sac-assessment.Rmd

---
layout: page
title: RStudio Trainer Program
subtitle: "Tidyverse: Split-Apply-Combine Strategy - Assessment"
author:
    name: xwMOOC
    url: https://www.facebook.com/groups/tidyverse/
    affiliation: Tidyverse Korea
date: "`r Sys.Date()`"
output:
  html_document: 
    toc: yes
    toc_float: true
    highlight: tango
    code_folding: show
    number_section: true
    self_contained: true
editor_options: 
  chunk_output_type: console
---
 
``` {r, include=FALSE}
# source("tools/chunk-options.R")

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE,
                    comment="", digits = 3, tidy = FALSE, prompt = FALSE, fig.align = 'center')

```

# Multiple Choice Question {#sac-multiple-choice}

Split-Apply-Combine strategy is an attractive conceptual model in terms of removing `for` loops. from the following R workflow, which is the Split-Apply-Combine strategy?

1. `split` → `lapply` → `do.call`
1. `ddplyr`: `.data`, `.variable`, `.fun`
1. `tidyverse`: `group_by`, `do`
1. `split` → `map_dfr`
1. `group_by` → `nest()` → `mutate(map())`
1. All of them

Hint: [R 병렬 프로그래밍 - "분할-적용-병합(Split-Apply-Combine) 전략"](https://statkclee.github.io/parallel-r/r-parallel-split-apply-combine.html)


# Parsons Problems {#sac-parsons-problem}

<div class = "row">
  <div class = "col-md-4">
**Instruction**

- Use `iris_tbl` dataframe
- Group by `Species`, and then `nest()`
- Calculate `Sepal.Length` mean through `mutate()`, `map()` pattern

  </div>
  <div class = "col-md-8">
**Write Code**

```{r gapminder-sac, eval=FALSE}
iris_tbl %>% 
  ____(Species) %>% 
  nest() %>% 
  ____(mean_sepal_length = map____(data, ~mean(.$`_____`)))
```

  </div>
</div>