across_microflip_3.Rmd

---
title: "How to Use the _across()_ function"
subtitle: "mutate or summarize across many columns at once"  
author: 
  - "Peter Higgins"
date: '`r Sys.Date()`'
output:
  xaringan::moon_reader:
    lib_dir: libs
    css: xaringan-themer.css
    nature:
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
      ratio: 16:9
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(fig.width = 6, message = FALSE, warning = FALSE, comment = "", cache = FALSE, fig.retina = 3)

library(flipbookr)
library(xaringan)
library(xaringanthemer)
library(tidyverse)
library(medicaldata)
data <- medicaldata::blood_storage %>% as_tibble()
```

```{r xaringan-themer, include=FALSE, warning=FALSE}
library(xaringanthemer)
style_duo_accent(
  primary_color = "#00274C",
  secondary_color = "#FFCB05",
  inverse_header_color = "#FFFFFF"
)
```

### How to Use Across to Act on Many Columns at Once


Format: <br>

summarize(across(logic.test = which variables, _function to apply()_))

**Example 3:** calculate multiple summary measures across several columns

We will calculate mean, median, and sd for Age, & Tumor Volume for prostate cancer

---

```{r across3, include = FALSE, eval = FALSE}
data %>%
  group_by(Recurrence) %>% 
# now do summarize across
  summarize(across(c(Age, TVol), 
                list(mean = mean, 
                     median = median,
                     sd = sd), 
                na.rm = TRUE,
            .names = "{col}__{fn}")) 
# Format:
# summarize(across(logic.test, function, .names))

```

`r chunk_reveal("across3", 
    break_type = "auto", 
    title = "Multiple Summary Functions Across Several Variables")`