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Yarrr! the Pirate’S Guide to R 6 2dr. nathaniel d. phillips YaRr! The Piate’s Guide to R DR. NATHANIEL D. PHILLIPS YARRR! THE PIRATE’S GUIDE TO R 6 10: Plotting: Part Deux 157 Advanced colors 157 Plot margins 162 Arranging multiple plots with par(mfrow) and layout 163 Additional Tips 166 11: Inferential Statistics: 1 and 2-sample Null-Hypothesis tests 167 Null vs. Alternative Hypotheses, Descriptive Statistics, Test Statistics, and p-values: A very short introduction 168 Null v Alternative Hypothesis 168 Hypothesis test objects – htest 172 T-test with t.test() 174 Correlation test with cor.test() 178 Chi-square test 181 Getting APA-style conclusions with the apa function 183 Test your R might! 185 12: ANOVA and Factorial Designs 187 Between-Subjects ANOVA 188 4 Steps to conduct a standard ANOVA in R 190 y x1 * x2 194 ANOVA with interactions: ( ⇠ ) Additional tips 197 Test your R Might! 200 13: Regression 201 The Linear Model 201 Linear regression with lm() 201 Estimating the value of diamonds with lm() 202 Including interactions in models: dv x1 *x2 206 ⇠ Comparing regression models with anova() 208 Regression on non-Normal data with glm() 211 7 Getting an ANOVA from a regression model with aov() 214 Additional Tips 215 Test your Might! A ship auction 217 14: Writing your own functions 219 Why would you want to write your own function? 219 The basic structure of a function 220 Additional Tips 225 Test Your R Might! 230 15: Loops 231 What are loops? 232 Creating multiple plots with a loop 235 Updating objects with loop results 236 Loops over multiple indices 237 When and when not to use loops 238 Test your R Might! 240 16: Data Cleaning and preparation 241 The Basics 241 Splitting numerical data into groups using cut() 245 Merging two dataframes 247 Random Data Preparation Tips 249 Appendix 251 Index 255 11: Inferential Statistics: 1 and 2-sample Null-Hypothesis tests In this chapter we’ll cover 1 and 2 sample null hypothesis tests: like the t-test, correlation test, and chi-square test: library(yarrr) # Load yarrr to get the pirates data # 1 sample t-test # Are pirate ages different than 30 on average? t.test(x = pirates$age, mu = 30) # 2 sample t-test # Do females and males have different numbers of tattoos? sex.ttest <- t.test(formula = tattoos ~ sex, data = pirates, Figure 64: Sadly, this still counts as just subset = sex %in% c("male", "female")) one tattoo. sex.ttest # Print result ## Access specific values from test sex.ttest$statistic sex.ttest$p.value sex.ttest$conf.int # Correlation test # Is there a relationship between age and height? cor.test(formula = ~ age + height, data = pirates) # Chi-Square test # Is there a relationship between college and favorite pirate? chisq.test(x = pirates$college, y = pirates$favorite.pirate) 168 yarrr! the pirate’sguidetor Do we get more treasure from chests buried in sand or at the bot- tom of the ocean? Is there a relationship between the number of scars a pirate has and how much grogg he can drink? Are pirates with body piercings more likely to wear bandannas than those without body piercings? Glad you asked, in this chapter, we’ll answer these questions using 1 and 2 sample frequentist hypothesis tests. As this is a Pirate’s Guide to R, and not a Pirate’s Guide to Statis- tics, we won’t cover all the theoretical background behind frequentist null hypothesis tests (like t-tests) in much detail. However, it’s impor- tant to cover three main concepts: Descriptive statistics, Test statistics, and p-values. To do this, let’s talk about body piercings. Null vs. Alternative Hypotheses, Descriptive Statistics, Test Statis- tics, and p-values: A very short introduction As you may know, pirates are quite fond of body piercings. Both Body Piercing Survey as a fashion statement, and as a handy place to hang their laundry. 9 Now, there is a stereotype that European pirates have more body 8 piercings than American pirates. But is this true? To answer this, I 7 6 conducted a survey where I asked 10 American and 10 European 5 pirates how many body piercings they had. The results are below, 4 3 and a Pirateplot of the data is in Figure 65: Number of Body Piercings 2 1 american.bp <- (3, 5, 2, 1, 4, 4, 6, 3, 5, 4) c American European european.bp <- c(8, 5, 9, 7, 6, 8, 8, 6, 9, 7) Group Figure 65: A Pirateplot (using the pirateplot() function in the yarrr package) of the body piercing data Null v Alternative Hypothesis . In null hypothesis tests, you always start with a null hypothesis. The specific null hypothesis you choose will depend on the type of ques- tion you are asking, but in general, the null hypothesis states that nothing is going on and everything is the same. For example, in our body piercing study, our null hypothesis is that American and European Null Hypothesis: Everything is the pirates have the same number of body piercings on average. same Alternative Hypothesis: Everything is The alternative hypothesis is the opposite of the null hypothesis. In not the same this case, our alternative hypothesis is that American and European pirates do not have the same number of piercings on average. We can have different types of alternative hypotheses depending on how specific we want to be about our prediction. We can make a 1-sided (also called 1-tailed) hypothesis, by predicting the direction of the difference between American and European pirates. For example, our alternative hypothesis could be that European pirates have more piercings on average than American pirates. 11: inferential statistics: 1and2-sample null-hypothesis tests 169 Alternatively, we could make a 2-sided (also called 2-tailed) alterna- tive hypothesis that American and European pirates simply differ in their average number of piercings, without stating which group has more piercings than the other. Once we’ve stated our null and alternative hypotheses, we collect data and then calculate descriptive statistics. Descriptive Statistics Descriptive statistics (also called sample statistics) describe samples of data. For example, a mean, median, or standard deviation of a dataset is a descriptive statistic of that dataset. Let’s calculate some descriptive statistics on our body piercing survey American and European pirates using the summary() function: summary(american.bp) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 1.00 3.00 4.00 3.70 4.75 6.00 summary(european.bp) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 5.00 6.25 7.50 7.30 8.00 9.00 Well, it looks like our sample of 10 American pirates had 3.7 body piercings on average, while our sample of 10 European pirates had 7.3 piercings on average. But is this difference large or small? Are we justified in concluding that American and European pirates in general differ in how many body piercings they have? To answer this, we need to calculate a test statistic Test Statistics An test statistic compares descriptive statistics, and determines how different they are. The formula you use to calculate a test statistics depends the type of test you are conducting, which depends on many factors, from the scale of the data (i.e.; is it nominal or inter- val?), to how it was collected (i.e.; was the data collected from the same person over time or were they all different people?), to how its distributed (i.e.; is it bell-shaped or highly skewed?). For now, I can tell you that the type of data we are analyzing calls for a two-sample T-test. This test will take the descriptive statistics from our study, and return a test-statistic we can then use to make a decision about whether American and European pirates really differ. To calculate a test statistic from a two-sample t-test, we can use the 170 yarrr! the pirate’sguidetor t.test() function in R. Don’t worry if it’s confusing for now, we’ll go through it in detail shortly. bp.test <- t.test(x = american.bp, y = european.bp, alternative = "two.sided") I can get the test statistic from my new bp.test object by using the $ operator as follows: bp.test$statistic ## t ## -5.68 It looks like our test-statistic is 5.68. If there was really no differ- − ence between the groups of pirates, we would expect a test statistic close to 0. Because test-statistic is 5.68, this makes us think that − there really is a difference. However, in order to make our decision, we need to get the p-value from the test. p-value The p-value is a probability that reflects how consistent the test statistic is with the hypothesis that the groups are actually the same. p-value Assuming that there the null hypothesis is true (i.e.; that there is no difference between the groups), what is the probability that we would have gotten a test statistic as extreme as the one we actually got? For this problem, we can access the p-value as follows: bp.test$p.value ## [1] 2.3e-05 The p-value we got was .000022, this means that, assuming the two populations of American and European pirates have the same number of body piercings on average, the probability that we would obtain a test statistic as large as 5.68 is around 0.0022%. This is very − small – in other words, it’s close to impossible. Because our p-value is so small, we conclude that the two populations must not be the same.
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