• Meet with those that you are reviewing
• Answer the questions on the next slide as you review their work on a sheet of paper that you will turn in to me:

Data set

1. Is the data set sociology related? If you aren't sure, have them explain.
   
2. Were they able to upload their data to the RStudio Server? What file format is it? Were they able to read it into R as a data frame?
3. Were they correct in identifying the "tidy"-ness of their data? Explain.
4. Were they correct on identifying how easy the variable names were to understand?

Sociology article

1. Is the article related to sociology?
2. Does it use data visualization to convey a message?
3. Do they do a good job of explaining how data visualization is used?

• Are there typos in their paragraphs? If so, correct the typos for them.

The mosaic package has the following 4 functions will give us (most of) the random simulation tools we need. We've seen

1. rflip(): Flip a coin
2. shuffle(): Shuffle a set of values
3. do(): Do the same thing many, many, many times
4. resample(): the swiss army knife of functions

• Today we do shuffle() and resample()

A huge distinction in types of sampling:

1. Sampling with replacement
2. Sampling without replacement

In the Powerball analogy, this translates to:

1. After picking a ball, putting it back into the machine
2. After picking a ball, leaving it out. What the lottery does in real life.

Shuffling AKA re-ordering AKA permuting are all synonyms. I'm going to use all three terms interchangeably.

Run the following in your console:

library(mosaic)
# Define a vector fruit
fruit <- c("apple", "orange", "mango")

# Do this multiple times:
shuffle(fruit)

This works with the do() operator…

do(5) * shuffle(fruit)

… as well as within a mutate()

example_data <- data_frame(
  name = c("Ilana", "Abbi", "Hannibal"),
  fruit = c("apple", "orange", "mango")
)

# Run this multiple times: 
example_data %>% 
  mutate(fruit = shuffle(fruit))

At its most basic, resample() resamples the input vector with replacement. Run this in the console multiple times:

resample(fruit)

• You can get the same fruit all three times i.e. sampling with replacement
• resample() has default settings that we can set to fit our needs; it is a swiss army knife.
• Let's unpack the defaults:

resample(x = fruit, size = length(x), replace = TRUE, 
         prob = rep(1 / length(x), length(x)) )

• x is the input. In this case fruit.
• size: size of output vector. By default the same size as x.
• replace: Sample with or without replacement. By default with replacement.
• prob: Probability of sampling each input value. By default, equal probability
• Run rep(1/length(fruit), length(fruit)) in your console. In the case of fruit, this vector is rep(1/3, 3) i.e. repeat 1/3 three times.

1. Rewrite rflip(10) using the resample() command.

   Hint: coin <- c("H", "T")

2. Rewrite the shuffle() command by changing the minimal number of default settings of resample(). Test this on fruit.
3. Write code that will allow you to generate a sample of 15 fruit without replacement.
4. Write code that will allow you to generate a sample of 15 fruit with replacement.

5. What's the fastest way to do the above (in Question 4) 5 times? Write it out.

• Say its the early 1900's, and you are a statistician and you meet someone who claims to be able to tell by tasting whether the tea or the milk was added first to a cup.
• You call BS and think they are just guessing.
• Say you have 8 cups, tea, and milk handy. How would you design an experiment to test whether a) they can really tell which came first or b) they are just guessing?
• Brainstorm all the components of this experiment with your seatmates.
• Then think about how you can implement this with resample()ing.

Describe the process of bootstrapping. (You can use your notes if you like, but you can't use the textbook.)