Module 10: Improving Visualizations

Hi everyone!

This week we learned about time series analysis and visualizations. Time series models help us see trends over time such as Minard's Napoleon March visualization that is able to show the size of the army, location, and route.

Nathan's Hot Dog Contest Visualization

I decided to improve the visualization above of Nathan's Hot Dog Eating Contest Results to include more information about who won each win and clarify when new records were set. The number of hotdogs eaten in each record win is shown in the bar and the red diamond on top indicates a record winner. 

I utilized the 5 principles of visualization from last week to enhance the alignment and repetition through new records shown as red diamonds, contrast through the different colored countries, and proximity and balance through the easily comparable bar heights.

Findings: The colors for each country add additional information on which country won that allows us to see Japan had the most record breaking wins consecutively in the early 2000s. It is also clear U.S has had the most wins especially in the beginning years of this competition.

This time series graph is useful to observe the trend of which countries have won the hot dog eating contests and when records were set and even forecast which country may set the next record as well as what the hot dog count would be.

Economics Data Visualization

The second visualization to improve was the unemployment time series. The original visualization is seen below.



This graph lacks a title and detailed information on other factors to do with unemployment that are present in the dataset.









I believe it would be interesting to see how the rate of unemployment by population compares to the median duration of unemployment in weeks. Does the trend of rate of unemployment inform the duration?

These two lines with two different y axises shows the data is not being compared by the actual unit values, but the focus is on the fluctuation in values. From this graph, we see clarity in the title of what is being displayed and a descriptive legend. 

Findings: An increase in rate of unemployment matches an increase in duration, but we can see the duration of unemployment rises faster and drops lower quickly between 2000 to 2010 than the rate.

This time series graph is useful to observe the trend of when unemployment increased the most and how these shifts match the duration of unemployment and even forecasts how the unemployment duration will change with changes in unemployment rate.

Check this out in GitHub!

-Ramya's POV

Comments

Post a Comment

Popular posts from this blog

Module 8: Correlation and ggplot2

Image
Hi everyone! This week we learned about correlation analysis and how to utilize the ggplot2 package to visualize correlation.  A great method to visualize correlation or relationships between variables is a scatterplot with a line of best fit. Within the mtcars dataset, I wanted to gain a better understanding of how each variable correlated against each other to find a relationship between variables I find the most interesting to visualize. I used the corrplot package to generate this initial visualization. From this plot, we can see there is a strong negative correlation (dark red) around 0.7 for horsepower (hp) and acceleration (qsec). I thought this was unusual because a car with higher horsepower would make sense to accelerate faster since it generates more power. To explore this further, I created a scatterplot with a line of best fit. Based on this scatterplot, it is clear that increasing horsepower relates to a lower acceleration by the car. However, this graphic explains th...
Read more

Final Project: Biodiversity in U.S National Parks

Image
Hi everyone! For my final project, I plan to explore the 2016 National Park Dataset: https://www.kaggle.com/datasets/nationalparkservice/park-biodiversity/data Problem Description: Nonnative species have been identified as a major contributor to biodiversity decline because invasive species are highly adaptive to any environment and can easily outcompete natives. This loss of native species is a particular issue for U.S National Parks that were created to be untouched and protected. According to the National Park Service, they disturb ecological processes, harm ecosystem integrity, degrade natural resources, interfere with visitor experiences in parks, and exacerbate climate change and fragmentation from land use change (NPS, 2021). In order to understand these non-natives, it is important to understand their distribution, where they have been reducing biodiversity, and potential reasons to why. Problem Objectives: Visualize the largest National Parks in size and their locations in...
Read more

Module 12: Social Network Analysis

Image
Hi everyone! This week we learned about social network analysis and how we can use ggnet2 in R to accomplish this. I absolutely love the new Netflix show "Wednesday" and the original Addams Family movies, so I decided to analyze which characters in the original movie have the most interactions with the other characters.  My hypothesis:  Wednesday will have the most interactions since she is seen as the main character. The dataset site:  https://moviegalaxies.com/movies/view/26/the-addams-family/#  I imported the json file with the nodes and edges. I set the ggnet2 parameters to vary the size of the node based on the "indegree" or amount of interactions that character has overall. Result: From the visualization, we can see Granny has the most connections (9) and Wednesday has very few connections.  I enjoyed using ggnet2 to create this social network. The biggest challenge of creating this social network was finding a dataset for the visualization and customizing...
Read more