Class 2 notes

Introduction

You are welcome to follow along here to take notes, or take breaks to program when we do. This is a “Quarto” file, which is sort of next-generation RMarkdown. To be honest, I’m still working out some of the differences. But the good news is that if you know .Rmd you should be fine, and if you don’t this is not really noticeably different.

Milestones of adulthood

Read in the data in milestone.csv stored in the folder data/.

Use the code chunk below as a start. Code chunks can be lengthy or brief.

dat <- read.delim(file="./data/milestone.csv", header = TRUE, sep = ',')
head(dat, n = 5)

    milestone year percent
1 independent 1983      83
2 independent 1993      77
3 independent 2003      77
4 independent 2013      73
5 independent 2023      64

tail(dat, n = 8)

   milestone year percent
13     child 2003      55
14     child 2013      50
15     child 2023      35
16      home 1983      49
17      home 1993      42
18      home 2003      44
19      home 2013      35
20      home 2023      33

# this is a comment

plot(percent ~ year, dat, subset = milestone == "independent")

Figure 1: A scatterplot of percent of target population living alone, by UC Census Bureau data.

barplot(percent ~ year, dat, subset = milestone == "independent", ylim = c(30, 100), xpd = FALSE)
box()

Figure 2: A barplot of percent of target population living alone, by UC Census Bureau data. The option xpd = FALSE will clip the bars at the vertical axis minimum.

plot(percent ~ year, dat, subset = milestone == "independent", type = 'l', xlim = c(1980, 2025), ylim = c(0, 100), las = 1)

Figure 3: A connected line graph of percent of target population living alone, by UC Census Bureau data.

par(mfrow = c(1, 1), mar = c(4.1, 5.1, 1.6, 0.8), xaxs = "i", yaxs = "i")
plot(percent ~ year, dat, subset = milestone == "independent", type = 'b', xlim = c(1980, 2025), ylim = c(0, 100), las = 1, xlab = "", ylab = "", lwd = 2, pch = 19)
mtext("Time (Years)", side = 1, line = 2.5, font = 2, cex = 1.25)
mtext("Percent living Independently", side = 2, line = 2.5, font = 2, cex = 1.25)
mtext("Metrics of Adulthood", side = 3, line = 0, font = 2, cex = 1.5)
grid()
box()

Figure 4: A connected line graph of percent of target population living alone, by UC Census Bureau data. We have added annotation.

par(mfrow = c(1, 1), mar = c(4.1, 5.1, 1.6, 0.8), xaxs = "i", yaxs = "i")
plot(percent ~ year, dat, subset = milestone == "independent", type = 'o', xlim = c(1980, 2025), ylim = c(0, 100), las = 1, xlab = "", ylab = "", lwd = 2, pch = 19, axes = FALSE)
axis(1, at = seq(1983, 2023, by = 10))
axis(2, at = (0:10)*10, las = 1)
mtext("Time (Years)", side = 1, line = 2.5, font = 2, cex = 1.25)
mtext("Percent living Independently", side = 2, line = 2.5, font = 2, cex = 1.25)
mtext("Metrics of Adulthood", side = 3, line = 0, font = 2, cex = 1.5)
grid()
box()

Figure 5: A connected line graph of percent of target population living alone, by UC Census Bureau data. We have added additional annotation.

par(mfrow = c(2, 2), mar = c(4.1, 5.1, 1.6, 0.8), xaxs = "i", yaxs = "i")
plot(percent ~ year, dat, subset = milestone == "independent", type = 'o', xlim = c(1980, 2025), ylim = c(0, 100), las = 1, xlab = "", ylab = "", lwd = 2, pch = 19)
mtext("Percent", side = 2, line = 2.5, font = 2, cex = 1.25)
mtext("Living Independently", side = 3, line = 0.5, font = 2, cex = 1.5)

plot(percent ~ year, dat, subset = milestone == "married", type = 'o', xlim = c(1980, 2025), ylim = c(0, 100), las = 1, xlab = "", ylab = "", lwd = 2, pch = 19)
mtext("Married", side = 3, line = 0, font = 2, cex = 1.5)

plot(percent ~ year, dat, subset = milestone == "child", type = 'o', xlim = c(1980, 2025), ylim = c(0, 100), las = 1, xlab = "", ylab = "", lwd = 2, pch = 19)
mtext("Time (Years)", side = 1, line = 2.5, font = 2, cex = 1.25)
mtext("Percent", side = 2, line = 2.5, font = 2, cex = 1.25)
mtext("With Children", side = 3, line = 0, font = 2, cex = 1.5)

plot(percent ~ year, dat, subset = milestone == "home", type = 'o', xlim = c(1980, 2025), ylim = c(0, 100), las = 1, xlab = "", ylab = "", lwd = 2, pch = 19)
mtext("Time (Years)", side = 1, line = 2.5, font = 2, cex = 1.25)
mtext("Homeowner", side = 3, line = 0, font = 2, cex = 1.5)

Figure 6: A connected line graph for percent of target population meeting four independently-determined metrics of adulthood, by UC Census Bureau data.

plot(percent ~ year, dat, subset = milestone == "independent", ylim = c(0, 100), type = 'l', lwd = 2, col = hcl.colors(4)[1])
lines(percent ~ year, dat, subset = milestone == "married", type = 'l', lwd = 2, col = hcl.colors(4)[2])
lines(percent ~ year, dat, subset = milestone == "child", type = 'l', lwd = 2, col = hcl.colors(4)[3])
lines(percent ~ year, dat, subset = milestone == "home", type = 'l', lwd = 2, col = hcl.colors(4)[4])
legend("bottomleft", c("Independent", "Married", "Child", "Home"), lty = 1, lwd = 2, col = hcl.colors(4))

Figure 7: Connected line graphs for percent of target population meeting four independently-determined metrics of adulthood, by UC Census Bureau data.

Exponential growth and decay

Read in the data in exponential.csv. My convention is usually calling data dat, but I don’t normally mix datasets in a file like this. Maybe call this one dat2.

Mouse-elephant curve

Read in the data in mouse.csv as dat3 or something equally convenient. Just remember the variable name can’t start with a number and shouldn’t contain special characters - definitely no dashes or spaces.