Advanced data preparation practical 1

Overview

Teaching: 0 min
Exercises: 40 min

Questions

Objectives

Exercise 1

library(palmerpenguins)
penguins

Exercise 1 tasks and solution

Your tasks are as follows:

1) Have a look at the data and can you see any issues you may have? after noticing the problem, its time to clean the data

2) Convert the “sex” label to 1 and 0s.

3) you have created 4 models for predicting the sex of penguins with different variables. the AIC values from you models are as follows model1:50, model2:48, model3:60, model4:53. Using a Alpha = 0.05, which models can be dismissed?

4) build a linear model using the penguin dataset base on sex prediction and use the drop1 method to find the optimal collection of variables.

Solution

1) The issue is that we have some missing values and if we want to do any sort of linear regression they will need to be removed. 1) penguins=na.omit(penguins) 1) penguins

2) either penguin$sex <- ifelse(penguin$sex == “male”, 0, 1) or penguin$sex <- ifelse(penguin$sex == “female”, 0, 1)

3) assuming to 3dp, model1: 0.368, model3:0.003, model4:0.082. We can therefore reject model3. 3) What value would model4 need to get for us to reject it?

4) lm1 <- lm(sex ~ ., data = penguins)

4) drop1(lm1, test = “F”) # So called ‘type II’ anova

4) species + island + bill_length_mm + bill_depth_mm + flipper_length_mm + body_mass_g + year

Key Points