Katelynn C analyzed a Spotify Alternative Rock dataset containing hundreds of songs and thousands of values. Here is some of the beautiful Pyret code she wrote to analyze the loudness, energy, and key of each song:

fun key-color(r):

if (r["KEY"] == 1): circle(5, "solid", "yellow")

  else if (r["KEY"] == 2): circle(5, "solid", "blue")

  else if (r["KEY"] == 3): circle(5, "solid", "green")

  else if (r["KEY"] == 4): circle(5, "solid", "brown")

  else if (r["KEY"] == 5): circle(5, "solid", "orange")

  else: circle(5, "solid", "red")

  end

end

Nancy B analyzed a GSMArena Mobile Devices dataset containing 10,000+ phones. Here is some of the Pyret code she wrote to identify discontinued models:

examples: 

  is-discontinued(phone1) is phone1["STATUS"] == "Discontinued"

  is-discontinued(phone6) is phone6["STATUS"] == "Discontinued"

  is-discontinued(phone1) is "Discontinued" == "Discontinued"

  is-discontinued(phone6) is "continued" == "Discontinued"

  is-discontinued(phone1) is true

  is-discontinued(phone6) is false

end

# Consumes a row and tests what the percentage of phones are discontinued

# is-discontinued :: (r :: Row) --> Boolean

fun is-discontinued(r): r["STATUS"] == "Discontinued" end

Makenzie E analyzed a Connecticut UCR Crime Index dataset containing 39,879 incidents in the Hartford area during 2019. Here is some of the Pyret code she wrote to compare crime patterns by time of day:

fun is-early(r): (r["TIME-24HR"] >= 0) and (r["TIME-24HR"] <= 600) end

fun is-late(r): (r["TIME-24HR"] >= 1800) and (r["TIME-24HR"] <= 2300) end

early-morning-reports-only = ct-police-incidents-table.filter(is-early)

late-night-reports-only = ct-police-incidents-table.filter(is-late)