baptiste

# library(cubs)
library(Ryacas)

Ylm <- function(l,m,phi,theta){
  
  ex <- Ryacas::yac_expr(glue::glue("(-1)^{m}*(1-x^2)^({m}/2)*D(x,{m})OrthoP({l}, x)"))
  
  sqrt((2*l+1)/(4*pi) * factorial(l-m) / factorial(l+m))*
    exp(1i*abs(m)*phi) * eval(ex, list(x = cos(theta)))
  

baptiste

---
title: "A graphics workflow"
author: "baptiste"
date: 2018-20-05
---

Glyphs are positioned by x and y coordinates; other parameters are kept separate as they may differ from glyph to glyph and, crucially, compound glyphs contain nested glyphs with their own properties, requiring a flexible container.

The structure of a glyph is thus as follows,

baptiste

grab_math_strings <- function(fe){
  
  parse <- readLines(glue("{fe}-math.tex"))
  content <- parse[parse != ''] 
  lines <- grep("math start", content)
  
  start <- lines+1
  end <- c(lines[-1], length(content) + 1) - 1
  

baptiste

library(minixml)
# devtools::install_github("coolbutuseless/minixml")
library(anytime)

generate_empty_feed <- function(file = 'index.xml'){
  doc <- xml_elem("rss", version="2.0")
  channel <- doc$add('channel')
  channel$add('title', "Photography")
  channel$add('link', "https://photo.bapt.xyz/")
  channel$add('description', "My latest photo pages")

baptiste

```{r}
path <- fs::path("<<variable>>", ext = "csv")
cmd <- paste0("{{< downloadthis ", "'", path, "'", ' dname=<<variable>> label="Download the <<variable>> data" icon=database-fill-down type=info class=data-button id=<<variable>> >}}')
```


```{r}
#| results: asis
cat(cmd)

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---
title: "file 1"
format: html
---

## Code Externalisation

Write R code in external R scripts, and use `read_chunk()` to read them into the current document.

```{r cache=FALSE}

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# not needed
# library(ggplot2)

# disclaimer: this code is from 15 years ago...
# (kind of impressed it still runs!)

# example data
x <- seq(0, 10, len = 100)
y1 <- jitter(sin(x), 1000)

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library(ggplot2)
library(tagger)

d <- data.frame(x=rep(1:10, 5*4*2), y=rnorm(10*5*4*2), f1=gl(4,10*5*2,labels=letters[1:4]), f2=gl(5,10*4*2,labels = LETTERS[1:5]), f3=gl(2,10*4*5))

p <- ggplot(d, aes(x,y)) +
  facet_wrap(f1~f2+f3)

data <- ggplot_build(p)
str(data$layout$layout)

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library(ggplot2)
library(readr)
library(ggfittext)
d <- read_csv('PrevalenceAtLarge.csv')

d$value <- cut(d$PercentAtLarge, breaks = 5,
               labels = c("doubling under 6 weeks",
                          "doubling in 2–6 weeks",
                          "stable",
                          "halving in 2–6 weeks",

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<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE language SYSTEM "language.dtd">

<language name="Julia" section="Scientific"
          version="0"
          extensions="*.jl"
          mimetype="text/x-juliascript"
          author="Andreas Nordal ([email protected])"
          license="public domain">
  <highlighting>