Week 2: a Synthesis

• Synthesis # 2:  Write a wordpress post of about 500 words reflecting on your learning for the week.  Your post should:
  • Comment on things you learned this week
  • Comment on things that changed your perspective on something you already knew.  
  • Include commented code that another person who works with R could understand
  • Include an histogram
  • Reflect on what the histogram tells us
  • Interpret the histogram in light of theory.  
  • Explicitly reference at least one reading from this week.

This week took on more historical learning than code-based learning. It began with learning about indexing and subsetting, with the focus being on lists created using invoices from Benjamin Bates’ cotton industry. We would compile lists in R of the various weights of the bales of cotton transported to Bates, and then implement what we learned from outside of class videos to that data.

We also completed a worksheet during this week, one that focused on specific invoice data from the Bates Cotton Manufacturing Company, using that data to make a list of the bale weights for the cotton, and looking at statistical aspects of that data. We were able to find approximate costs for the total amount of money the cotton was worth/sold for, as well as the approximate number of days it took to compile the invoice amount of cotton that was provided.

bale_weights <- c(518, 530, 470, 503, 538, 518, 443, 478, 458, 463, 468, 501, 483, 543, 508, 493, 468, 523, 464, 468, 428, 443, 503, 543, 516, 503, 583, 470, 453, 463, 490, 468, 548, 508, 513, 478, 500, 508, 493, 473, 501, 549, 487, 508, 454, 448, 453, 498, 458, 443)
#Creates a vector that contains the weights of each bale of cotton contained in the invoice. 

bale_days <- bale_weights/150
sum(bale_days)
#creates a variable bale_days to determine the number of days of labor all bales of cotton
#from the invoice took

bale_money <-bale_weights*.1075
sum(bale_money)
#creates bale_money that uses the bale_weights vector to find the total amount of money the shipment
#was

We also were able to make a histogram containing the varying weights of the bales of cotton. Within this graph we were able to see the ranges in weight that the cotton bales were the most likely to be.

hist(bale_weights)
#creates a histogram of the amounts of weight that the bales of cotton were, and organizes it 
#in histogram format

Further this week, we were also able to learn more about the language the role of feminism in data science, as well as a concept called racial capitalism. Learning that a racial aspect lies within many forms of capitalism changed my views of money, specifically in how money is made and how many businesses and organizations thrive off of the forced labor of others. In his talk at the University of Washington, Robin Kelley referenced racial capitalism as being “the ways that money is earned, at the expense of violence, racism, imperialism, and genocide”. In a way, this explains the ‘sweat shops’ that run around the world today, and many other now-archaic business practices that took advantage of the needs of those who could not survive without some form of labor/pay.

We also read a paper on data feminism, specifically referencing how data science should not be a male-dominated conversation. It spoke in great length about how women’s accomplishments in the science fields have gone greatly undernoticed, and thus those who experience success are not allowed to celebrate it. The goal of data feminism is to convince people that data science remains a relevant topic to members of all genders, not necessarily males.

As of late, I feel as though there were many learning opportunities from various angles to take advantage of this week. Each article we read is a different look on data science, specifically leaning on topics like feminism, racial aspects, and the language components. I look forward to what is to come.