Displaying data in the chart based on plotly_click in R shiny - r

Question

Please run this script below, the following R script gives a shiny dashboard with two boxes. I want to reduce the width between two boxes and display data in the right chart. The data should be based on the on click event that we see in the ggplotly function. Also plotly can be used to do the job, I guess. I want the code to fast and efficient at the same time.
## app.R ##
library(shiny)
library(shinydashboard)
library(bupaR)
library(eventdataR)
library(lubridate)
library(dplyr)
library(XML)
library(edeaR)
library(xml2)
library(data.table)
library(ggplot2)
library(ggthemes)
library(glue)
library(tibble)
library(miniUI)
library(tidyr)
library(shinyTime)
library(magrittr)
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
})
patients10 <- reactive({
patients11 <- arrange(patients_eventlog, a1)
patients12 <- patients11 %>% arrange(a1, a2,a3)
patients12 %>%
group_by(a1) %>%
mutate(time = as.POSIXct( a2, format = "%m/%d/%Y %H:%M"),diff_in_sec = a2 -
lag( a2)) %>%
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("trace_table"))
)
)
server <- function(input, output)
{
output$trace_plot <- renderPlotly({
mp1 = ggplot(data = dta(), aes(x = variable,y = trace_id, fill = value,
label = value,
text=paste("Variable:",variable,"<br> Trace
ID:",trace_id,"<br>
Value:",value,"<br> Actuals:",af_percent))) +
geom_tile(colour = "white") +
geom_text(colour = "white", fontface = "bold", size = 2) +
scale_fill_discrete(na.value="transparent") +
theme(legend.position="none") + labs(x = "Traces", y = "Activities")
ggplotly(mp1, tooltip=c("text"), height = 1226, width = 1205)
})
output$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(a3~a1, data = patients10(), FUN = function(y){paste0(unique(y),collapse = "")})
currentPatient <- agg$a1[agg$a3 == valueText]
patients10_final <- patients10() %>%
filter(a1 %in% currentPatient)
datatable(patients10_final, options = list(paging = FALSE, searching = FALSE))
})
}
shinyApp(ui, server)

Answer 1

I have created an easy example how You can use coupled events from plotly with some sample data that is close to Your needs:
library(shiny)
library(plotly)
library(DT)
set.seed(100)
data <- data.frame(A=sample(c('a1','a2','a3'),10,replace=T),
B=1:10,
C=11:20,
D=21:30)
shinyApp(
ui = fluidPage(
plotlyOutput("trace_plot"),
DT::dataTableOutput('tbl')),
server = function(input, output) {
output$trace_plot <- renderPlotly({
plot_ly(data, x=~A,y=~B,z=~C, source = "subset") %>% add_histogram2d()})
output$tbl <- renderDataTable({
event.data <- event_data("plotly_click", source = "subset")
if(is.null(event.data) == T) return(NULL)
print(event.data[ ,c(3:4)])
})
}
)
As You can see by pressing on the first plot we get the subset of data below in the table (x and y values), further you can use it to merge with the primary data to display timestamps etc. .