ttest_flipbook.Rmd

---
title: "How to t test"
author: "Peter Higgins"
output:
  xaringan::moon_reader:
    lib_dir: libs
    css: xaringan-themer.css
    nature:
      ratio: 16:9
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
---

```{r setup, include = F}
# This is the recommended set up for flipbooks
# you might think about setting cache to TRUE as you gain practice --- building flipbooks from scratch can be time consuming
knitr::opts_chunk$set(fig.width = 6, message = FALSE, warning = FALSE, comment = "", cache = FALSE, fig.retina = 3)
library(flipbookr)
library(tidyverse)
library(infer)
library(medicaldata)
prostate <- medicaldata::blood_storage %>% as.tibble()
```

```{r xaringan-themer, include=FALSE, warning=FALSE}
library(xaringanthemer)
xaringanthemer::style_duo_accent(
  primary_color = "#00274C",
  secondary_color = "#FFCB05",
  header_font_google = google_font("Lato"),
  text_font_google   = google_font("Manrope"),
  code_font_google   = google_font("Roboto Mono"),
  base_font_size = "20px",
   code_font_size = "2rem",
)
```

### We will start with the tidy version from the {infer} package: t_test()

#### Notice that you have to state the order of the two levels of your grouping variable
---

```{r ttest1, include = FALSE}
prostate %>%
  t_test(TVol ~ AA, 
         order = c("0", "1"))
# Interpreting the results
# The t statistic is first
# followed by degrees of freedom
# then the p value
# the default alternative: two.sided
# then the confidence bounds
# output is a tibble so that it is 
# easy to use these results
```

`r chunk_reveal("ttest1", break_type = "auto", title = "Tidy version from the {infer} package")`


---

### Now we will use the baseR version: t.test()

#### Notice that you have to use   `data = .`
#### It is not quite as pipe-friendly
#### But you do not have to specify the order of the two levels.
---

```{r ttest2, include = FALSE}
prostate %>%
  t.test(TVol ~ AA, data = .)

# Interpreting the results
# The t statistic is first
# followed by degrees of freedom
# then the p value
# then the alternative hypothesis
# then the confidence bounds
# then the mean mpg for each group
```

`r chunk_reveal("ttest2", break_type = "auto", title = "Base R Version")`


---


```{css, eval = TRUE, echo = FALSE}
.remark-code{line-height: 1.5; font-size: 80%}
```

### Which version of the t test do you like better?

Discuss.