Final Project

For my final project I analyzed the amount of meat recalled in 2014 (2015 is still not over, so the data may not be fully accurate as of yet). The type of meat most recalled was beef, with over 13 million pounds called into question for potential health hazards. The least recalled was ovine (sheep), barely cracking 27,000 lbs. I chose to display the data in this way so it would be easy to read and pack a lot of data into it.

The data set comes from the United States Department of Agriculture that lists everything from the species, type of contamination, and occasionally specific product that was recalled (ex chicken noodle soup, beef jerky, etc) (fsis.usda.gov). I didn't know much about meat recalls at first but after some researching, some of the information blew my mind. Recalling meat is at a food companies' discretion, even if it's government mandated-meaning even if they knew the product was tainted they could simply sell it anyway. Most of the contaminated meat is also never recovered (USA Today). Customers are buying and consuming it without knowing the meat can get them sick. Interestingly enough, poultry and pork had more recalls overall than beef, but the number of pounds of beef recalled comparatively speaking were much more. This research lead me to rediscovering a major beef recall last year that I had totally forgotten about (Food Safety News). Beef all over the country had been infected with e.coli, which the USDA classifies as a "Class I" recall (the strongest of the classes, meaning there is a reasonable chance the meat will cause health problems or even death). I eat meat fairly regularly, so this information was fascinating to me and has me intrigued to learn more about meat industry practices.


References

http://www.fsis.usda.gov/wps/portal/fsis/topics/recalls-and-public-health-alerts/recall-summaries

http://usatoday30.usatoday.com/money/industries/food/2007-12-02-meat-recalls_N.htm

http://www.foodsafetynews.com/2014/02/whats-going-on-with-the-massive-rancho-beef-recall/#.VmYfg_krKUk

Project 1 and 2

For this assignment I decided to measure the frequency between the organization types and the end date of their time using the program. As you can see I've used a scatter plot to display the data. From analyzing it I can now say with certainty that private use of the programs had the most longevity-they ended decades after other groups had. This graph also tells me that most of the program use generally ends shortly after 2010-while a few private buyers are still around the vast majority of usage stops there.

I chose this analysis because I thought it was one of the more coherent ways to make sense of a large set of data and might be something someone would potentially want to know-what kind of organizations were using the programs for the longest. I also chose a scatter plot because I thought it would work best to illustrate general trends for a large set of data-while the result does have its drawbacks it's at least very clean looking and easy to read.

Assignment 12

This is a cleaned-up and improved version of the graph I did for Assignment 10. I liked Evergreen and Emery's strategies and thought they were well-reasoned and helpful. Their tips make graphs as easy to read as possible. I especially liked their points about color and especially the one about visualizations being accessible for people with color-blindness (I've known several people with that condition and think about them often when I see various designs).

Assignment 11

My animation was a graph building itself off of the results of a coin toss. I was inspired by a blog I found that talks about how to use R efficiently and had code on this kind of animation. I changed some of the numbers around and watched it form. The code was much more complicated than anything I had seen before. It was very rewarding to watch the sequence animate itself however, and to realize the hard work coders put into relatively simplistic designs like these. I see animated GIFS all over the internet so it's very interesting to know some of the coding that goes into making them.

Assignment 10

Unfortunately I couldn't get ggplot2 to install into R. I made another bar plot anyway, with made up data surrounding which presidential candidate students might want to vote for the democrat primary elections. I'm eager to look at my peers work to see what they came up with, and what ggplot2 can offer.

Assignment 9

I had never worked with R before this assignment (though obviously I remembered hearing about it in previous lectures), so it was an interesting experience. I don't personally have much experience with coding so at first imputing what I wanted was challenging, but eventually I got used to it. It ended up fascinating to see the graph build itself before my eyes however, rather than just seeing the final product. I'm interested in learning more about R and bettering my understanding of it (including having a better understanding of how to insert colors-I tried to get each bar to correspond to each color but could not get it to work).

Assignment 8

After generating the Chi-square results, here is what I have found:

-Goals: chi-squared equals 0.000 with a P value of 1.
-Grades: chi-squared equals 0.533 with a P value of 0.7661.
-Popular:chi-squared equals 0.982 with a P value of 0.6119.
-Sports: chi-squared equals  0.003 with a P value of  0.9987.

From the results, I gather that the biggest difference between the actual and expected results was how many students valued popularity the most in each group, and the least (actually no difference) was how many students valued goals.