---
title: "Problem Set 1"
subtitle: "H2 Statistics for Marine Science"
author: "Name"
date: today
format:
  html:
    theme: lux
    code-block-bg: true
    code-link: true
    toc: true
    number-sections: true
    callout-appearance: simple
    embed-resources: true
editor: visual
---

# Problem Set 1

::: callout-note
Please render this file to .html and submit via Brightspace.
:::

```{r}
#| message: false
#| warning: false
#| code-fold: false

library(tidyverse); theme_set(theme_classic())
library(readxl)
set.seed(2026)
```

## R Basics

### Data types

Create a vector called `site_num` with the values 1 through 10.

```{r}

```

Create a matrix called `pop_abund` with the values 101 through 120, with 2 columns and 10 rows.

```{r}

```

Create a copy of the dataframe `iris` (already available as `iris`) and name it `iris_df`. View the column names and create a new column called `Petal.Area`, calculated as `Petal.Width` times `Petal.Length`.

```{r}

```

## Data munging

The *SmoltWeights* sheet from H2SMS_practicalData.xlsx contains the weight in grams (column `weight_g`) from a sample of 34 wild salmon smolts (column `fish_id`) during their out-migration from a river to the ocean. Load the dataset and perform basic summaries (range, mean, median, number of missing values for each column). Identify (using code) which fish are missing values for `weight_g`.

```{r}

```

Create a new column called `weight_lb` that gives the weights in pounds, where 1 kg = 2.205 lb.

```{r}

```

What proportion of fish weigh less than 0.2 lb? How many fish is this?

```{r}

```

Create a new data.frame called `large_df` that contains only the fish as large or larger than the median weight *and* excludes rows with missing weights.

```{r}

```

Create a new data.frame called `extreme_df` that contains only fish more than 1.5 standard deviations greater than the mean *and* excludes rows with missing weights.

```{r}

```

## Plotting

### Histograms

Create a histogram of the smolt weights, labelling the axes and omitting a plot title. Add a figure legend using `#| fig-cap: "..."` in the quarto document code block. Add `#| label: fig-smoltHist`, and then in *text* below, reference the figure by typing `@fig-smoltHist`.

::: callout-tip
When rendered, `@fig-smoltHist` in the text is automatically replaced by "Figure 1", with figure numbers updated automatically. Adding a legend with `#| fig-cap: "..."` in your code block will automatically give that figure a number, making your life easier.

The label must start with `fig-` for this to work.

Note that you *cannot* re-use the same `label`.
:::

```{r}
#| fig-cap: "..."


```

### Line plots

The dataset `beaver1` is available in R, giving the body temperature through time of a female beaver (*Castor canadensis*), with observations every 10 minutes.

```{r}
str(beaver1)
```

Debug the following code so that it produces a line plot of body temperature by observation id. Note that `time` is given in the form `0330` for 3:30am (`?beaver1`). The `obs_id` column thus gives the ordered observations from first to last.

As an added challenge, add vertical dashed lines indicating when the beaver was active.

::: callout-tip
Set `#| eval: true` to run the code when you click Render. If you've successfully debugged, you should then see your plot.
:::

```{r}
#| eval: false

beaver_df <- beaver1
beaver_df$obs_id <- 1:nrow(beaver_df)
plot(obs_id ~ Temp, 
     data=beaver_df, 
     type="p", 
     xLab="Observation (time)",
     ylab="Body temperature (C)")
```

### Boxplots

The dataset `ToothGrowth` is available in R, giving the length (`len`) of tooth-growth cells (odontoblasts) in 60 guinea pigs under different vitamin C regimes (`supp`: OJ=orange juice, VC=ascorbic acid; `dose`: 0.5, 1, 2 mg/day). Create a boxplot of the length by dose for only the guinea pigs receiving orange juice. Write a figure caption and add a label as above.

```{r}
#| fig-cap: "..."


```

Use `ggplot()` to make the same boxplot, but with `supp` as the fill colour.

::: callout-tip
Remember to check the column types first!
:::

```{r}
#| fig-cap: "..."


```

### Scatter plots

Using the same `ToothGrowth` dataset, produce a scatter plot of odontoblast length as a function of dose, where `supp` is the point colour.

```{r}
#| fig-cap: "..."


```