Lab 2

All programming entails world-making, as the ritual act of writing and running Hello World reminds us.

Kirschenbaum, Mathew. “Hello Worlds (Why Humanities Students Should Learn to Program).” 2010.

I believe that humanities students should learn to code as programming develops critical thinking, creativity, and problem-solving skills that are essential to humanities disciplines. Matthew Kirschenbaum notes in this essay that “All programming entails world-making”. This idea aligns with the core of the humanities, which fundamentally places an emphasis on analysis and reasoning.

I don’t have much experience with coding. I am currently taking an Intro to Computer Science course and have taken Intro to Statistics which involved R, but that’s the only experience I have regarding coding. With this lab assignment, I was able to do tutorials on HTML and CSS, which were impressive to me. The HTML tutorial introduced how to structure a webpage from scratch and the CSS tutorial showed how to style the content. I believe this type of knowledge is beneficial, as it enables humanities students to present their ideas whatever they want without using an outside source.

Kirschenbaum’s perspective on coding as “world-making” emphasizes how coding is relevant to humanities. Programming is not only just a technical skill but also serves as a tool to visualize data and create interactive works. Without basic coding knowledge, humanities scholars cannot be active participants in their creation. As a non-CS major, this is also one reason I have decided to take Intro to CS and Hacking the Humanities this term.

Here is a sample of R code from my stats project that shows how coding can used to illustrate the findings that students make:

# Chi-square test for association between race and tier of law school
chisq.test(chitable_tier)

# Pearson's Chi-squared test
# data: chitable_tier
# X-squared = 1745.7, df = 15, p-value < 2.2e-16

# Confidence interval for the difference in proportions of Black vs White students attending Tier 1 schools:
prop_diff <- 0.180 - 0.011
conf_int <- c(prop_diff - 1.96 * sqrt((0.180 * (1 - 0.180) / 1198) + (0.011 * (1 - 0.011) / 17139)),
              prop_diff + 1.96 * sqrt((0.180 * (1 - 0.180) / 1198) + (0.011 * (1 - 0.011) / 17139)))
conf_int
# Output: [0.147, 0.191]