Lily McMullen Module2_Assignment1.Rmd

---
title: "Module 2, Assignment 1"
author: "Ellen Bledsoe" 'Lily McMullen'
date: '2022-09-29'
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Assignment Details

### Purpose

The goal of this assignment is to assess your ability to produce and interpret histograms and scatter plots.

### Task

Write R code which produces the correct answers and correctly interpret the plots produced.

### Criteria for Success

-   Code is within the provided code chunks
-   Code is commented with brief descriptions of what the code does
-   Code chunks run without errors
-   Code produces the correct result
    -   Code that produces the correct answer will receive full credit
    -   Code attempts with logical direction will receive partial credit
-   Written answers address the questions in sufficient detail

### Due Date

October 11 at midnight MST

# Assignment Questions

For this assignment, we are going to be making plots! We are going to use a data set called `penguins` from the `palmerpenguins` package.

Most of the code you will need to complete this assignment is code we used in the first lesson of this module, [1_FoodGoneBad](https://rstudio.cloud/spaces/269799/content/4554717).

1.  Load both the `palmerpenguins` package and the `tidyverse` package into the workspace. (2 points)

```{r}
library(tidyverse)
library(palmerpenguins)
```

When we use data from a data package, it doesn't automatically show up in our environment. Run this code chunk so it does show up in the environment.

```{r}
penguins <- penguins
```

2.  Use the `head()` function to take a look at the `penguins` data frame. (1 points)

```{r}
head(penguins)
```

3.  Make a histogram of the body mass column. (2 points)

```{r}
hist(penguins$body_mass_g)
```

4.  In 2-3 sentences, describe what the histogram is telling you. I'm not necessarily looking for technical answers, but I want you to practice interpreting what histograms are telling you. (Examples: Are there even numbers of each body mass or different? Where is the peak? Are there lots of heavy penguins or not?) (3 points)

*Answer: The histogram is telling us that most of the penguins are around 3500-4000g. However, there are some penguins form 3000-6000g, and very few penguins under 3000g and over 6000g.*

5.  Let's make the histogram in question 4 a bit easier for others to understand. (4 points)

Make the following changes:

-   remove the main title (set the "main" argument to `NULL`)
-   make the x-axis (horizontal) label easier to understand
-   for the y-axis (vertical) label, add more detail ("Frequency" of what?)
-   add total counts above each bin (this is controlled by the `labels` argument)

```{r}
hist(penguins$body_mass_g,
     main = NULL,
     xlab = "Penguin Mass (g)",
     ylab = "Frequency of Masses",
     labels = TRUE)
```

6.  Copy the code you wrote for question 5 and paste it below. Using the `abline()` function, add a vertical line to this histogram that represents the average penguin body mass. Make that line red. (2 points)

    Hint: you will need to include the `na.rm = TRUE` argument in the `mean()` function for the line to appear.

```{r}
hist(penguins$body_mass_g,
     main = NULL,
     xlab = "Penguin Mass (g)",
     ylab = "Frequency of Masses",
     labels = TRUE)
abline(v = mean(penguins$body_mass_g, na.rm = TRUE), col = "red")
```

7.  Make a scatter plot with flipper length on the x-axis (horizontal) and bill length on the y-axis (vertical). (2 points)

```{r}
plot(x = penguins$flipper_length_mm, y = penguins$bill_length_mm)
```

8.  Write 1-2 sentences interpreting this plot. (Examples: Is there a positive relationship or a negative relationship? As flipper length increases, does bill length tend to increase, decrease, or stay the same? Is a penguin with a long flipper likely to have a long bill, too?) (2 points)

*Answer: As flipper length increases, bill length tends to increase.*

9.  As with the histogram above, we want to make this plot easier for others to understand. Change the axis labels so that they are clearer. (2 points)

```{r}
plot(x = penguins$flipper_length_mm, y = penguins$bill_length_mm,
     xlab = "Penguin Flipper Length (mm)", ylab = "Penguin Bill Length (mm)")
```