如果您运行下面的 R Shiny 脚本,您会在 R Shiny 仪表板中看到两个框,左侧的图表显示事件日志数据patients_eventlog"中发生的所有跟踪或活动集的图.患者2"是脚本中的数据,用于解释出现在a1"列中的每个病例,相应的活动基于a2"列.我的要求是,当我单击左侧图表中特定轨迹上的任意位置时,我应该获得相关列a1"、a2"和a3",其中的数据仅具有且仅具有其中活动的那些情况痕迹正在发生.例如.比方说左侧图表中的跟踪具有活动注册"和分类和评估",通过单击跟踪,我想查看只有并且只有这两个活动的案例.这只需要在output$sankey_table"服务器组件中稍作调整.请帮忙,谢谢.
If you run the R shiny script below, you get two boxes in an R shiny dashboard, The chart on the left displays a plot for all the traces or set of activities that occur in the eventlog data "patients_eventlog". "patients2" is a data in the script that explains each and every case appearing in column "a1", and corresponding activities basides in column "a2". My requirement is that when I click anywhere on a particular trace in the chart on left, I should get the relevant columns "a1","a2" and "a3" with the data having only and only those cases in which the activities in that trace are occurring. E.g. Let's say a trace in the chart on left has activites "Registration" and "Triage and Assessment", the by clicking on the trace, I want to see the cases with only and only those two activities. This just needs a minor tweak in the "output$sankey_table" server component. Please help and thanks.
## app.R ##
library(shiny)
library(shinydashboard)
library(bupaR)
library(lubridate)
library(dplyr)
library(xml2)
library(ggplot2)
library(ggthemes)
library(glue)
library(tibble)
library(miniUI)
library(tidyr)
library(shinyWidgets)
library(plotly)
library(DT)
library(splitstackshape)
library(scales)
patients$patient = as.character(patients$patient)
a1 = patients$patient
a2 = patients$handling
a3 = patients$time
a123 = data.frame(a1,a2,a3)
patients_eventlog = simple_eventlog(a123, case_id = "a1",activity_id = "a2",
timestamp = "a3")
dta <- reactive({
tr <- data.frame(traces(patients_eventlog, output_traces = T, output_cases =
F))
tr.df <- cSplit(tr, "trace", ",")
tr.df$af_percent <-
percent(tr.df$absolute_frequency/sum(tr.df$absolute_frequency))
pos <- c(1,4:ncol(tr.df))
tr.df <- tr.df[,..pos]
tr.df <- melt(tr.df, id.vars = c("trace_id","af_percent"))
tr.df
})
Purchase_Final <- reactive({
patients1 <- arrange(patients_eventlog, a1)
patients2 <- patients1 %>% arrange(a1, a3,a2)
patients2 %>%
group_by(a1) %>%
mutate(a3 = as.POSIXct(a3, format = "%m/%d/%Y %H:%M"),diff_in_sec = a3 -
lag(a3)) %>%
mutate(diff_in_sec = ifelse(is.na(diff_in_sec),0,diff_in_sec)) %>%
mutate(diff_in_hours = as.numeric(diff_in_sec/3600)) %>%
mutate(diff_in_days = as.numeric(diff_in_hours/24))
})
ui <- dashboardPage(
dashboardHeader(title = "Trace Chart"),
dashboardSidebar(
width = 0
),
dashboardBody(
box(title = "Trace Chart", status = "primary",height = "455" ,solidHeader =
T,
plotlyOutput("trace_plot"),style = "height:420px; overflow-y:
scroll;overflow-x: scroll;"),
box( title = "Trace Summary", status = "primary", height = "455",solidHeader
= T,
dataTableOutput("sankey_table"))
)
)
server <- function(input, output)
{
output$trace_plot <- renderPlotly({
mp1 = ggplot(data = dta(), aes(x = variable,y = trace_id, fill = value,
label = value,
text=paste("Variable:",variable,"<br> Trace
ID:",trace_id,"<br>
Value:",value,"<br> Actuals:",af_percent))) +
geom_tile(colour = "white") +
geom_text(colour = "white", fontface = "bold", size = 2) +
scale_fill_discrete(na.value="transparent") +
theme(legend.position="none") + labs(x = "Traces", y = "Activities")
ggplotly(mp1, tooltip=c("text"), height = 1226, width = 1205)
})
output$sankey_table <- renderDataTable({
d = event_data("plotly_click")
d
})
}
shinyApp(ui, server)
插件脚本供参考
app.R
library(shiny)
library(shinydashboard)
library(bupaR)
library(lubridate)
library(dplyr)
library(xml2)
library(ggplot2)
library(ggthemes)
library(glue)
library(tibble)
library(miniUI)
library(tidyr)
library(shinyWidgets)
library(plotly)
library(DT)
library(splitstackshape)
library(scales)
dta <- reactive({
tr <- data.frame(traces(patients, output_traces = T, output_cases = F))
tr.df <- cSplit(tr, "trace", ",")
tr.df$af_percent <-
percent(tr.df$absolute_frequency/sum(tr.df$absolute_frequency))
pos <- c(1,4:ncol(tr.df))
tr.df <- tr.df[,..pos]
tr.df <- melt(tr.df, id.vars = c("trace_id","af_percent"))
tr.df
})
patients10 <- reactive({
patients11 <- arrange(patients, patient)
patients12 <- patients11 %>% arrange(patient, time,handling_id)
patients12 %>%
group_by(patient) %>%
mutate(time = as.POSIXct(time, format = "%m/%d/%Y %H:%M"),diff_in_sec = time
- lag(time)) %>%
mutate(diff_in_sec = ifelse(is.na(diff_in_sec),0,diff_in_sec)) %>%
mutate(diff_in_hours = as.numeric(diff_in_sec/3600)) %>%
mutate(diff_in_days = as.numeric(diff_in_hours/24))
})
ui <- dashboardPage(
dashboardHeader(title = "Sankey Chart"),
dashboardSidebar(
width = 0
),
dashboardBody(
box(title = "Sankey Chart", status = "primary",height = "455" ,solidHeader =
T,
plotlyOutput("trace_plot")),
box( title = "Case Summary", status = "primary", height = "455",solidHeader
= T,
dataTableOutput("trace_table"))
)
)
server <- function(input, output)
{
output$trace_plot <- renderPlotly({
mp1 = ggplot(data = dta(), aes(x = variable,y = trace_id, fill = value,
label = value,
text=paste("Variable:",variable,"<br> Trace
ID:",trace_id,"<br>
Value:",value,"<br> Actuals:",af_percent))) +
geom_tile(colour = "white") +
geom_text(colour = "white", fontface = "bold", size = 2) +
scale_fill_discrete(na.value="transparent") +
theme(legend.position="none") + labs(x = "Traces", y = "Activities")
ggplotly(mp1, tooltip=c("text"), height = 516, width = 605)
})
output$trace_table <- renderDataTable({
req(event_data("plotly_click"))
Values <- dta() %>%
filter(trace_id == event_data("plotly_click")[["y"]]) %>%
select(value)
valueText <- paste0(Values[[1]] %>% na.omit(),collapse = "")
agg <- aggregate(handling~patient, data = patients10(), FUN = function(y)
{paste0(unique(y),collapse = "")})
currentPatient <- agg$patient[agg$handling == valueText]
patients10_final <- patients10() %>%
filter(patient %in% currentPatient)
datatable(patients10_final, options = list(paging = FALSE, searching =
FALSE))
})
}
shinyApp(ui, server)
推荐答案
由于您给出了如此庞大的示例并且很难解码代码中的每一行,因此我删除了一些代码以获取您选择的行事件.
Since you have given such a huge example and its hard to decode each and every line in your code, I have removed some code to get the rows for your selected event.
而不是 event_data("plotly_click")[["y"]])
我使用 x 作为 vent_data("plotly_click")$x
并获得trace_id 使用 paste0
函数.
Instead of event_data("plotly_click")[["y"]])
I am using the x as vent_data("plotly_click")$x
and getting the trace_id by using paste0
function.
我为获取行而修改的代码部分是:
The part of the code that I have modified to get the rows is:
output$trace_table <- renderDataTable({
req(event_data("plotly_click"))
trace = event_data("plotly_click")$x
Values <- dta() %>%
filter(variable == paste0("trace_",trace))# %>%
#select(value)
datatable(Values)
# valueText <- paste0(Values[[1]] %>% na.omit(),collapse = "")
# agg <- aggregate(handling~patient, data = patients10(), FUN = function(y)
# {paste0(unique(y),collapse = "")})
#
# currentPatient <- agg$patient[agg$handling == valueText]
#
# patients10_final <- patients10() %>%
# filter(patient %in% currentPatient)
#
# datatable(patients10_final, options = list(paging = FALSE, searching =
# FALSE))
})
完整代码如下:
library(shiny)
library(shinydashboard)
library(bupaR)
library(lubridate)
library(dplyr)
library(xml2)
library(ggplot2)
library(ggthemes)
library(glue)
library(tibble)
library(miniUI)
library(tidyr)
library(shinyWidgets)
library(plotly)
library(DT)
library(splitstackshape)
library(scales)
dta <- reactive({
tr <- data.frame(traces(patients, output_traces = T, output_cases = F))
tr.df <- cSplit(tr, "trace", ",")
tr.df$af_percent <-
percent(tr.df$absolute_frequency/sum(tr.df$absolute_frequency))
pos <- c(1,4:ncol(tr.df))
tr.df <- tr.df[,..pos]
tr.df <- melt(tr.df, id.vars = c("trace_id","af_percent"))
tr.df
})
patients10 <- reactive({
patients11 <- arrange(patients, patient)
patients12 <- patients11 %>% arrange(patient, time,handling_id)
patients12 %>%
group_by(patient) %>%
mutate(time = as.POSIXct(time, format = "%m/%d/%Y %H:%M"),diff_in_sec = time
- lag(time)) %>%
mutate(diff_in_sec = ifelse(is.na(diff_in_sec),0,diff_in_sec)) %>%
mutate(diff_in_hours = as.numeric(diff_in_sec/3600)) %>%
mutate(diff_in_days = as.numeric(diff_in_hours/24))
})
ui <- dashboardPage(
dashboardHeader(title = "Sankey Chart"),
dashboardSidebar(
width = 0
),
dashboardBody(
box(title = "Sankey Chart", status = "primary",height = "455" ,solidHeader =
T,
plotlyOutput("trace_plot")),
box( title = "Case Summary", status = "primary", height = "455",solidHeader
= T,
dataTableOutput("trace_table"))
)
)
server <- function(input, output)
{
output$trace_plot <- renderPlotly({
mp1 = ggplot(data = dta(), aes(x = variable,y = trace_id, fill = value,
label = value,
text=paste("Variable:",variable,"<br> Trace
ID:",trace_id,"<br>
Value:",value,"<br> Actuals:",af_percent))) +
geom_tile(colour = "white") +
geom_text(colour = "white", fontface = "bold", size = 2) +
scale_fill_discrete(na.value="transparent") +
theme(legend.position="none") + labs(x = "Traces", y = "Activities")
ggplotly(mp1, tooltip=c("text"), height = 516, width = 605)
})
output$trace_table <- renderDataTable({
req(event_data("plotly_click"))
trace = event_data("plotly_click")$x
Values <- dta() %>%
filter(variable == paste0("trace_",trace))# %>%
#select(value)
datatable(Values)
# valueText <- paste0(Values[[1]] %>% na.omit(),collapse = "")
# agg <- aggregate(handling~patient, data = patients10(), FUN = function(y)
# {paste0(unique(y),collapse = "")})
#
# currentPatient <- agg$patient[agg$handling == valueText]
#
# patients10_final <- patients10() %>%
# filter(patient %in% currentPatient)
#
# datatable(patients10_final, options = list(paging = FALSE, searching =
# FALSE))
})
}
shinyApp(ui, server)
希望对你有帮助!
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