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25
4
我在我的 shinyapp 中添加了一个响应式(Reactive)仪表。该仪表将显示最近的跳跃高度得分与运动员之前所有时间的最小值和最大值的比较。
selectInput 设置为 Athlete 和最近的日期 (max(jumpdata$Date))。我的代码非常适用于最大响应式(Reactive)仪表，但不会为最小值进行响应式(Reactive)更新。当我运行该应用程序时，第一个运动员输入的最小值会显示，然后在我更新并选择不同的输入时保持相同的值(但最大变化)。
我不确定障碍在哪里，因为最大值正在更新。
用户界面
library(shiny)
library(shinydashboard)
library(flexdashboard)
library(dplyr)
jumpdata <- read.csv("SO CMJ Dummy.csv")
jumpdata$Date <- as.Date(jumpdata$Date, "%Y-%m-%d")
shinyUI(
fluidPage(
sidebarPanel(width = 3,
selectInput("Athlete", label = "Athlete",
choices = unique(jumpdata$Athlete))),
mainPanel(
fluidRow(
box(title = "Jump Height", gaugeOutput("Gauge_JH"))
))
))
服务器.r
library(shiny)
library(shinydashboard)
library(flexdashboard)
library(dplyr)
jumpdata <- read.csv("SO CMJ Dummy.csv")
jumpdata$Date <- as.Date(jumpdata$Date, "%Y-%m-%d")
shinyServer(function(input, output){
output$Gauge_JH <- renderGauge({
f <- jumpdata %>%
select(Date, Athlete, JumpHeight_cm) %>%
filter(Athlete == input$Athlete & Date == c(max(jumpdata$Date)))
t <- jumpdata %>%
select(Date, Athlete, JumpHeight_cm) %>%
filter(Athlete == input$Athlete)
g <- gauge(f$JumpHeight_cm, min = min(t$JumpHeight_cm), max = max(t$JumpHeight_cm), symbol = 'cm', gaugeSectors(
success = c((max(t$JumpHeight_cm)*.9), max(t$JumpHeight_cm)), warning = c((max(t$JumpHeight_cm)*.8), max(t$JumpHeight_cm)*.9), danger = c(min(t$JumpHeight_cm), max(t$JumpHeight_cm)*.8)
))
print(g)
})
})
数据
jumpdata <- structure(list(Athlete = structure(c(1L, 1L, 1L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L,
11L, 11L, 11L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L,
14L, 14L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L), .Label = c("Athlete 1", "Athlete 10",
"Athlete 11", "Athlete 12", "Athlete 13", "Athlete 14", "Athlete 2",
"Athlete 3", "Athlete 4", "Athlete 5", "Athlete 6", "Athlete 7",
"Athlete 8", "Athlete 9"), class = "factor"), Date = structure(c(1L,
4L, 5L, 1L, 3L, 5L, 7L, 2L, 3L, 5L, 7L, 1L, 3L, 5L, 7L, 1L, 3L,
5L, 7L, 1L, 3L, 6L, 7L, 2L, 4L, 5L, 8L, 1L, 3L, 5L, 7L, 1L, 3L,
5L, 7L, 1L, 3L, 5L, 7L, 1L, 3L, 5L, 7L, 1L, 3L, 5L, 7L, 1L, 3L,
6L, 7L, 1L, 3L, 5L, 7L), .Label = c("2020-01-06", "2020-01-07",
"2020-01-13", "2020-01-14", "2020-01-21", "2020-01-23", "2020-01-27",
"2020-01-28"), class = "factor"), Position = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L), .Label = c("DEF", "FWD", "GOALIE"), class = "factor"),
Program = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L,
4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L), .Label = c("Navy", "Red", "RTP", "White"), class = "factor"),
mRSI = c(0.36, 0.38, 0.42, 0.46, 0.46, 0.47, 0.48, 0.31,
0.3, 0.24, 0.3, 0.29, 0.26, 0.28, 0.28, 0.36, 0.35, 0.43,
0.43, 0.28, 0.31, 0.28, 0.3, 0.33, 0.36, 0.35, 0.37, 0.37,
0.36, 0.37, 0.36, 0.3, 0.36, 0.34, 0.37, 0.26, 0.28, 0.34,
0.3, 0.39, 0.4, 0.43, 0.43, 0.43, 0.47, 0.46, 0.48, 0.34,
0.36, 0.33, 0.37, 0.28, 0.28, 0.34, 0.33), SystemWeight = c(617.21,
612.4, 620.45, 672.08, 682.23, 670.5, 663.41, 517.33, 515.23,
511.62, 517.85, 697.55, 703.92, 689.43, 691.33, 859.06, 845.9,
850.97, 851.84, 655.79, 665.09, 673.91, 667.92, 626.78, 632.92,
634.52, 624.88, 637.55, 645.6, 648.78, 646.64, 558.03, 563.23,
569.58, 560.95, 693.63, 695.54, 684.37, 684.58, 641.18, 660.8,
663.95, 660, 594.92, 596.97, 591.36, 585.64, 522.35, 518.17,
530.95, 523.5, 780.65, 789.81, 775.84, 775.48), FTCT = c(0.61,
0.62, 0.67, 0.74, 0.75, 0.77, 0.77, 0.54, 0.55, 0.44, 0.53,
0.53, 0.49, 0.53, 0.56, 0.6, 0.58, 0.68, 0.68, 0.53, 0.57,
0.54, 0.55, 0.61, 0.63, 0.64, 0.65, 0.59, 0.58, 0.59, 0.59,
0.51, 0.59, 0.59, 0.59, 0.53, 0.57, 0.63, 0.59, 0.76, 0.76,
0.79, 0.78, 0.67, 0.72, 0.72, 0.74, 0.63, 0.65, 0.61, 0.63,
0.49, 0.5, 0.53, 0.57), JumpHeight_cm = c(28.97, 29.78, 31.43,
35.83, 35.41, 36.59, 36.92, 27.56, 26.11, 26.15, 26.82, 26.15,
25.08, 24.98, 24.62, 29.39, 30.17, 32.42, 32.56, 26.6, 27.25,
25.58, 27.88, 29.17, 31.58, 28.48, 31.24, 33.73, 32.78, 33.09,
33.43, 29.73, 31.91, 30.65, 32.98, 24.15, 24.24, 27.57, 25.44,
26.68, 26.39, 27.43, 28.87, 35.44, 36.29, 35.71, 36.06, 26.79,
27.76, 26.82, 29.71, 28.69, 26.9, 31.12, 29.77), EJH = c(17.6,
18.58, 21.11, 26.66, 26.69, 28.08, 28.38, 14.99, 14.39, 11.41,
14.33, 13.8, 12.34, 13.29, 13.67, 17.58, 17.5, 22.03, 22.19,
14.03, 15.59, 13.92, 15.39, 17.7, 19.75, 18.37, 20.3, 19.99,
18.9, 19.62, 19.61, 15.09, 18.8, 18.18, 19.6, 12.78, 13.87,
17.28, 15.06, 20.44, 20.12, 21.74, 22.52, 23.8, 26.25, 25.68,
26.73, 16.99, 18.13, 16.42, 18.82, 14.09, 13.43, 16.61, 16.9
), Weight = c(62.94, 62.45, 63.27, 68.54, 69.57, 68.38, 67.65,
52.76, 52.54, 52.17, 52.81, 71.13, 71.78, 70.31, 70.5, 87.61,
86.26, 86.78, 86.87, 66.88, 67.82, 68.72, 68.11, 63.92, 64.54,
64.71, 63.72, 65.02, 65.84, 66.16, 65.94, 56.91, 57.44, 58.09,
57.2, 70.74, 70.93, 69.79, 69.81, 65.39, 67.39, 67.71, 67.31,
60.67, 60.88, 60.31, 59.72, 53.27, 52.84, 54.15, 53.39, 79.61,
80.54, 79.12, 79.08)), class = "data.frame", row.names = c(NA,
-55L))
根据 github 上发布的解决方法，这是我的新代码，但我无法渲染它。根据我原来的标准，我不确定要将什么作为 input$range 包含在内。
用户界面
library(shiny)
library(shinydashboard)
library(flexdashboard)
library(dplyr)
jumpdata <- read.csv("SO CMJ Dummy.csv")
jumpdata$Date <- as.Date(jumpdata$Date, "%Y-%m-%d")
shinyUI(
fluidPage(
sidebarPanel(width = 3,
selectInput("Athlete", label = "Athlete",
choices = unique(jumpdata$Athlete))),
mainPanel(
fluidRow(
box(title = "Jump Height", gaugeOutput("Gauge_JH")),
uiOutput("Gauge_JH_Proxy")
))
))
服务器.r
library(shiny)
library(shinydashboard)
library(flexdashboard)
library(dplyr)
jumpdata <- read.csv("SO CMJ Dummy.csv")
jumpdata$Date <- as.Date(jumpdata$Date, "%Y-%m-%d")
shinyServer(function(input, output){
output$Gauge_JH <- renderGauge({
f <- jumpdata %>%
select(Date, Athlete, JumpHeight_cm) %>%
filter(Athlete == input$Athlete & Date == c(max(jumpdata$Date)))
t <- jumpdata %>%
select(Date, Athlete, JumpHeight_cm) %>%
filter(Athlete == input$Athlete)
g <- gauge(f$JumpHeight_cm, min = min(t$JumpHeight_cm), max = max(t$JumpHeight_cm), symbol = 'cm', gaugeSectors(
success = c((max(t$JumpHeight_cm)*.9), max(t$JumpHeight_cm)), warning = c((max(t$JumpHeight_cm)*.8), max(t$JumpHeight_cm)*.9), danger = c(min(t$JumpHeight_cm), max(t$JumpHeight_cm)*.8)
))
print(g)
})
output$Gauge_JH_Proxy <- renderUI({
input$Athlete # force re-rendering
gaugeOutput(outputId = "Gauge_JH", width = "30%", height = "200px")
})
})
最佳答案
可以通过使用 renderUI 和 debounce(延迟渲染，以便计算准备就绪)来解决此问题。
请注意，我已经更改了范围逻辑以实际显示一些颜色并查看以下内容:
library(shiny)
library(shinydashboard)
library(flexdashboard)
library(dplyr)
jumpdata <- structure(list(Athlete = structure(c(1L, 1L, 1L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L,
11L, 11L, 11L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L,
14L, 14L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L), .Label = c("Athlete 1", "Athlete 10",
"Athlete 11", "Athlete 12", "Athlete 13", "Athlete 14", "Athlete 2",
"Athlete 3", "Athlete 4", "Athlete 5", "Athlete 6", "Athlete 7",
"Athlete 8", "Athlete 9"), class = "factor"), Date = structure(c(1L,
4L, 5L, 1L, 3L, 5L, 7L, 2L, 3L, 5L, 7L, 1L, 3L, 5L, 7L, 1L, 3L,
5L, 7L, 1L, 3L, 6L, 7L, 2L, 4L, 5L, 8L, 1L, 3L, 5L, 7L, 1L, 3L,
5L, 7L, 1L, 3L, 5L, 7L, 1L, 3L, 5L, 7L, 1L, 3L, 5L, 7L, 1L, 3L,
6L, 7L, 1L, 3L, 5L, 7L), .Label = c("2020-01-06", "2020-01-07",
"2020-01-13", "2020-01-14", "2020-01-21", "2020-01-23", "2020-01-27",
"2020-01-28"), class = "factor"), Position = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L), .Label = c("DEF", "FWD", "GOALIE"), class = "factor"),
Program = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L,
4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L), .Label = c("Navy", "Red", "RTP", "White"), class = "factor"),
mRSI = c(0.36, 0.38, 0.42, 0.46, 0.46, 0.47, 0.48, 0.31,
0.3, 0.24, 0.3, 0.29, 0.26, 0.28, 0.28, 0.36, 0.35, 0.43,
0.43, 0.28, 0.31, 0.28, 0.3, 0.33, 0.36, 0.35, 0.37, 0.37,
0.36, 0.37, 0.36, 0.3, 0.36, 0.34, 0.37, 0.26, 0.28, 0.34,
0.3, 0.39, 0.4, 0.43, 0.43, 0.43, 0.47, 0.46, 0.48, 0.34,
0.36, 0.33, 0.37, 0.28, 0.28, 0.34, 0.33), SystemWeight = c(617.21,
612.4, 620.45, 672.08, 682.23, 670.5, 663.41, 517.33, 515.23,
511.62, 517.85, 697.55, 703.92, 689.43, 691.33, 859.06, 845.9,
850.97, 851.84, 655.79, 665.09, 673.91, 667.92, 626.78, 632.92,
634.52, 624.88, 637.55, 645.6, 648.78, 646.64, 558.03, 563.23,
569.58, 560.95, 693.63, 695.54, 684.37, 684.58, 641.18, 660.8,
663.95, 660, 594.92, 596.97, 591.36, 585.64, 522.35, 518.17,
530.95, 523.5, 780.65, 789.81, 775.84, 775.48), FTCT = c(0.61,
0.62, 0.67, 0.74, 0.75, 0.77, 0.77, 0.54, 0.55, 0.44, 0.53,
0.53, 0.49, 0.53, 0.56, 0.6, 0.58, 0.68, 0.68, 0.53, 0.57,
0.54, 0.55, 0.61, 0.63, 0.64, 0.65, 0.59, 0.58, 0.59, 0.59,
0.51, 0.59, 0.59, 0.59, 0.53, 0.57, 0.63, 0.59, 0.76, 0.76,
0.79, 0.78, 0.67, 0.72, 0.72, 0.74, 0.63, 0.65, 0.61, 0.63,
0.49, 0.5, 0.53, 0.57), JumpHeight_cm = c(28.97, 29.78, 31.43,
35.83, 35.41, 36.59, 36.92, 27.56, 26.11, 26.15, 26.82, 26.15,
25.08, 24.98, 24.62, 29.39, 30.17, 32.42, 32.56, 26.6, 27.25,
25.58, 27.88, 29.17, 31.58, 28.48, 31.24, 33.73, 32.78, 33.09,
33.43, 29.73, 31.91, 30.65, 32.98, 24.15, 24.24, 27.57, 25.44,
26.68, 26.39, 27.43, 28.87, 35.44, 36.29, 35.71, 36.06, 26.79,
27.76, 26.82, 29.71, 28.69, 26.9, 31.12, 29.77), EJH = c(17.6,
18.58, 21.11, 26.66, 26.69, 28.08, 28.38, 14.99, 14.39, 11.41,
14.33, 13.8, 12.34, 13.29, 13.67, 17.58, 17.5, 22.03, 22.19,
14.03, 15.59, 13.92, 15.39, 17.7, 19.75, 18.37, 20.3, 19.99,
18.9, 19.62, 19.61, 15.09, 18.8, 18.18, 19.6, 12.78, 13.87,
17.28, 15.06, 20.44, 20.12, 21.74, 22.52, 23.8, 26.25, 25.68,
26.73, 16.99, 18.13, 16.42, 18.82, 14.09, 13.43, 16.61, 16.9
), Weight = c(62.94, 62.45, 63.27, 68.54, 69.57, 68.38, 67.65,
52.76, 52.54, 52.17, 52.81, 71.13, 71.78, 70.31, 70.5, 87.61,
86.26, 86.78, 86.87, 66.88, 67.82, 68.72, 68.11, 63.92, 64.54,
64.71, 63.72, 65.02, 65.84, 66.16, 65.94, 56.91, 57.44, 58.09,
57.2, 70.74, 70.93, 69.79, 69.81, 65.39, 67.39, 67.71, 67.31,
60.67, 60.88, 60.31, 59.72, 53.27, 52.84, 54.15, 53.39, 79.61,
80.54, 79.12, 79.08)), class = "data.frame", row.names = c(NA,
-55L))
jumpdata$Date <- as.Date(jumpdata$Date, "%Y-%m-%d")
ui <- fluidPage(
fluidPage(
sidebarPanel(width = 3,
selectInput("Athlete", label = "Athlete",
choices = unique(jumpdata$Athlete))),
mainPanel(
fluidRow(
box(title = "Jump Height", uiOutput("Gauge_JH_Proxy"))
))
))
server <- function(input, output, session) {
output$Gauge_JH <- renderGauge({
g()
})
Athlete <- debounce(reactive({input$Athlete}), 500)
output$Gauge_JH_Proxy <- renderUI({
req(Athlete()) # force rerendering
gaugeOutput("Gauge_JH")
})
g <- reactive({
t <- jumpdata %>%
select(Date, Athlete, JumpHeight_cm) %>%
filter(Athlete == input$Athlete)
f <- t %>% filter(Date == max(Date))
minJump = min(t$JumpHeight_cm)
maxJump = max(t$JumpHeight_cm)
diffJump = maxJump-minJump
gauge(
value = f$JumpHeight_cm,
min = min(t$JumpHeight_cm),
max = max(t$JumpHeight_cm),
sectors = gaugeSectors(
success = c(min(t$JumpHeight_cm) + diffJump * 0.8, max(t$JumpHeight_cm)),
warning = c(min(t$JumpHeight_cm) + diffJump * 0.4, min(t$JumpHeight_cm) + diffJump * 0.8),
danger = c(min(t$JumpHeight_cm), min(t$JumpHeight_cm) + diffJump * 0.4)
),
symbol = 'cm'
)
})
}
shinyApp(ui, server)
但是，由于所有这些不便，我会切换库。这是一个 plotly 方法:
library(shiny)
library(shinydashboard)
library(dplyr)
library(plotly)
# jumpdata <- [copy & paste jumpdata here]
jumpdata$Date <- as.Date(jumpdata$Date, "%Y-%m-%d")
ui <- fluidPage(
fluidPage(
sidebarPanel(width = 3,
selectInput("Athlete", label = "Athlete",
choices = unique(jumpdata$Athlete))),
mainPanel(
fluidRow(
plotlyOutput("Gauge_JH_plotly", height = 250, width = "50%")
))
))
server <- function(input, output, session) {
output$Gauge_JH_plotly <- renderPlotly({
t <- jumpdata %>%
select(Date, Athlete, JumpHeight_cm) %>%
filter(Athlete == input$Athlete)
f <- t %>% filter(Date == max(Date))
currentJump = f$JumpHeight_cm
meanJump = mean(t$JumpHeight_cm)
minJump = min(t$JumpHeight_cm)
maxJump = max(t$JumpHeight_cm)
diffJump = maxJump-minJump
success = c(min(t$JumpHeight_cm) + diffJump * 0.8, max(t$JumpHeight_cm))
warning = c(min(t$JumpHeight_cm) + diffJump * 0.4, min(t$JumpHeight_cm) + diffJump * 0.8)
danger = c(min(t$JumpHeight_cm), min(t$JumpHeight_cm) + diffJump * 0.4)
ranges <- unique(c(danger, warning, success))
currentJumpColor <- c("red", "orange", "green")[findInterval(currentJump, ranges, rightmost.closed = TRUE)]
fig <- plot_ly(
domain = list(x = c(0, 1), y = c(0, 1)),
value = currentJump,
title = list(text = "Jump Height [cm]"),
type = "indicator",
mode = "gauge+number+delta",
delta = list(reference = meanJump),
gauge = list(
bar = list(color = currentJumpColor),
axis = list(range = list(minJump, maxJump)),
steps = list(
list(range = danger, color = "lightgray"),
list(range = warning, color = "gray")),
threshold = list(
line = list(color = "green", width = 4),
thickness = 0.75,
value = maxJump)))
fig <- fig %>% layout(margin = list(l=30, r=30, t=80, b=30))
fig
})
}
shinyApp(ui, server)
关于r - Shiny 的 Flexdashboard 响应式(Reactive)仪表未更新，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/60253611/
25
4
0
类型‘AbstractControl’上不存在属性‘Controls’。
主要是我很好奇。 我们有一个名为 Unit 的对象在我们的代码库中 - 代表桥梁或道路的组件。在我们的例子中，看到带有 Unit 的 ReactiveUI 命令可能会模棱两可。作为声明中的泛型之一。
我一直听说六边形架构必须与任何框架无关，并使用接口(interface) (SPI) 来委托(delegate)不属于业务层的每个代码部分。 但是如何在不使用额外框架的情况下通过六边形架构创建一个响应
我读了 Reactive Manifesto . 但我无法理解 event driven architectures 之间的核心差异和 message driven architectures .结果
申请要求: 订阅两个事件流 A 和 B 对于每个 A 事件，一段时间后应该有相应的 B 事件 如果没有相应的 B 到达(及时)，应用程序会监视 A 事件并发出警报 B 事件可以以与 A 事件不同的顺序
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我有一个 ViewModel，它在其初始化程序中有一个输入 init(sliderEvents: Reactive) { 在测试中我想做类似的事情 slider.send(.touchDownInsi
经典实时搜索示例: var searchResults = from input in textBoxChanged from results in GetDa
我有一个响应式(Reactive)管道来处理传入的请求。对于每个请求，我需要调用一个与业务相关的函数 ( doSomeRelevantProcessing )。 完成后，我需要将发生的事情通知一些外部
是否可以为响应式扩展实现基于硬件计时器的自定义调度程序？我该如何开始，有什么好的例子吗？ 我有一个硬件可以每毫秒向我发送一个准确的中断。我想利用它来创建更精确的 RX 调度程序。 更新 感谢 Asti
我正在通过网络浏览 Rx 框架 Material ，我发现了很多。 现在，每当我为此在 google 上搜索时，我还会在 wikipedia 链接中找到“响应式(Reactive)编程”。 由于响应式
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SignalR 与响应式扩展是同一回事吗？你能解释一下为什么或为什么不吗？ 最佳答案 不，它们绝对不是同一件事。 Reactive Extensions 是一个用于创建和组合可观察的数据流或事件流的库
我知道有一种简单的方法可以做到这一点 - 但今晚它打败了我...... 我想知道两个事件是否在 300 毫秒内发生，就像双击一样。 在 300 毫秒内单击两次左键鼠标 - 我知道这是构建响应式(Rea
我们的应用程序使用 Reactive Extensions (Rx)。这些通常通过 Microsoft 的可下载包安装。但是，当我们发布应用程序时，我们会提供 dll 的副本(即 System.Cor
我想了解 Reactive 和 ReactiveStreams 之间的区别，特别是在 RxJava 的上下文中？ 我能想到的最多的是 Reactive Streams 在规范中有一些背压的概念，但它已
我想探索来自 Quarkus 的响应式 REST 客户端的慢速后端，并在他们建议的样本 (https://github.com/quarkusio/quarkus-quickstarts/tree/m
假设我有一个存储桶，我需要从中获取日期早于现在的文档。 该文档如下所示: { id: "1", date: "Some date", otherObjectKEY: "key1" } 对于每个文档，我
我有一个 RIA 服务数据服务，它有几个函数调用，如下所示: public InvokeOperation SomeFunc( SomeData data, Action> callb
我一直在使用 Rx 在单个应用程序中创建事件总线(想想 CQRS/ES)，它似乎工作得很好。然而，在调查了一堆不同的事件溯源框架之后，我还没有看到使用过一次 Rx。与基于反射/容器的调度程序相比，它似
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