Live code that goes with Data Rectangling talk

2018-02_game-of-thrones-rectangling.R

#' ---
#' output:
#'   word_document
#' ---

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

#+
library(repurrrsive)
library(tidyverse)

## Hello got_chars!
?got_chars

## ick
str(got_chars)

## enjoy the Object Explorer
#View(got_chars)
got_chars[[9]][["name"]]
got_chars[[9]][["titles"]]

## back to slides!

## use special shortcuts for extracting elements
## map(YOUR_LIST, "TEXT")
## map(YOUR_LIST, i)
map(got_chars, "name")
map(got_chars, 3)

## how would you do with lapply? inline an anonymous function
lapply(got_chars, function(x) x[["name"]])
lapply(got_chars, `[[`, 3)

## use map()'s special friends for specifying type
## map_chr(YOUR_LIST, .f, ...)
## map_dbl(YOUR_LIST, .f, ...)
## map_int(YOUR_LIST, .f, ...)
## map_chr(YOUR_LIST, .f, ...)
map_chr(got_chars, "name")
map_lgl(got_chars, "alive")

## how would you do with vapply? inline an anonymous function & provide a mold
vapply(got_chars, function(x) x[["name"]], FUN.VALUE = character(1))

## back to slides!

library(glue)

## use toy example you control
glue_data(
  list(name = "jenny", born = "in Atlanta"),
  "{name} was born {born}."
)

## specific examples in your data
glue_data(got_chars[[2]], "{name} was born {born}.")
glue_data(got_chars[[9]], "{name} was born {born}.")

## drop YOUR PROVEN-TO-WORK code into map()
map(got_chars, ~ glue_data(.x, "{name} was born {born}."))
## shows the "formula syntax" for .f

## exploit type-specific, simplifying map
map_chr(got_chars, ~ glue_data(.x, "{name} was born {born}."))
## shows the "formula syntax" for .f

## back to slides!

## put a list into a tibble
got <- tibble(
  name = map_chr(got_chars, "name"),
  stuff = got_chars
)
got

## drop YOUR WORKING map() code into mutate()
got %>%
  mutate(b = map_chr(stuff, ~ glue_data(.x, "{name} was born {born}."))) %>%
  select(-stuff)

## use your usual data manipulation tools, even in presence of a list
got %>%
  mutate(
    alive = map_lgl(stuff, "alive"),
    titles = map(stuff, "titles"),
    n_titles = map_int(titles, length)
  ) %>%
  # count(n_titles) %>%
  filter(n_titles > 4, alive) %>%
  select(-stuff) %>%
  unnest()