---
title: Getting started with R
subtitle: Exercises for Day 1
author: Your name
date: '2025-11-26'
format: html
embed-resources: true
---

## Setting up a new project
### Create a new project for exercises 

::: {.callout-note}
### Note
This project will be used for this the exercise only. Give it a name to reflect this. Remember the conventions for naming style.


<!--- Create a new quarto document, give it an appropriate title and save the file.
  - Remember that titles should be written in "plain english" and the file should be saved with file name conventions (*e.g.,* `plain_english.qmd`). -->
  
I strongly recommend not using an LLM for this exercise. Figuring this out on your own is one of the most critical lessons for the day.

:::
  

### Set up

For this exercise we will need two packages: `tidyverse` and `nycflights13`. Load the two packages. What is different between loading tidyverse and nycflights13? 

Remember the importance of documentation: use hash symbols to create a section in the .qmd file, use code chunks, document your work.  

The nycflights13 package will quietly (in the background, not visible in your Environment) load a data object called "flights". Use one of the functions we have learned today to view the flights data. 

```{r}
# Write answer here
```


## Exploratory analysis

>A very common situations you will find yourself in research: You get some data - you don't know what format it is, what it's meant to look like, you don't yet know what questions you can ask of this data. 

Use functions we have encountered today to answer the following questions: 

### How much data is in `flights`?

How would you measure data in a table?

```{r}
# Write answer here
```


### What type of data is stored? 

Look at the column names of the data (`colnames()`). What do `dep_time`, `arr_time`, and `air_time` mean? With these three variables we can carry out a simple but important test - can you identify what that test is? 

  - We can perform a similarly important test using just `dep_time` and `arr_time`.
  
 

\

### Hypothesis generation
Given the data variables in flights, propose two hypotheses that we could test with this dataset. 



## Exploring values and distributions of numerical variables


### Explore categorical variables

Instead of passing all the data in an object to a function we can specify a single variable with `$`. Use `flights$variable_name` to pass a single variable to a function. 

*e.g.,*
```{r}
flights$carrier |> unique()
```

Use the functions `unique()` and `n_distinct()` to answer:

### What are the locations flights are leaving from?

```{r}
# Write answer here
```

### How many destinations are these flights leaving for?

```{r}
# Write answer here
```

### Use functions `range()`, `mean()`, `median()` to gauge the level of departure delays

::: {.callout-note}
### Note
We will need to add `na.rm = TRUE` as an argument within `range()`, `mean()`, and `median()`. 

If we do not pass `na.rm = TRUE`as an argument the functions will detect the presence of NAs in the data and return NA.
:::


```{r}
# Write answer here
```

## Exploratory analysis with `ggplot`

### Use `ggplot` to look at associations between two variables.

```{r}
# Write answer here
```


### Can you use ggplot to test the hypotheses **you** generated above?

::: {.callout-note}
### My hypothesis

Here's a plot to test my hypothesis. Can you identify my hypothesis? 

```{r}
ggplot(flights,
       aes(x = dep_time,
           y = dep_delay)) +
  geom_point()
```
:::


### Extra message
Depending on your choice of variables your plot (and the plot above) may produce a message that looks like this: 

```
Warning: Removed 8255 rows containing missing values or values outside the scale range
(`geom_point()`).
```

What type of message is this?
What does this message mean? What steps, if any, do you recommend taking in response?