class: center, middle, inverse, title-slide # FundRmentals 08 ### Dr Danielle Evans | University of Sussex --- # Setup & Q&A - Open RStudio - Open/create your fundRmentals R Project (click the blue cube in the top right corner of RStudio) - Open a new Rmd file for today - **If you missed last week**, install the **palmerpenguins** package - install.packages("palmerpenguins") - Install the **GGally**, **correlation**, & **effectsize** packages - In a new code chunk, load **tidyverse**, **palmerpenguins**, **GGally**, **correlation** & **effectsize** using the **library()** command - In another code chunk, create **peng_data** by copying the below code: ```r peng_data <- palmerpenguins::penguins %>% na.omit() ``` .center[ ### Any questions from the last tutorial? ] --- # Overview - Correlations - Independent *t*-test - Reporting results with inline code (extRa) - Next steps --- # Correlations: Visualization - Visualising relationships is made easy with the **GGally::ggscatmat()** function - **GGally::ggscatmat()** shows us a correlation matrix, with distributions, scatterplots, & correlation coefficients - To use it, we give the names of our data & the variables we want to examine ```r GGally::ggscatmat(data, columns = c("variable 1", "variable 2", "variable 3")) ``` <br> <br> <br> <br> <br> <br> <br> **Task**: using **peng_data**, use the GGally::ggscatmat() function on **body_mass_g**, **flipper_length_mm**, **bill_depth_mm** --- .center[ ```r GGally::ggscatmat(peng_data, columns = c("body_mass_g", "flipper_length_mm", "bill_depth_mm")) ``` <!-- --> ] --- # Correlations: Tests - We can use the correlation::correlation() function to perform a correlation, the default options are given below: ```r correlation::correlation(data, method = "pearson", p_adjust = "holm", ci = 0.95 ) ``` <br> - If we are happy with these defaults, we can pipe in the data we want to use, and select the variables: ```r data %>% dplyr::select(variable_1, variable_2) %>% correlation::correlation() ``` <br> **Task**: perform a correlation with our **peng_data**, on the **body_mass_g** & **flipper_length_mm** variables, save it in an object (<-) ??? p_adjust. By default the function corrects the p-value for the number of tests you have performed (a good idea) using the Holm-Bonferroni method, which applies the Bonferroni criterion in a slightly less strict way that controls the Type I error rate but with less risk of a Type II error. You can change this argument to none (i.e. don’t correct for multiple tests, a bad idea), bonferroni (to apply the standard Bonferroni method) or several other methods. --- # Correlations: Tests .tiny[ ```r peng_cor <- peng_data %>% dplyr::select(body_mass_g, flipper_length_mm) %>% correlation::correlation() peng_cor ``` ``` ## # Correlation Matrix (pearson-method) ## ## Parameter1 | Parameter2 | r | 95% CI | t(331) | p ## -------------------------------------------------------------------------- ## body_mass_g | flipper_length_mm | 0.87 | [0.84, 0.90] | 32.56 | < .001*** ## ## p-value adjustment method: Holm (1979) ## Observations: 333 ``` <br> We can even edit the number of decimal places if we wanted to: ```r peng_cor <- peng_data %>% dplyr::select(body_mass_g, flipper_length_mm) %>% correlation::correlation(digits = 3, ci_digits = 3) ``` ] --- # Comparing Independent Means: *t*-test - We can use the t.test function to perform a *t*-test, this function has the following arguments & defaults: - We sub in our column names for the outcome & predictor, the name of our dataset, whether we want a paired *t*-test or not, whether we want Welch's correction (the correction is the default), the confidence interval, and whether to exclude missing data (na.action = na.exclude) .tiny[ ```r t.test(outcome ~ binary_predictor, data = data, paired = FALSE, var.equal = FALSE, conf.level = 0.95) ``` ] <br> <br> <br> <br> <br> **Task**: perform an independent *t*-test with our **peng_data**, using the **sex** & **body_mass_g** variables, keep the default options of Welch's correction & 95% CIs, give the object a name (<-) --- # Comparing Independent Means: *t*-test ```r body_mass_test <- t.test(body_mass_g ~ sex, data = peng_data) body_mass_test ``` ``` ## ## Welch Two Sample t-test ## ## data: body_mass_g by sex ## t = -8.5545, df = 323.9, p-value = 4.794e-16 ## alternative hypothesis: true difference in means is not equal to 0 ## 95 percent confidence interval: ## -840.5783 -526.2453 ## sample estimates: ## mean in group female mean in group male ## 3862.273 4545.685 ``` --- # Comparing Independent Means: Effect Size - We can calculate effect size just as easy as doing a *t*-test with the effectsize::cohens_d() function: ```r cohens_d <- effectsize::cohens_d(outcome ~ binary_predictor, data = data) ``` <br> **Task**: calculate cohen's d for the *t*-test we just did! Make sure to save it in an object <br> <br> -- ```r cohens_d <- effectsize::cohens_d(body_mass_g ~ sex, data = peng_data) cohens_d ``` ``` ## Cohen's d | 95% CI ## -------------------------- ## -0.94 | [-1.16, -0.71] ## ## - Estimated using pooled SD. ``` --- # ExtRa: Reporting Results .pull-left[ - We can report the results of these tests using inline code: - By saving all our results into objects we can access the values contained within them ] .pull-right[ <img src="../img/inline.png" width="80%" style="display: block; margin: auto;" /> ] - We need to know the name of the object & the name of the element - We can look in our environment pane & click the blue 'play button' next to the name of the object to see what it contains & what the elements are called .pull-left[ - We can then select individual elements by using the $ - If there's more than one value in an element, we can use [] & the number of the one we want ] .pull-right[ <img src="../img/inl2.png" width="90%" style="display: block; margin: auto;" /> ] **Task**: try it out on some of your results & knit your doc! --- # Next Steps Save the RMarkdown document from today **Ctrl + S** or **Command + S** For next week you should complete the **discovr_08** tutorial on the linear model by pasting the below code into the **Console** in RStudio, pressing **Enter** ```r learnr::run_tutorial("discovr_08", package = "discovr") ``` ??? write_csv(peng_cor, file = "cor.csv") write_csv(broom::tidy(body_mass_test), file = "ttest.csv") write_csv(cohens_d, file = "cohens.csv")