Code gists to the rescue

[helgasoft]

Several programming questions have been addressed already in our Code Gists.
Gists are searchable by keyword. Example: to search for scatter in all gists - do this.

Here is the complete list of sample code locations:

• official examples
• Shiny code is in eshiny.R, run with demo(eshiny)
• website gallery and tutorials
• demos on RPubs
• searchable gists
• answers to Github issues
• command examples in Help, like ?ec.init
• code in Github tests

[helgasoft]

endLabel formatting, inquiry by @Tom-Jenkins

tmp <- read.csv('https://github.com/JohnCoene/echarts4r/files/9375371/life_continent.txt', sep='\t')
tmp |> mutate(life_expectancy= round(life_expectancy,2), year= as.factor(year)) |>
	select(year, life_expectancy, country) |>   # order x,y,grp - will be redundant in echarty 1.4.7
	group_by(country) |> 
ec.init(
	ctype='line', 
	yAxis= list(scale= TRUE), 
	grid= list(right= '28%'),
	animation= list(duration= 5000)) |> 
ec.upd({
	series <- lapply(series, function(ss) {
		ss$showSymbol <- FALSE
		ss$emphasis <- list(disable=FALSE, lineStyle= list(width= 3), focus= "self")
		ss$endLabel <- list(show= TRUE, valueAnimation= TRUE, color= 'inherit',
				offset= c(-50,11), 
				formatter= ec.clmn('%@ %L@',3,2))
	ss })
})

if you like this solution, please consider granting a Github star ⭐ to echarty.

[helgasoft]

Layout with two pie charts, inquiry by @DavZim.

library(dplyr)
df <- tibble(
	name = rep(c("v1", "v2", "v3"), 2), # pie native 'axes' are name/value
	value = c(20, 40, 40, 50, 30, 20),
	group = rep(c("A", "B"), each = 3)  # echarty currently requires group column to be last
)

library(echarty)   # v.1.4.7.01+
p <- lapply(df |> group_by(group) |> group_split(), function(x) { 
	ec.init(preset= FALSE, height= 250,
			  title= list(text= unique(x$group)), 
			  series= list(list(type= 'pie', data= ec.data(x, 'names'))),
			  legend= list(show=TRUE))
})
ec.util(p, cmd='layout', cols=2)

# try also new tabset command
htmltools::browsable(
	ec.util(cmd='tabset', tab1= p[[1]], tab2= p[[2]])
)

df2 <- tibble(
	group = rep(c("A", "B"), each = 100),
	time = rep(1:100, 2)
) %>% mutate(value = time + rnorm(100) + ifelse(group == "A", 10, 0)) %>% 
	select(time,value,group)

library(echarty)
lapply(df2 |> group_by(group) |> group_split(), function(gd) { 
	tmp <- lm(value ~ time, gd)
	ec.init( 
		height= 300, title= list(text= unique(gd$group)), 
		xAxis= list(show=TRUE), yAxis=list(show=TRUE),
		series= list(
			list(type= 'scatter', data= ec.data(gd[,1:2]), symbolSize=3, name='data'),
			list(type='line', data= ec.data(data.frame(gd$time, tmp$fitted.values)),
				  showSymbol= FALSE, lineStyle= list(width=1, color='red'), name='lm')
		),
		legend= list(show=TRUE))
}) |>
ec.util(cmd='layout', cols= 2)

if you like this solution, please consider granting a Github star ⭐ to echarty.

[helgasoft]

Answer question by @kesselhwvan about duplicating a ggplot chart

#  set.seed(?)
data = tibble(time = factor(sort(rep(c(4,8,24), 30)), levels = c(4,8,24)),
	dose = factor(rep(c(1,2,3), 30), levels = c(1,2,3)),
	id = rep(sort(rep(LETTERS[1:10], 3)),3),
	y = rnorm(n = 90, mean = 5, sd = 3))

# This is the plot i am aiming to recreate:
# ggplot(data = data, mapping = aes(x = time, y = y, group = id)) + geom_point() +
#      geom_line() + facet_wrap(~dose)

remotes::install_github('helgasoft/echarty')      # get latest 1.4.7.02
library(echarty)

df <- data |> select(time,y,id,dose)	# default order for X,Y,group,etc 
lapply(unique(df$dose), function(i) 
	df |> filter(dose==i) |> group_by(id) |> 
	ec.init(ctype='line', title= list(text= i), legend= list(show=FALSE)
               ,yAxis= list(min= floor(min(df$y)), max= ceiling(max(df$y)))
               ,grid= list(containLabel= TRUE)) 
) |>
ec.util(cmd='layout', cols=3)

if you like this solution, please consider granting a Github star ⭐ to echarty.

[helgasoft]

@XiangyunHuang, hope that's what you are looking for.
Parameter visualMap.dimension works with ECharts dataset, which is set by default in echarty.

library(echarty)
# enhance symbol size related to magnitude
quakes |> mutate(mage= ifelse(mag<5, 4, ifelse(mag<6, 10, 15))) |> 
ec.init(load='3D',
  xAxis3D= list(name= "Lat", scale=TRUE),
  yAxis3D= list(name = "Long", scale=TRUE),
  zAxis3D= list(name = "Depth"),
  series= list(list(type= 'scatter3D',
    encode= list(x= 'lat', y= 'long', z= 'depth'),
    symbolSize = ec.clmn(6),  # = mage
    name = "Fiji"
  )),
  visualMap= list(
    type = "continuous",
    # inRange = list(color = c('#4B0055', '#009B95', '#FDE333')),
    inRange = list(color = c('green', 'yellow', 'red')),
    dimension = 3, # third dimension x = 0, y = 1, z = 2, mag = 3, station = 4
    max= max(quakes$mag), min= min(quakes$mag), 
    text= c(paste('mag\n',max(quakes$mag)), min(quakes$mag)),
    top = 20, calculable= TRUE, precision= 1,
    textStyle= list(color= '#bbb')
  )
) |> ec.theme('dark-mushroom')

[helgasoft]

Numbers locale-based formatting, asked by @oobd
Done with ec.clmn() in echarty. Format %L@ stands for 'localized'.

library(dplyr); library(echarty)
iris %>%
  mutate(Petal.Length = Petal.Length * 1000) %>% 
  group_by(Species) %>% 
  summarise(Petal.Length = sum(Petal.Length)) %>% 
  ec.init(
    grid= list(containLabel= TRUE),
    series= list(list( 
      type= 'bar',
      encode= list(x= 'Petal.Length', y= 'Species'),
      label= list(show= TRUE, position= 'inside', fontSize= 10, offset= c(0, 0.5),
                  formatter= ec.clmn('%L@', 'Petal.Length'))
    ))
  )

[helgasoft]

Simple heatmap of weekly hours, inquiry by @avenn98

set.seed(2022)
df <- data.frame(day_of_week= sample(c('Mon','Tue','Wed','Thu','Fri','Sat','Sun'),100, replace=TRUE), 
		hour_of_day= sample(5:23, 100, replace=TRUE), 
		jobs= sample(1:100, 100)) |> 
	dplyr::distinct(hour_of_day, day_of_week, .keep_all= TRUE)
df |> echarty::ec.init(
	title= list(text= "Heatmap of Successful API Requests by Day and Hour"),
	series= list(list(type= 'heatmap')),
	xAxis= list(type= 'category', data= c('Mon','Tue','Wed','Thu','Fri','Sat','Sun')),
	yAxis= list(min= min(df$hour_of_day)-1, name= 'hours', interval= 1),
	visualMap= list(calculable= TRUE, max= 100, 
		orient= 'horizontal', bottom= 'bottom', left='middle', align= 'right', 
		inRange= list(color= c('yellow','red'))),
	tooltip= list(trigger= "item")
)

if you like this solution, please consider granting a Github star ⭐ to echarty.

[avenn98]

That worked perfectly, thank you so much!

[helgasoft]

Stacked bar chart with factor categories + colors, sample by @mmfc

library(dplyr)
ccv <- "project_id	sum	count	status
100	35	20	Accepted
100	35	14	Proposal
100	35	1	QA
107	33	8	Accepted
107	33	9	In Progress
107	33	12	Proposal
107	33	4	QA
83	20	10	Accepted
83	20	3	In Progress
83	20	7	Proposal
11	18	6	Accepted
11	18	3	In Progress
11	18	5	Proposal
11	18	4	QA
91	17	11	Accepted
91	17	4	In Progress
91	17	2	Proposal
8	15	7	Accepted
8	15	3	Аbandoned
8	15	1	Blocked"
df <- read.csv(text=ccv, sep='\t', header=T) 
df$status <- factor(df$status, levels= c('Accepted','Proposal','QA','In Progress','Blocked','Аbandoned','Reference'))

library(echarty)
df |> mutate(project_id= as.character(project_id)) |> arrange(desc(sum)) |> 
  relocate(sum, .after=last_col()) |> group_by(status) |> 
ec.init(ctype= 'bar', 
  series.param= list(stack= "stak"), tooltip= list(show=T),
  xAxis= list(type= 'category', 
              data= unique(unname(unlist(df$project_id)))),
  # 'status' colors in same (factor) order
  color= c("#FEF2C0","#BFE5BF","#D4C5F9","#a10b0b","#000000","#7F8C8D","green")
) |> ec.theme('dark-mushroom')

[helgasoft]

A solution for boxplot + data chart using helper function echarty::ec.data(format='boxplot',...)
for @lhabegger and @varunrd

library(echarty)      # use v.1.5.1+
# prepare data
ds <- iris |> dplyr::relocate(Species) |>
	ec.data(format= 'boxplot', jitter= 0.1, layout= 'v', 
	        symbolSize= 6, tooltip= list(formatter= ec.clmn('%@',2)))
# display
ec.init(
  dataset= ds$dataset, series= ds$series,xAxis= ds$xAxis, yAxis= ds$yAxis,
  legend= list(show= T), tooltip= list(show= T)
)

if you like this solution, please consider granting a Github star ⭐ to echarty.

[helgasoft]

Make data.tree acme dataset show as sunburst chart, for @GillesSanMartin.
See also various hierarchical structures for echarty explained.

library(data.tree)
tmp <- acme
tmp$Do(function(x) x$value <- x$cost)  # add 'value' for sunburst
lst <- tmp |> ToListExplicit(unname = TRUE)

library(echarty)
p1 <- ec.init(
	series= list(list(
           # data=list(lst) is ok too, but children are not colored
           #type= 'treemap', data= lst$children) 
            type= 'sunburst', data= lst$children, 
               radius= list(0, '90%'), label= list(rotate='radial')))
)
p2 <- ec.init(width=300, series= list(list(type= 'tree', data= list(lst))))
ec.util(list(p1,p2), cmd='layout', cols=2)

[helgasoft]

New in version 1.6.0 - save complete chart to file, show local or remote charts.
@rdatasculptor will like it

remotes::install_github('helgasoft/echarty')    # get latest v.1.6.0
library(echarty)

# build a chart with custom Javascript handlers
js = "chart.getZr().on('contextmenu', x => {chart.setOption({title:{text:'right-clicked'}}) })"
p <- cars |> ec.init(js= js, dataZoom= list(type='inside'))
p$x$on = list(
    list(event= 'datazoom', 
         handler= ec.clmn("function() {this.setOption({title:{text:'zoomed'}});}") )
)

# save chart to local file
p |> ec.inspect(target='full', file='c:/temp/tcar.txt')
# file could now be uploaded to net

# read local file and show chart
con <- file('c:/temp/tcar.txt','rb')
ec.fromJson(con); close(con)

# show a remote chart
echarty::ec.fromJson('https://helgasoft.github.io/echarty/test/pfull.json')

[helgasoft]

@pizifan, an complex example indeed from ECharts.
But echarty can handle it. Currently tl.series supports a single serie, but more can be added thru ec.upd().
Notice that list attributes are straight from ECharts API.

remotes::install_github('helgasoft/echarty')  # get latest!
library(echarty)

tmp <- as.data.frame(jsonlite::fromJSON('https://echarts.apache.org/examples/data/asset/data/life-expectancy-table.json'))
colnames(tmp) <- tmp[1,]
df <- tmp[-1,]
df <- df |> mutate(across(.cols= -c(Country), as.numeric)) |> 
  filter(Year %in% c(1985,2000,2015), Country %in% c('Canada','France','Turkey'))
clr <- ec.clmn("(p) => { return p.dataIndex==0 ? 'violet' : p.dataIndex==1 ? 'orange' : 'brown' }")

df |> group_by(Year) |> 
ec.init(
  yAxis= list(list(name='income', max=45000), 
              list(name='life', max=85)),
  tl.series= list(type='bar', name='income',
                  encode= list(x='Country', y='Income')),
  tooltip= list(show=T), 
  legend= list(data= c('income','life','pop.'))
) |> 
ec.upd({
  options <- lapply(options, \(oo) {
    dix <- oo$series[[1]]$datasetIndex  # from tl.series (bar)
    oo$series <- append(oo$series, 
      list(
        list(type='bar', name='life', yAxisIndex=1, 
             datasetIndex= dix,
             encode= list(x='Country', y='Life Expectancy')),
        list(type='pie', name='pop.', itemStyle= list(color=clr),
             datasetIndex= dix,
             encode= list(value='Population', itemName='Country'), 
             center= c('75%', '25%'), radius= '20%', 
             label= list(show=T), labelLine= list(length=5, length2=0))
    ))
    oo
  })
})