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R in 3 Months (Fall 2022)

Statistical Tests

Transcript

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In the video below Charlie walks through everything you need to know about using the 31 different statistical test functions built into R.

These built in functions come from the {stats} package - part of base R. For that reason they're designed to be given vectors instead of data.frame. Here's the code that Charlie uses in the video to perform a t.test on the penguins dataset

library(tidyverse)
library(palmerpenguins)

penguins_adelie <- penguins %>% 
  filter(species == "Adelie")

penguins_chinstrap <- penguins %>% 
  filter(species == "Chinstrap")

t.test(penguins_adelie$body_mass_g, penguins_chinstrap$body_mass_g)

Unfortunately, this output is unstructured and difficult to work with. But the tidyverse has our back!We can use the {broom} package to tidy the results into a data.frame

library(broom)
t.test(penguins_adelie$body_mass_g, penguins_chinstrap$body_mass_g) %>% 
  tidy()

{gtsummary}

Charlie also demonstrates how the {gtsummary} package allows you to create attractive statistical summary tables within which you can perform statistical tests:

library(gtsummary)

penguins %>% 
  select(species, body_mass_g) %>% 
  filter(species %in% c("Chinstrap", "Adelie")) %>% 
  mutate(species = fct_drop(species)) %>% 
  tbl_summary(by = "species", missing = "no") %>% 
  add_p(
    test = everything() ~ "t.test"
  )

Have any questions? Put them below and we will help you out!

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Course Content

142 Lessons

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R in 3 Months Fall 2022 Week 10 Drop-in Session