R语言Shiny App和交互式绘图echarts4r包Advanced深探

更新

library(shiny)
ui <- fluidPage(fluidRow(column(12, actionButton("update", "Update"))),fluidRow(column(12, echarts4rOutput("plot")))
)server <- function(input, output){data <- eventReactive(input$update, {Sys.sleep(1) # sleep one second to show loadingdata.frame(x = 1:10,y = rnorm(10))})output$plot <- renderEcharts4r({data() |> e_charts(x) |> e_bar(y) |> e_show_loading()})
}shinyApp(ui, server)

基本应用

library(shiny)
library(echarts4r)ui <- fluidPage(actionButton("add", "Add Data to y"),echarts4rOutput("plot"),verbatimTextOutput("selected")
)server <- function(input, output, session){data <- data.frame(x = rnorm(10, 5, 3), y = rnorm(10, 50, 12), z = rnorm(10, 50, 5))react <- eventReactive(input$add, {set.seed(sample(1:1000, 1))data.frame(x = rnorm(10, 5, 2), y = rnorm(10, 50, 10))})output$plot <- renderEcharts4r({data |> e_charts(x) |> e_scatter(y) |>e_scatter(z) |> e_brush(throttleDelay = 1000)})observeEvent(input$add, {echarts4rProxy("plot") |> e_append1_p(0, react(), x, y)})output$selected <- renderPrint({input$plot_brush})}shinyApp(ui, server)

Shiny App

library(shiny)
library(echarts4r)ui <- fluidPage(fluidRow(column(3, echarts4rBoxOutput("box1")),column(3, echarts4rBoxOutput("box2")),column(3, echarts4rBoxOutput("box3")),column(3, echarts4rBoxOutput("box4")))
)server <- function(input, output){output$box1 <- renderEcharts4rBox({echarts4rBox(cars, speed, dist, "Cake", type = "bar")})output$box2 <- renderEcharts4rBox({echarts4rBox(cars, speed, dist, "Pears", type = "area")})output$box3 <- renderEcharts4rBox({echarts4rBox(cars, speed, dist, "More Cakes!", type = "step")})output$box4 <- renderEcharts4rBox({echarts4rBox(cars, dist, speed, "Misc", type = "scatter", title_args = list(left = "right"))})
}shinyApp(ui, server)

分类

时间线

library(echarts4r)iris |> group_by(Species) |> e_charts(Sepal.Length) |> e_line(Sepal.Width) |> e_title("Grouped data")

library(echarts4r)iris |> group_by(Species) |> e_charts(Sepal.Length, timeline = TRUE) |> e_line(Sepal.Width) |> e_title("Timeline")

存储栈

library(echarts4r)df <- data.frame(x = LETTERS[1:10],a = runif(10),b = runif(10),c = runif(10),d = runif(10)
)df |> e_charts(x) |> e_bar(a, stack = "grp") |> e_bar(b, stack = "grp") |> e_bar(c, stack = "grp2") |> e_bar(d, stack = "grp2")

散点图

library(echarts4r)iris |> group_by(Species) |> e_charts(Sepal.Length) |> e_scatter(Petal.Length, Sepal.Width)

library(echarts4r)my_scale <- function(x){scales::rescale(x, to = c(5, 30))
}iris |> group_by(Species) |> e_charts(Sepal.Length) |> e_scatter(Petal.Length, Sepal.Width, scale = my_scale)

library(echarts4r)iris |>  group_by(Species) |> e_charts(Sepal.Length) |> e_scatter(Petal.Length, Sepal.Width, scale = \(x) scales::rescale(x, to = c(5, 30)))

library(echarts4r)iris |> group_by(Species) |> e_charts(Sepal.Length) |> e_scatter(Petal.Length, symbol_size = 15)

library(echarts4r)
iris |> group_by(Species) |> e_charts(Sepal.Length) |> e_scatter(Petal.Length, Sepal.Width, scale = log1p, symbol_size = 10)、

library(echarts4r)
echart <- mtcars |> e_charts(mpg) |> e_scatter(qsec, wt, scale = e_scale) |> e_legend(show = FALSE)echart |> e_visual_map(wt, scale = e_scale)

library(echarts4r)
mtcars |> e_charts(mpg) |> e_scatter(qsec, wt, scale = NULL, scale_js = "function(data){ return data[3] * 3;}") |> e_legend(show = FALSE) |> e_visual_map(wt, scale = NULL)

library(echarts4r)
mtcars |> e_charts(cyl) |> e_scatter(drat, symbol_size = 4) |> e_scatter(drat, jitter_factor = 2, symbol_size = 6,name = "noisy")

热点图

library(echarts4r)
v <- LETTERS[1:10]
matrix <- data.frame(x = sample(v, 300, replace = TRUE), y = sample(v, 300, replace = TRUE), z = rnorm(300, 10, 1),stringsAsFactors = FALSE
) |> dplyr::group_by(x, y) |> dplyr::summarise(z = sum(z)) |> dplyr::ungroup()
#> `summarise()` has grouped output by 'x'. You can override using the `.groups`
#> argument.matrix |> e_charts(x) |> e_heatmap(y, z) |> e_visual_map(z)

日历图

library(echarts4r)
dates <- seq.Date(as.Date("2018-01-01"), as.Date("2018-12-31"), by = "day")
values <- rnorm(length(dates), 20, 6)year <- data.frame(date = dates, values = values)year |> e_charts(date) |> e_calendar(range = "2018") |> e_heatmap(values, coord_system = "calendar") |> e_visual_map(max = 30)

library(echarts4r)
df <- data.frame(x = 1:10, y = seq(1, 20, by = 2))df |> e_charts(x) |> e_line(y)

library(echarts4r)
df |> e_charts(x) |> e_polar() |> e_angle_axis() |> e_radius_axis() |> e_line(y, coord_system = "polar", smooth = TRUE)

定制化坐标轴

旋转坐标轴

library(echarts4r)
USArrests |> e_charts(Assault) |> e_line(Murder, smooth = TRUE) |> e_line(Rape, y_index = 1) |>  # add secondary axise_y_axis(splitLine = list(show = FALSE)) # hide split lines on first Y axis

library(echarts4r)
data.frame(x = LETTERS[1:5],y = runif(5, 1, 15)
) |> e_charts(x) |> e_bar(y, name = "flipped") |> e_flip_coords() # flip axis

library(echarts4r)
USArrests |> tibble::rownames_to_column("State") |> dplyr::mutate(Rape = -Rape) |> e_charts(State) |> e_area(Murder) |>e_bar(Rape, name = "Sick basterd", x_index = 1) |> # second y axis e_mark_line("Sick basterd", data = list(type = "average")) |> e_mark_point("Murder", data = list(type = "min"))

寻找参数

library(echarts4r)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#>     filter, lag
#> The following objects are masked from 'package:base':
#>
#>     intersect, setdiff, setequal, union
df <- tibble(name = "earth",        # 1st levelchildren = list(tibble(name = c("land", "ocean"),             # 2nd levelchildren = list(tibble(name = c("forest", "river")),   # 3rd level tibble(name = c("fish", "kelp"),children = list(tibble(name = c("shark", "tuna"),  # 4th level NULL  # kelp))))))
)df |> e_charts() |> e_tree()

转化成半径

library(echarts4r)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#>     filter, lag
#> The following objects are masked from 'package:base':
#>
#>     intersect, setdiff, setequal, union
df <- tibble(name = "earth",        # 1st levelchildren = list(tibble(name = c("land", "ocean"),             # 2nd levelchildren = list(tibble(name = c("forest", "river")),   # 3rd level tibble(name = c("fish", "kelp"),children = list(tibble(name = c("shark", "tuna"),  # 4th level NULL  # kelp))))))
)df |> e_charts() |> e_tree(layout = "radial")

显示标签

library(echarts4r)
library(dplyr)
USArrests |> tibble::rownames_to_column("State") |> dplyr::slice(1:10) |> e_charts(State) |> e_area(Murder, label = list(normal = list(show = TRUE)))

嵌套数据

library(echarts4r)
# add columns to iris
iris_dat <- iris |> dplyr::mutate(show = TRUE, # to show the labelsfontSize = exp(Sepal.Length) / 10, # font size will correspond to Sepal.Lengthcolor = sample(c("red", "black", "blue"), dplyr::n(), replace = TRUE) # assign a random color to the label)iris_dat |> dplyr::slice(1:10) |> # simplify the graph. e_charts(Sepal.Width) |> e_line(Sepal.Length) |> e_add_nested("label", show, fontSize, color) |> # add our columns to "label"e_x_axis(min = 2.5)

烟囱

集合List

library(echarts4r)
funnel <- data.frame(stage = c("View", "Click", "Purchase"), value = c(80, 30, 20),color = c("blue", "red", "green")
)funnel |> e_charts() |> e_funnel(value, stage) |> e_add_nested("itemStyle", color)

参考资料：
https://echarts4r.john-coene.com/articles/advanced.html

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