helgasoft

#'-----------  echarty coding helper ------------
#' Please report bugs and ask questions in https://github.com/helgasoft/echarty/issues
#' run in RStudio with command demo(coder), or demo(coder, package='echarty')

stopifnot('session non-interactive'= interactive())

  library(shiny)
  library(shinyjs)
  library(shinybusy)
  library(dplyr)

helgasoft

set.seed(22)
data <- tibble(x = 2 * pi * (1:100)/100) |>
    mutate(
        y1_bar = 0.5 * (sin(x)) + 1,
        y2_bar = 0.5 * (sin(x)) - 1,
        deltaY1 = rnorm(100, sd = 0.1),
        deltaY2 = rnorm(100, sd = 0.1),
        y1 = y1_bar + deltaY1,
        y2 = y2_bar + deltaY2,
        sizeY1 = abs(deltaY1),

helgasoft

#'  This gist is for echarty announcements and notes
#'  Comments are temporary, periodically deleted.
#'  If you like echarty, please consider granting a Github star ⭐.

library(echarty)
data.frame(x = seq.int(1, 5, 1),
           y = seq.int(1, 10, 2)) |>
ec.init(
  series.param= list(
     type='line', symbolSize= 10,

helgasoft

library(echarty)
url <- 'https://test.softpower.tw/echartstw/example/svg/football.svg'
svg <- url |> readLines(encoding='UTF-8') |> paste0(collapse="")
p <- ec.init(preset=FALSE,
	backgroundColor= '#228b22',
	geo= list("map"="football"),
	title= list(text ='2014 World Cup', textStyle=list( color='#FFFF00'),
					subtext='original JS code', subtextStyle= list(color='#FFFF00'),
					sublink='https://test.softpower.tw/echartstw/example/map19.html'
	),

helgasoft

# Expanding on the great code from Milos Popovic
#   new display as layer on Leaflet map, instead of ggplot

# original: https://github.com/milos-agathon/mapping-raster-files-with-terra-in-r
################################################################################
#                 Mapping OSM and satelitte data with terra in R
#                 Milos Popovic
#                 2022/09/22
################################################################################

helgasoft

library(ggplot2)  # for diamonds data =56K recs
library(dplyr)
data <- diamonds |> select(price, carat, cut) |> group_by(cut)

library(echarty)
p <- data |> 
	ec.init(   # series & dataset are preset here
		dataZoom= list(type='inside'),
		tooltip= list(show= TRUE))    
p$x$opts$series <- lapply(p$x$opts$series, function(s) {

helgasoft

#' inspired by https://juliasilge.com/blog/giant-pumpkins/
#' advantage over ggplot is interactivity - zoom brush and tooltips
#' horizontal layout code at https://helgasoft.github.io/echarty/gallery.html#boxplot

library(tidyverse)
pumpkins_raw <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-19/pumpkins.csv")
pumpkins <-
	pumpkins_raw |>
	separate(id, into = c("year", "type")) |>
	mutate(across(c(year, weight_lbs), parse_number)) |> 

helgasoft

library(dplyr)
library(lubridate)
# add event for clicking on blank area
jscode <- c('','',"chart.getZr().on('click', function(e) {
  if (!e.target) alert('blank area: '+JSON.stringify(e.event)); });")
set.seed(421)
df1 <- tibble(date= as_date("2021-11-01") + months(0:11),
              x= 12*10^6 + rnorm(12,0,2000000), 
              y= 12*10^6 + rnorm(12,0,1000000))

helgasoft

library(echarty)
radial_gradient <- htmlwidgets::JS(
  "new echarts.graphic.RadialGradient(0.3, 0.3, 0.8)"
  #  ,[{ offset: 0, color: 'red'}, { offset: 1, color: 'red'}])"
)
color_stops <- htmlwidgets::JS(
  "[{ offset: 0, color:'gold' }, // color at 0% 
    { offset: 0.5, color:'MediumSlateBlue' }, // color at 50%
    { offset: 1, color:'skyblue' // color at 100%
  }]"

helgasoft

# (elegant) ggplot2 code to replicate in echarty
library(ggplot2)
iris |> 
  ggplot(aes(x = Species, y = Petal.Length)) + 
  geom_jitter(width = 0.1)

library(echarty); library(dplyr)

# with categorical axis