---
title: "M2: Looking for patterns"
author: "Your name (your SUNet ID)"
date: "`r Sys.Date()`"
output: pdf_document
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(coursekata)
library(here)
```

<!-- Fill in every line marked [...]. Knit often. Before you submit:
Session -> Restart R, then Knit, and submit this file and the PDF. -->

## 1. Your claim and your data

- **Your claim, in one sentence:** [...]
- **The dataset you're using:** [...]

```{r load-data}
# Copy the data-loading code from your M1 file, including any library()
# line it needs. (Fingers is only here so the file knits before you start.)
my_data <- Fingers
```

## 2. A histogram of one column (without AI)

**The column you chose and what it measures:** [...]

<!-- Pick one quantitative column and write the gf_histogram() code yourself,
as in CourseKata Chapter 3. -->
```{r histogram}
# Your histogram code goes here. (Replace Thumb with your column; this line
# is only here so the file knits before you start.)
gf_histogram(~ Thumb, data = my_data)
```

**The shape (where most values are, symmetric or skewed, any unusual values):** [...]

**Is it what you expected?** [...]

## 3. The same histogram, from the AI

<!-- Ask the AI for the same histogram in plain words, with no code (for
example: "Make a histogram of life expectancy in 2007 from the gapminder
data"). Paste the code it gives you into the chunk below. -->

**What you asked the AI:** [...]

```{r histogram-ai}
# The AI's code goes here.

```

**Is it the same as yours? If not, what is different?** [...]

**Does the difference change what you said about the shape in step 2?** [...]

## 4. Which columns could test your claim

<!-- Look at the columns in str(), as in M1. Which ones could you use to test
your claim, and what would each one tell you about it? Add lines if you need
them. -->

- **Column:** [...] **What it would tell you about the claim:** [...]
- **Column:** [...] **What it would tell you about the claim:** [...]

## 5. What you asked the AI

<!-- Ask in plain words: say what your data looks like (paste the str()
output), name the columns from step 4, and state the claim you want the plot
to check. Copy your request here. -->

[...]

## 6. The plot, and what you see

```{r claim-plot}
# Paste the AI's code for the plot here. (The gf_point() line is only here so
# the file knits before you start; replace it.)
gf_point(Thumb ~ Height, data = my_data)
```

**What pattern do you see? (Or do you see none?)** [...]

**Is it what you expected?** [...]

**Does this plot help you answer your claim? Why or why not?** [...]

**One thing you learned about the data from looking at this plot:** [...]

## 7. What you'd want to find out next

<!-- In one or two sentences: what about your claim or your data you'd most
like to look into for the project, and why. -->

[...]