Intro to Coding

Part 4: Exercises

Part 1 | Part 2 | Part 3 | Part 4

Exercises

Ex: R as a Calculator

Meet Leia, a fictitious undergrad student taking Stat 20. Leia loves to drink coffee in the morning, and she brews her own coffee at home. She even has a monthly budget of $20 to cover this type of expense. As you know, we can use R to create an object or variable coffee for Leia’s budget:

coffee <- 20

Alternatively, you can also use the equals sign = as an assignment operator:

coffee = 20

Your Turn: Leia’s Expenses

Consider the bills of Leia’s fixed monthly expenses:

• phone $80
• transportation $20
• groceries $600
• rent $1800

1. Make more assignments to create variables phone, transportation, groceries, and rent with their corresponding amounts.

TipSolution:

phone <- 80
transportation <- 20
groceries <- 600
rent <- 1800

/

2. Now that you have all the variables, create a total object with the sum of her fixed monthly expenses.

TipSolution:

total <- phone + transportation + groceries + rent
total

/

3. Assuming that Leia has the same expenses every month, how much would she spend during a school “semester”? (assume the semester involves five months).

TipSolution:

total * 5

/

4. Maintaining the same assumption about the monthly expenses, how much would Leia spend during a school “year”? (assume the academic year is 10 months).

TipSolution:

total * 10

/

Ex: Taxonomy of Data and R Vectors

From the taxonomy of data, you know that we have 4 flavors of variables, and their corresponding classes in R (shown below inside parenthesis) illustrated in the following examples:

# continuous (numeric)
x1 <- c(1.2, 3.3, -0.5)

# discrete (numeric)
x2 <- c(2, 4, 6)

# ordinal (ordered factor)
x3 <- factor(c("sm", "md", "lg", "sm"), levels = c("sm", "md", "lg"))

# nominal (character or factor)
x4 <- c("strawberry", "lemon", "vanilla")
x4bis <- factor(c("strawberry", "lemon", "vanilla"))

Your Turn: Terrestrial Planets

Consider the following data set—shown in the table below—containing variables of so-called Terrestrial planets. These planets include Mercury, Venus, Earth, and Mars. They are called like this because they are “Earth-like” planets: relatively small in size and in mass, with a solid rocky surface, and metals deep in its interior.

name	gravity	moons
Mercury	3.7	0
Venus	8.9	0
Earth	9.8	1
Mars	3.7	2

/

1. Consider the column name in the provided table of terrestrial planets. Use the c() function to create a character vector name containing the names of the Terrestrial planets.

TipSolution:

name = c("Mercury", "Venus", "Earth", "Mars")

/

2. Consider the column gravity in the provided table of terrestrial planets. Use the combine function c() to make a numeric vector gravity for the Terrestrial planets.

TipSolution

gravity = c(3.7, 8.9, 9.8, 3.7)

/

3. Consider the column moons in the provided table of terrestrial planets. Use the combine function c() to make an ordinal factor moons.

TipSolution

moons = factor(c(0, 0, 1, 2), ordered = TRUE)

/

Ex: Data Frames in R

Consider again the data set of Terrestrial planets—shown in the table below.

name	gravity	moons
Mercury	3.7	0
Venus	8.9	0
Earth	9.8	1
Mars	3.7	2

/

Use the vectors that you defined in the previous section in order to create a data frame planets:

TipSolution

planets = data.frame(
  "name" = name,
  "gravity" = gravity,
  "moons" = moons,
  "haswater" = haswater
)

/

Ex: Challenge

Let’s apply everything that you’ve learned so far in order to create a data frame students containing the following data, and the provided specifications listed below:

name	height	year	resident
Leia	160	sophomore	TRUE
Luke	170	freshman	FALSE
Han	182	senior	TRUE
Lando	178	junior	FALSE

/

• name: nominal variable (character)
• height continuous variable (numeric)
• year: ordinal variable (ordered factor)
• resident: nominal variable (logical)

TipSolution

students = data.frame(
  "name" = c("Leia", "Luke", "Han", "Lando"),
  "height" = c(160, 170, 182, 178),
  "year" = factor(x = c("sophomore", "freshman", "senior", "junior"),
                  levels = c("freshman", "sophomore", "junior", "senior")),
  "resident" = c(TRUE, FALSE, TRUE, FALSE)
)

Want more practice? Studying for a quiz?

Try the “extras” for this week - on the homepage you’ll see a link to “Coding Basics” with more simple coding problems. Or click here to go there directly.

Summary

That was a lot! But we promise that this lesson will pay dividends for the rest of the semester. Coding is like the foundation of data science: your house is only as good as the foundation you build it on. Now we can focus on more fun things, as we use R to analyze data, ask questions, make plots, and generally empower our curiosity.

More good things about R (optional read):

R is one of the most powerful languages for doing statistics and data science. One of the reasons for its power and popularity is that it is both free and open-source – anyone can go see the internal gears of how it works, and anyone can propose changes to it. This turns languages like R into something that resembles Wikipedia: a collaborative effort that is constantly evolving. Extensions to the R language have been authored by professional programmers¹, people working in industry and government², professors³, and students like you⁴.

You’ll be writing and running code through an app called RStudio. Beyond writing R code, RStudio allows you to manage your files and author polished documents that weave together code and text. RStudio can be run through a browser and we have set up an account for you that you can access by sending a browser tab to https://stat20.datahub.berkeley.edu/ or clicking the link in the upper right corner of the course website.

References and further reading

• Hands on Programming with R by Garret Grolemund. A friendly introduction to the R language with fun examples.

• The official (somewhat dense) documentation fo the R language. https://cran.r-project.org/doc/manuals/r-release/R-lang.html

• R for Data Science by Hadley Wickham and Garrett Grolemund. A comprehensive but approachable guide to doing data science with R. A good reference once you’re deeper into this course..

Figure 1: The arc of learning R⁵.

← Back to: Vectors and data frames

Footnotes

1. The googlesheets4 package, which reads spreadsheet data into R was authored by Jenny Bryan, a developer at Posit: :https://googlesheets4.tidyverse.org/.

2. The statistics office of the province of British Columbia maintains a public R package with all of their data: https://bcgov.github.io/bcdata/

3. Dr. Christopher Paciorek in the Department of Statistics at UC Berkeley maintains a package to fit a very broad class of statistical models called Bayesian Models: https://r-nimble.org/.

4. Simon Couch wrote the stacks package for model ensembling while an undergraduate https://stacks.tidymodels.org/index.html.

5. R monster artwork by @allison_horst.