Taxonomy of Data

STAT 20: Introduction to Probability and Statistics

Agenda

• Announcements
• Concept Questions: Conceptual
• Problem Set 1: Taxonomy of Data
• Break
• Concept Questions: Coding
• Lab 0 work time

Announcements

• Problem Set 1 and Lab 0 are both due Tuesday, January 23rd at 9am on Gradescope

• RQ: Summarizing Categorical Data due Monday, January 22nd at 11:59pm on Gradescope

• Please read the Ed expectations post here before posting on Ed!

Concept Questions: Conceptual

Concept Question 1 - Quick Refresher

• Head to PollEverywhere for a quick set of questions regarding Taxonomy of Data!

01:00

Concept Question 2

There’s no escape from the bird…

Images as data

• Images are composed of pixels (this image is 1520 by 1012)

• The color in each pixel is in RGB

• Each band takes a value from 0-255

• This image is data with 1520 x 1012 x 3 values.

Grayscale

• Grayscale images have only one band
• 0 is black, 255 is white
• This image is data with 1520 x 1012 x 1 values.

Grayscale

• To simplify, assume our photos are 8 x 8 grayscale images.

Images in a Data Frame

Consider the following images which are our data:

• Let’s simplify them to 8 x 8 grayscale images

Images in a Data Frame

If you were to put the data from these (8 x 8 grayscale) images into a data frame, what would the dimensions of that data frame be in rows x columns? Answer at pollev.com.

01:00

Concept Question 3

A note on variables

There are three things that “variable” could be referring to:

1. a phenomenon
2. how the phenomenon is being recorded or measured into data
   • what values can it take? (this is often an intent- or value-laden exercise!)
   • for numerical units, what unit should we express it in?
3. How the recorded data is being analyzed
   • might you bin/discretizing income data? what are the consequences of this?

• For the following question, you may work under the second definition.

What type of variable is age?

For each of the following scenarios where age could be a variable, choose the most appropriate taxonomy according to the Taxonomy of Data.

1. Ages of television audiences/demographics
2. Ages of UC Berkeley students
3. The weight of a rock

Answer at pollev.com.

01:00

Problem Set 1: Taxonomy of Data

20:00

Break

05:00

Concept Questions: Coding

• Time to make a series of educated guesses. Close your laptops!

Educated Guess 1

What will happen here?

Answer at pollev.com/<name>

1 + "one"

01:00

• “one” is a string with no link at all to the number 1
• without that link, without two objects that are recognized for their numerical value, + doesn’t work.

Educated Guess 2

What will happen here?

Answer at pollev.com/<name>

a <- c(1, 2, 3, 4)
sqrt(log(a))

01:00

Talking points - a is a vector of length four - log and sqrt are functions that will return vectors of length four - they’re nested and will be evaluated from the inside out

Educated Guess 3

What will happen here?

Answer at pollev.com/<name>

a <- 1 + 2
a + 1

01:00

• a is a not a string, it’s the name of an object that’s a number
• to overwrite a with a + 1 requires re-assigning it to a: a <- a + 1 (in some languages, a + 1 would change the value of a)
• a <- a + 1 is a good time to mention that while a = a + 1 works in R and they might see it online, its convention to use <- for many reasons including that mathematically the statement with = is confusing.

Educated Guess 4

What will happen here?

Answer at pollev.com/<name>

a <- c(1, 3.14, "seven")
class(a)

01:00

• the definition of a vector requires every element to be of the same type
• based on their reading, there are three classes that they’re familiar with: numeric, factor, and character
• there’s no way to translate “seven” into 7, so instead 1 and 3.14 must be translated into strings
• they will likely encounter this when looking at a data set in R (or other languages) and finding that vectors with what looked like numbers are stored as strings. This usually happens because there’s a single errant character that the language doesn’t know how to parse as a number.

Time to work on Lab 0

25:00

End of Lecture