penguins-lm2

lm

en

regression

penguins

Published

September 12, 2024

1 Exercise

Consider the dataset penguins. Compute a linear model with body mass as output variable (DV) and a) flipper length and b) sex as input (IV).

Tidy up the data set, if and where needed.
Report the coefficients and interpret them.
Plot the model and the coefficients.
Report the model fit (R squared).
BONUS: predict() the weight of an average flipper-sized animal (male and female). Check out the internet for examples of how to do so in case you need support.

2 Solution

2.1 Setup

library(tidyverse)
library(easystats)
library(ggpubr)  # visualization

# import data:
penguins <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/palmerpenguins/penguins.csv")

2.2 Tidy up

penguins_tidier <-
  penguins |> 
  select(body_mass_g, flipper_length_mm, sex) |> 
  drop_na() |> 
  filter(sex != "")  # maybe better to be excluded

Note that, strangely, there are some animals for which the sex is reported as "", an empty string value. This is not the same as NA. However, we may want the exclude such animals of unclear sex.

2.3 Let’s go

lm2 <- 
  lm(body_mass_g ~ flipper_length_mm + sex, 
          data = penguins_tidier)

Plot the model:

plot(estimate_relation(lm2))

---
# gleich diese Datei in einem Ordner mit Namen der Aufgabe abspeichern!
date: 2024-09-12  # HEUTIGES DATUM EINTRAGEN, nicht "today"
draft: FALSE   # ACHTUNG DRAFT STEHT AUF TRUE!
title: penguins-lm2  # HIER TITEL DES POSTS EINGEBEN.
execute: 
  
  eval: true 
highlight-style: arrow 
cache: true
toc: true
number-sections: true
extype: string
exsolution: ""
exshuffle: no
categories:
- lm  # ENTER CATEGORIES HERE
- en
- regression
- penguins


bibliography: "../../library-ses.bib"


---





```{r}
#| include: false

options(digits = 2)
```



















# Exercise


Consider the dataset `penguins`. 
Compute a linear model with *body mass* as output variable (DV) and a) *flipper length* and b) *sex* as input (IV). 


1. Tidy up the data set, if and where needed.
2. Report the coefficients and interpret them.
3. Plot the model and the coefficients.
4. Report the model fit (R squared).
5. BONUS: `predict()` the weight of an average flipper-sized animal (male and female).  Check out the internet for examples of how to do so in case you need support.



# Solution

## Setup

```{r}
library(tidyverse)
library(easystats)
library(ggpubr)  # visualization

# import data:
penguins <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/palmerpenguins/penguins.csv")
```


## Tidy up

```{r}
penguins_tidier <-
  penguins |> 
  select(body_mass_g, flipper_length_mm, sex) |> 
  drop_na() |> 
  filter(sex != "")  # maybe better to be excluded
```


Note that, strangely, there are some animals for which the sex is reported as `""`, an empty string value. This is not the same as `NA`. However, we may want the exclude such animals of unclear sex.

## Let's go


```{r}
lm2 <- 
  lm(body_mass_g ~ flipper_length_mm + sex, 
          data = penguins_tidier)
```



Plot the model:


```{r}
plot(estimate_relation(lm2))
```