Code gists to the rescue #3
Comments
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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$endLabel <- list(show= TRUE, valueAnimation= TRUE, color= 'inherit',
offset= c(-50,11),
formatter= ec.clmn('%@ %L@',3,2))
ss })
})
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Layout with two pie charts, inquiry by @DavZim. library(dplyr)
df <- tibble(
name = rep(c("v1", "v2", "v3"), 2), # pie native 'axes' are name/value
value = c(20, 40, 40, 50, 30, 20),
group = rep(c("A", "B"), each = 3) # echarty currently requires group column to be last
)
library(echarty) # v.1.4.7.01+
p <- lapply(df |> group_by(group) |> group_split(), function(x) {
ec.init(preset= FALSE, height= 250,
title= list(text= unique(x$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$fitted.values)),
showSymbol= FALSE, lineStyle= list(width=1, color='red'), name='lm')
),
legend= list(show=TRUE))
}) |>
ec.util(cmd='layout', cols= 2)
if you like this solution, please consider granting a Github star ⭐ to echarty. |
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Answer question by @kesselhwvan about duplicating a ggplot chart # set.seed(?)
data = tibble(time = factor(sort(rep(c(4,8,24), 30)), levels = c(4,8,24)),
dose = factor(rep(c(1,2,3), 30), levels = c(1,2,3)),
id = rep(sort(rep(LETTERS[1:10], 3)),3),
y = rnorm(n = 90, mean = 5, sd = 3))
# This is the plot i am aiming to recreate:
# ggplot(data = data, mapping = aes(x = time, y = y, group = id)) + geom_point() +
# geom_line() + facet_wrap(~dose)
remotes::install_github('helgasoft/echarty') # get latest 1.4.7.02
library(echarty)
df <- data |> select(time,y,id,dose) # default order for X,Y,group,etc
lapply(unique(df$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)
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@XiangyunHuang, hope that's what you are looking for. 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')
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Numbers locale-based formatting, asked by @oobd 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'))
))
)
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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$hour_of_day)-1, name= 'hours', interval= 1),
visualMap= list(calculable= TRUE, max= 100,
orient= 'horizontal', bottom= 'bottom', left='middle', align= 'right',
inRange= list(color= c('yellow','red'))),
tooltip= list(trigger= "item")
)
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That worked perfectly, thank you so much! |
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Stacked bar chart with factor categories + colors, sample by @mmfc library(dplyr)
ccv <- "project_id sum count status
100 35 20 Accepted
100 35 14 Proposal
100 35 1 QA
107 33 8 Accepted
107 33 9 In Progress
107 33 12 Proposal
107 33 4 QA
83 20 10 Accepted
83 20 3 In Progress
83 20 7 Proposal
11 18 6 Accepted
11 18 3 In Progress
11 18 5 Proposal
11 18 4 QA
91 17 11 Accepted
91 17 4 In Progress
91 17 2 Proposal
8 15 7 Accepted
8 15 3 Аbandoned
8 15 1 Blocked"
df <- read.csv(text=ccv, sep='\t', header=T)
df$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')
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A solution for boxplot + data chart using helper function echarty::ec.data(format='boxplot',...) 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)
)
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Make data.tree acme dataset show as sunburst chart, for @GillesSanMartin. 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)
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New in version 1.6.0 - save complete chart to file, show local or remote charts. |
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@pizifan, an complex example indeed from ECharts.
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Closed
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some less used chart types, translated to echarty from official source, for @msgoussi waterfall
bar race
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New extras function 💲 to build a two-level axis, with responsive auto-resize. |
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