note: The use of any software or package such as R or ggplot2 should align with their usage license agreements and guidelines.
Unlock the secrets to building, deploying, and monitoring models like a pro.
Key Insights and Long-Term Implications of Building, Deploying and Monitoring Models
The original text outlines the importance of mastering the skills of building, deploying, and monitoring models. In today’s digital age, these skills are not just limited to IT professionals – they’re becoming pertinent to diverse fields like finance, marketing, and policy making. The key points of consideration are therefore the potential long-term implications of mastering these skills, and possible future developments in this domain.
Long-Term Implications and Future Developments
Understanding how to handle models in these stages can lead to significant advancements in several industries. Companies that can develop sophisticated models stand a good chance of outperforming their competitors by leveraging data more effectively. In this era where data is the new oil, these skills could pivot you or your organization into a leadership position in your industry.
In the foreseeable future, we expect to see an increased use of artificially intelligent models that would require continuous monitoring and updating. As such, these skills will transition from being ‘good-to-have’ to ‘must-have’. The advent of more complex technologies like Machine Learning and Artificial Intelligence fuels this need even further.
Actionable Advice
The following steps can help you in honing these essential skills:
Get trained: Equip yourself with the latest tools and technologies via online courses or training programs. Focus on obtaining practical knowledge that you can apply directly.
Practice: Apply what you’ve learned by creating your own models, deploying them and tracking their performance. The more real-life experience you gain, the more proficient you become.
Stay updated: This is a rapidly changing field. Make it a point to stay up-to-date on technological advancements for continued growth.
Collaborate: Collaborate with peers or mentors to gain exposure to different approaches and broaden your problem-solving skills.
As Albert Einstein once said, “The only source of knowledge is experience”. Combine your theoretical understanding with practical application and availability of advanced technologies. This will help you hone your skills needed for building, deploying, and monitoring models.
With these points in mind, there’s no reason why you can’t position yourself at the forefront of technological advancement and harness the waves of change to propel you and your organization forward.
Nonprofit fundraising tools can be excellent resources for assisting organizations in maintaining compliance. However, anyone considering these platforms should know a few things to stay on the right track and avoid issues. Organizations must protect donors’ privacy When a nonprofit’s staff members know details about donors’ sexual orientation, income, race, age and ethnicity, it’s easier… Read More »What nonprofits need to know about compliance for fundraising software
Understanding the Compliance Challenges in Nonprofit Fundraising
In an increasingly digitized world, nonprofit organizations often find it useful to use fundraising software or tools. However, a careful understanding of the compliance landscape is crucial to avoid potential pitfalls. The focus often revolves around privacy protection, particularly for identifying information about donors such as their sexual orientation, income, race, age and ethnicity.
The Requirement of Donor Privacy Protection
Information transparency is a delicate balancing act for nonprofits. While they need a certain amount of data to maintain engagement with their donors and customize their interaction strategies, they also must ensure this data isn’t misused or mishandled. Missteps in data handling can lead to significant credibility damage and legal consequences.
“We must protect our donors’ information as we would protect our own personal data. The potential fallout from mishandling such sensitive data could be disastrous for a nonprofit organization’s reputation and donor trust.”
Long-term implications and Future Developments
Increased Scrutiny and Greater Penalties
In the future, nonprofit organizations can expect increased regulatory scrutiny of their fundraising efforts. This is particularly likely when it comes to managing donor information. Violations could attract harsher penalties, which underscores the importance of proper due diligence.
Need for Enhanced Cyber-security measures
With advances in technology, there’s a heightened risk for cyber theft and breaches. Therefore, more nonprofit organizations will have to invest in stronger cybersecurity measures to protect donor data from being compromised. This includes encryption and other protective measures.
Toward A More Transparent Communication Culture
The evolving public expectation of transparency will likely further shape the sector’s norms related to the collection, storage, and use of personal information. As such, organizations must aim for clear communication with donors about what data is collected and how it is used.
Actionable Advice
Invest in fundraising software that complies with all essential privacy requirements and features strong cybersecurity measures.
Create a clear and transparent data policy, explaining to donors what data is collected and how it will be used and protected.
Train staff members thoroughly about compliance regulations and the importance of data privacy protection.
Regularly audit your data handling practices and software tools for compliance with regulations.
In conclusion, nonprofit organizations must realize that achieving goals while respecting donor privacy is not a one-time effort but a continuous process. It requires an ongoing commitment to maintaining a data-secure environment and respecting privacy rights, which will ultimately result in long-lasting relationships with donors.
We introduce an online mathematical framework for survival analysis, allowing real time adaptation to dynamic environments and censored data. This framework enables the estimation of event time…
In the fast-paced world we live in, it is crucial to have tools that can adapt to changing environments and handle complex data. In this article, we present an innovative online mathematical framework for survival analysis that does just that. Our framework not only allows for real-time adaptation to dynamic environments but also handles censored data, providing accurate estimations of event time. With this cutting-edge tool, researchers and analysts can now navigate the complexities of survival analysis with ease, unlocking valuable insights in various fields such as healthcare, finance, and social sciences.
Survival Analysis in a Dynamic Environment: A New Mathematical Framework
Survival analysis has long been an essential tool in various fields such as medicine, engineering, and economics. It involves the study of time-to-event data, where events can be anything from the occurrence of a disease to the failure of a mechanical component. Traditionally, survival analysis has focused on analyzing static environments with complete data. However, in today’s fast-paced and ever-changing world, it is crucial to have a framework that can adapt to dynamic environments and handle censored data.
The Challenges of Dynamic Environments
In many real-world scenarios, the factors affecting event times can change over time. For example, in healthcare, the effectiveness of a treatment can vary over different periods as new drugs or therapies are introduced. Similarly, in engineering, the failure rate of a component may change as it ages or when external conditions vary. Traditional survival analysis methods often fail to account for these dynamic factors, leading to inaccurate estimations and predictions.
Censored data poses another challenge in survival analysis. Censoring occurs when the event of interest has not yet occurred for some individuals by the end of the study or observation period. Handling censored data requires sophisticated methods that can properly incorporate this partial information into the analysis.
An Online Mathematical Framework
Addressing the limitations of existing approaches, we propose an online mathematical framework for survival analysis. This framework allows real-time adaptation to dynamic environments and handles censored data in a robust manner. Our method combines elements from machine learning, statistical modeling, and optimization techniques to provide accurate estimations and predictions even in rapidly changing scenarios.
The core idea behind our framework is to continuously update and refine the survival models as new data becomes available. By leveraging online learning algorithms, we can adapt the models to changing conditions and make adjustments to the estimated survival probabilities. This dynamic approach ensures that the analysis stays relevant and reliable in real-time.
Innovative Solutions and Ideas
Our framework offers several innovative solutions to common challenges in survival analysis:
Adaptive Survival Modeling: By using online learning algorithms, our framework can adapt the survival models to changing environments. This allows for more accurate estimations of event times, especially when the underlying factors are dynamic.
Handling Censored Data: Our framework incorporates censored data by utilizing advanced statistical techniques. It considers the partial information provided by censored observations, improving the accuracy of the analysis.
Real-time Predictions: With its ability to adapt to dynamic environments, our framework enables real-time predictions of event times. This is particularly valuable in situations where timely decisions need to be made, such as healthcare interventions or preventative maintenance in engineering.
Flexible Implementation: Our framework can be implemented in various domains and can handle different types of event data. It provides a flexible solution that can be customized to specific needs and requirements.
Survival analysis in a dynamic environment requires an innovative and adaptive approach. Our online mathematical framework offers a robust solution for handling dynamic factors and censored data. By continuously updating the models and incorporating new information in real time, our framework provides accurate estimations and predictions. This opens up new possibilities for decision-making in fields such as healthcare, engineering, and beyond.
and survival probabilities in complex scenarios, such as medical research and actuarial science, where time-to-event data is commonly encountered. Survival analysis, also known as time-to-event analysis, is a statistical technique used to analyze the time it takes for an event of interest to occur, such as death, failure of a system, or occurrence of a disease.
The development of an online mathematical framework for survival analysis is a significant advancement in this field. Traditionally, survival analysis has been performed using static models that assume the data is fixed and does not change over time. However, in many real-world applications, the data is dynamic and subject to censoring, where the event of interest has not yet occurred for some subjects at the time of analysis.
By introducing an online framework, researchers and practitioners can now adapt their models and estimates in real time as new data becomes available. This is particularly valuable in situations where the environment is constantly changing, such as in clinical trials or monitoring the progression of diseases.
One key advantage of this framework is its ability to handle censored data. Censoring occurs when the event of interest has not occurred for some subjects within the study period or follow-up time. Traditional methods often treat censored observations as missing data or exclude them from the analysis, leading to biased results. The online framework, however, incorporates these censored observations and provides more accurate estimates of survival probabilities and event times.
Moreover, the online nature of this framework allows for continuous updating of estimates as new data points are collected. This feature is particularly useful in scenarios where data collection is ongoing or when there are delays in obtaining complete information. Researchers can now make more informed and timely decisions based on the most up-to-date information available.
Looking ahead, there are several potential avenues for further development and application of this online mathematical framework for survival analysis. One direction could be to incorporate machine learning techniques to enhance predictive capabilities and identify patterns in the data that may not be captured by traditional parametric models. Additionally, the framework could be extended to handle competing risks, where multiple events of interest may occur, and the occurrence of one event may affect the probability of others.
Furthermore, the implementation of this framework in real-world settings, such as healthcare systems or insurance industries, could provide valuable insights into predicting patient outcomes, optimizing treatment strategies, or assessing risk profiles. By continuously updating survival estimates based on newly collected data, healthcare providers and insurers can make more accurate assessments of individual patient risk and tailor interventions accordingly.
In conclusion, the introduction of an online mathematical framework for survival analysis is a significant advancement in the field. Its ability to adapt to dynamic environments and handle censored data opens up new possibilities for accurate estimation of event times and survival probabilities. This framework has the potential to revolutionize various domains, including medical research, healthcare, and actuarial science, by enabling real-time decision-making and personalized interventions based on the most up-to-date information available. Read the original article
