ch9_measurement.qmd

---
title: "Measurement Simulations: Test-Retest Reliability"
format: html
editor: visual
---

# Setup

## Learning goals

1.  Simulate data for two experiments and compute test-retest reliability
2.  Practice some tidyverse (`pivot_longer`, `mutate`, `select`, and add onto existing base ggplot skills (`geom_point`, `geom_jitter`, `facet_wrap`, `geom_line`)
3.  Run a basic correlation (`cor.test` and interpret differences in observed reliability based on differences in the simulated data)

## Import the libraries we need

```{r}
library(tidyverse)
library(ggplot2) # plotting

```

## Define the simulation function - same as before

This makes "tea data", a tibble (dataframe) where there are a certain number of people in each condition (default = 48, i.e., n_total, with n_total/2 in each half)

The averages of the two conditions are separated by a known effect ("delta") with some variance ("sigma"). You can change these around since we're simulating data!

```{r}
set.seed(123) # good practice to set a random seed, or else different runs get you different results
```

```{r}
make_tea_data <- function(n_total = 48,
                          sigma = 1.25,
                          delta = 1.5) {
  n_half <- n_total / 2
  tibble(condition = c(rep("milk first", n_half), rep("tea first", n_half)),
         rating = c(round(
           rnorm(n_half, mean = 3.5 + delta, sd = sigma)
         ),
         round(rnorm(
           n_half, mean = 3.5, sd = sigma
         )))) |>
    mutate(rating = if_else(rating > 10, 10, rating),
           # truncate if greater than max/min of rating scale
           rating = if_else(rating < 1, 1, rating)) %>%
  rownames_to_column() %>% # added for this excercise
  rename(sub_num = rowname)
}
```

## 1. Make a dataframe with our simulated data

1.  Input more participants (50 per condition) with a bigger average difference between conditions (2 points), with variance between participants at 2 points (sigma)

```{r}
# YOUR CODE HERE


```

## 2. Creating the second experiment

Now create a new column in your tibble for the second experiment. You should now have a dataframe with some tea rating (column `rating`)

-   We're gonna pretend that we are doing an additional experiment and collect tea ratings again! And we can simulate this data by pretending that each participant when they rerank the tea might change their rating slightly, by some number between -3 and 3 (so some participants might become more negative, and some participants might become more positive of the tea)

-   Let's call this change in rating the `difference`. you can generate this `difference` by using `sample` (`sample(-3:3, 1)` ) this gives you a random number between -3 and 3. use `?sample` to get the documentation for this function to further understand what it's doing

-   If your difference is the same for each participant \--\> that's not very realistic! It's unlikely that people will change their rating so uniformly, yeah? so what we want to do is use `rowwise()` to make sure that R is computing a new `difference` for each participant

-   Then let's create a `new_rating` column by adding the `difference` to the old `rating` column! this is the simulated result from "experiment 2"

-   Then you might want to clip the rating so that it's between 1 and 10 again (you can see the code for this from the original simulation function)

TIPS:

Recommend running `rowwise()` in your pipe before creating the new condition to force tidyverse to sample a new random value for each row

Make your next dataframe A NEW NAME so that you're not rewriting old dataframes with new ones and getting confused

```{r}
# YOUR CODE HERE

```

## 3. Make a plot and compute the correlation

Examine how the ratings are correlated across these simulations, by making a plot

```{r}
# YOUR CODE HERE

```

Hint: 1. Try out `geom_point` which shows you the exact values\
2. Then try out `geom_jitter` which shows you the same data with some jitter around height / width

Bonus: 3. Use `alpha` to make your dots transparent 4. Use `ylab` and `xlab` to make nice axes labels 5. Use `geom_smooth()` to look at the trend_line 6. Try making each dot different by condition, if you want.

Now examine -- how correlated are your responses? What is your test-retest reliability? Use `cor.test` to examine this -- the first value should be ratings from the first experiment, and the second argment is the ratings from the second experiment. You'll need the `$` operator to grab the column from the tibble (e.g., `all_tea_data$rating`)

```{r}
```

## 5. Make another plot -- like the one from the chapter, where each line should connect an individual subject

1.  First, use `pivot_longer` to make the dataframe longer -- you're going to need one column with all ratings, and a new column that says which experiment the ratings are from.
2.  In your ggplot, use `facet_wrap(~condition)` to make two plots, one for `milk_first` and one for `tea_first`
3.  Use `geom_line` -- with grouping specification by `aes(group=sub_id)` -- to connect individual datapoints between participants for each condition

```{r}
# YOUR DATA WRANGLING CODE HERE



```

```{r}
# YOUR PLOTTING CODE HERE

```

# 5. OK, now go back and change things and test your intuition about how this works.

How does reliability change if you increase the variance between participants (sigma) in the first experiment simulated data?

How does reliability change if you change how much variation you allow between the first and second experiment?

How does reliability change if you increase sample size, holding those things constant?

Hint: copy the code from above where you made your new dataframe with experiment number 2, copy the correlation computation code, and just run this block over an over, modifying the code (command-shift-enter on Mac runs the block)

```{r}
# YOUR CODE HERE


```