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:

peng_data <- palmerpenguins::penguins %>% na.omit()Copy Code

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

GGally::ggscatmat(data, columns = c("variable 1", "variable 2", "variable 3"))Copy Code

Task: using peng_data, use the GGally::ggscatmat() function on 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:

correlation::correlation(data,
                         method = "pearson",
                         p_adjust = "holm",
                         ci = 0.95
                         )Copy Code

• If we are happy with these defaults, we can pipe in the data we want to use, and select the variables:

data %>% 
  dplyr::select(variable_1, variable_2) %>% 
correlation::correlation()Copy Code

Task: perform a correlation with our peng_data, on the body_mass_g & flipper_length_mm variables, save it in an object (<-)

Correlations: Tests

peng_cor <- peng_data %>% 
  dplyr::select(body_mass_g, flipper_length_mm) %>%
  correlation::correlation()
peng_corCopy Code

## # 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: 333Copy Code

peng_cor <- peng_data %>% 
  dplyr::select(body_mass_g, flipper_length_mm) %>% 
  correlation::correlation(digits = 3, ci_digits = 3)Copy Code

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)

t.test(outcome ~ binary_predictor, data = data, paired = FALSE, var.equal = FALSE, conf.level = 0.95)Copy Code

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

body_mass_test <- t.test(body_mass_g ~ sex, data = peng_data)
body_mass_testCopy Code

## 
##     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             4545.685Copy Code

Comparing Independent Means: Effect Size

• We can calculate effect size just as easy as doing a t-test with the effectsize::cohens_d() function:

cohens_d <- effectsize::cohens_d(outcome ~ binary_predictor, data = data)Copy Code

Task: calculate cohen's d for the t-test we just did! Make sure to save it in an object

cohens_d <- effectsize::cohens_d(body_mass_g ~ sex, data = peng_data)
cohens_dCopy Code

## Cohen's d |         95% CI
## --------------------------
## -0.94     | [-1.16, -0.71]
## 
## - Estimated using pooled SD.Copy Code

ExtRa: Reporting Results

• 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

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

learnr::run_tutorial("discovr_08", package = "discovr")Copy Code