Matt Dray matt-dray

## gitalias.txt
alias.hist=log --decorate --oneline --graph --all

## allequal-tibble-dataframe.R
# all.equal() behaves differently when presented with objects of class
# data.frame versus those with tibble class. This is important for
# trying to rewrite the {tidyr} episode of Software Carpentry's
# R for Reproducible Scientific Analysis lesson to include the new
# pivot_wider() and pivot_longer() columns instead of spread() and
# gather(), which have been superseded.

# Seems to be a noted problem:
# https://github.com/tidyverse/tibble/issues/287

## DATADIR.sh
cd /Users/matthewdray/.bash_profile

## regex-example.py
# Import re for working with regular expressions
import re

# Provide some text to search
# 'Hipster ipsum' via https://hipsum.co/
text = "Lorem ipsum dolor amet selfies williamsburg tattooed ethical wolf cloud bread. Cronut marfa heirloom pour-over. Jean shorts aesthetic before they sold out, yr subway tile kale chips occupy. Banjo lomo af, meditation roof party cronut vape glossier."

# Search for a specific phrase
# Returns a 'match' object
# More info here: https://www.w3schools.com/python/python_regex.asp#matchobject

## officer-scrape-word.R
# Basic use of the {officer} package to scrape a table from a Word doc
# https://davidgohel.github.io/officer/

# I've now written a couple of functions to do this task:
#  https://gist.github.com/matt-dray/d4837f106bcee80ea39235b6465a7cac
# You can read more about those in a blog post:
#   https://www.rostrum.blog/2023/06/07/rectangular-officer/

# There are other solutions. You can also try {docxtractr} by Bon Rudis
# (on CRAN), which doesn't depend on {officer}, or {officerExtras} by Eli

## screenshot-gif-rstudio.R
library(magick)

before <- image_read("~/Desktop/before.png")
before <- image_crop(before, "815x300-80-200")

after <- image_crop(image_read("~/Desktop/after.png"))
after <- image_crop(after, "815x300-80-200")

# To morph
# frames <- image_morph(c(before, after, before), frames = 20)

## tidyr-expand.R
library(dplyr)
library(tidyr)

data <- tibble(
  var1 = c(rep("A", 3), rep("B", 2)),
  var2 = c(letters[1:3], letters[1:2]),
  var3 = runif(5)
)

expand(data, var1, var2) %>%

## coolanduseful.R
# Plot weight by cylinders, with weight by gear inside
# Using {ggpattern} by @coolbutuseless
# https://coolbutuseless.github.io/package/ggpattern/
# Tongue-in-cheek blog post: https://www.rostrum.blog/2020/04/05/yo-dawg/

# Load packages
library(dplyr)
library(purrr)
library(tidyr)
library(ggplot2)

## vars_setnames.R
# Question: how can content of vars() be supplied to set_names() after a map()?
# Also: below, can variables_bare be coerced to variables_quoted, or vice-versa?

# Load packages
suppressPackageStartupMessages(library(dplyr))
library(purrr)

# Quoted and unquoted vectors
variables_bare <- vars(Sepal.Width, Petal.Length)  # to be passed to .x in map()
variables_quoted <- c("Sepal.Width", "Petal.Length")  # as above, quoted version

## small-crosstalk-test.Rmd
---
title: "Crosstalk test"
output:
  html_document:
    code_folding: hide
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(crosstalk)
	# all.equal() behaves differently when presented with objects of class
	# data.frame versus those with tibble class. This is important for
	# trying to rewrite the {tidyr} episode of Software Carpentry's
	# R for Reproducible Scientific Analysis lesson to include the new
	# pivot_wider() and pivot_longer() columns instead of spread() and
	# gather(), which have been superseded.

	# Seems to be a noted problem:
	# https://github.com/tidyverse/tibble/issues/287
	# Import re for working with regular expressions
	import re

	# Provide some text to search
	# 'Hipster ipsum' via https://hipsum.co/
	text = "Lorem ipsum dolor amet selfies williamsburg tattooed ethical wolf cloud bread. Cronut marfa heirloom pour-over. Jean shorts aesthetic before they sold out, yr subway tile kale chips occupy. Banjo lomo af, meditation roof party cronut vape glossier."

	# Search for a specific phrase
	# Returns a 'match' object
	# More info here: https://www.w3schools.com/python/python_regex.asp#matchobject
	# Basic use of the {officer} package to scrape a table from a Word doc
	# https://davidgohel.github.io/officer/

	# I've now written a couple of functions to do this task:
	# https://gist.github.com/matt-dray/d4837f106bcee80ea39235b6465a7cac
	# You can read more about those in a blog post:
	# https://www.rostrum.blog/2023/06/07/rectangular-officer/

	# There are other solutions. You can also try {docxtractr} by Bon Rudis
	# (on CRAN), which doesn't depend on {officer}, or {officerExtras} by Eli
	library(magick)

	before <- image_read("~/Desktop/before.png")
	before <- image_crop(before, "815x300-80-200")

	after <- image_crop(image_read("~/Desktop/after.png"))
	after <- image_crop(after, "815x300-80-200")

	# To morph
	# frames <- image_morph(c(before, after, before), frames = 20)
	library(dplyr)
	library(tidyr)

	data <- tibble(
	var1 = c(rep("A", 3), rep("B", 2)),
	var2 = c(letters[1:3], letters[1:2]),
	var3 = runif(5)
	)

	expand(data, var1, var2) %>%
	# Plot weight by cylinders, with weight by gear inside
	# Using {ggpattern} by @coolbutuseless
	# https://coolbutuseless.github.io/package/ggpattern/
	# Tongue-in-cheek blog post: https://www.rostrum.blog/2020/04/05/yo-dawg/

	# Load packages
	library(dplyr)
	library(purrr)
	library(tidyr)
	library(ggplot2)
	# Question: how can content of vars() be supplied to set_names() after a map()?
	# Also: below, can variables_bare be coerced to variables_quoted, or vice-versa?

	# Load packages
	suppressPackageStartupMessages(library(dplyr))
	library(purrr)

	# Quoted and unquoted vectors
	variables_bare <- vars(Sepal.Width, Petal.Length) # to be passed to .x in map()
	variables_quoted <- c("Sepal.Width", "Petal.Length") # as above, quoted version
	---
	title: "Crosstalk test"
	output:
	html_document:
	code_folding: hide
	---

	```{r setup, include=FALSE}
	knitr::opts_chunk$set(echo = TRUE)
	library(crosstalk)