at the moment I try to create an interactive heatmap in R with apexcharter. This works fine at manual chart creation but fails on interactive use within shiny.
library(shiny)
library(tidyverse)
library(apexcharter)
# Define UI for application that draws a histogram
ui <- fluidPage(
# Application title
titlePanel("Test Heatmap"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
selectInput(
inputId = "heatmap_filter",
label = "heatmap filter",
choices = c(1999, 2008),
selected = 2008
)
),
mainPanel(
apexchartOutput("heatmap")
)
)
)
# Define server logic required to draw a histogram
server <- function(input, output) {
output$heatmap <- renderApexchart({
df <- mpg %>% filter(year == input$heatmap_filter) %>% mutate_if(is.character, as.factor) %>% group_by(manufacturer, class) %>% summarise(cnt = n()) %>% tidyr::complete(class, fill = list(cnt = 0))
q20 <- round(as.numeric(quantile(df %>% filter(cnt>0) %>% pull(cnt), probs = seq(0,1,0.2), na.rm = TRUE))[2],0)
q40 <- round(as.numeric(quantile(df %>% filter(cnt>0) %>% pull(cnt), probs = seq(0,1,0.2), na.rm = TRUE))[3],0)
q60 <- round(as.numeric(quantile(df %>% filter(cnt>0) %>% pull(cnt), probs = seq(0,1,0.2), na.rm = TRUE))[4],0)
q80 <- round(as.numeric(quantile(df %>% filter(cnt>0) %>% pull(cnt), probs = seq(0,1,0.2), na.rm = TRUE))[5],0)
apex(
data = df,
type = "heatmap",
mapping = aes(x = manufacturer, y = class, fill = cnt)
) %>%
ax_dataLabels(enabled = TRUE) %>%
ax_plotOptions(
heatmap = heatmap_opts(
enableShades = FALSE,
colorScale = list(
ranges = list(
list(from = 0, to = q20, color = "#106e45"), #grün
list(from = q20, to = q40, color = "#90dbba"), #leichtes grün
list(from = q40, to = q60, color = "#fff33b"), #gelb
list(from = q60, to = q80, color = "#f3903f"), # orange
list(from = q80, to = 20, color = "#e93e3a") #rot
)
)
)
) %>%
ax_title(
text = paste("Test interactive heatmap",
input$heatmap_filter
), align = "center"
)
})
}
# Run the application
shinyApp(ui = ui, server = server)
With the manual approach everthing works as expected. But when I change the input select only the values changes but not the heatmap quantil ranges and not the title input. Its seems like the input value is not pushing the changes to already calculated variables. I already tried to use an reactive df or reactive variables but so far nothing works.
I added a minimal example where you could change the year input and this should change the title and the color ranges.
Can you help me?
Thanks in advance.
Try setting auto_update to FALSE in the call to apex
apex(
data = df,
type = "heatmap",
auto_update = FALSE,
...
Related
I have a shiny app that lets the user add categories to the facet_wrap. When I start with one category the plot fills the entire box but when I add a second category, the initial plot adjusts to half the initial size. Is there any way I can set the size, such that the first facet fits half the box and doesn't adjust in size when I add a second category?
Here's what I happens when I choose a second facet category:
Current behavior
Here's what I want to happen:
desired behavior
Here is a simple reprex--when you add a second feature from select feature, it adjusts the size of the first plot.
I found a decent solution and added it to this example using the ggh4x::facet_manual. However, this solution does not work with ggplotly and in a bs4dash box, it starts to look crammed in my app where there are upwards of 40 plots. Ideally, I'd like the box to be scrollable. Thanks in advance for any suggestions!
library(shiny)
library(tidyverse)
library(glue)
library(ggh4x)
library(plotly)
library(janitor)
library(bs4Dash)
iris_df <- iris %>%
clean_names() %>%
mutate(extra_feature1 = sepal_length,
extra_feature2 = sepal_width,
extra_feature3 = petal_length,
extra_feature4 = petal_width,
extra_feature5 = sepal_length,
extra_feature6 = sepal_width,
extra_feature7 = petal_length,
extra_feature8 = petal_width) %>%
select(species, everything()) %>%
pivot_longer(-species) %>%
mutate(feature = glue("{name}_{species}"))
iris_species <- iris_df %>%
clean_names() %>%
distinct(species) %>%
pull()
iris_features <- iris_df %>%
clean_names() %>%
distinct(feature) %>%
pull()
# Define UI for application that draws a histogram
ui <- dashboardPage(dark = FALSE,
# Application title
dashboardHeader("Reprex"),
# Sidebar with a slider input for number of bins
dashboardSidebar(skin = "light",
selectInput("species",
"Select species:",
choices = iris_species,
selectize = FALSE,
multiple = TRUE,
selected = iris_species[1]
),
selectInput("features",
"Select feature:",
choices = iris_features,
selectize = TRUE,
multiple = TRUE,
selected = iris_features[1]
),
radioButtons("facets", label = "View all features:",
choices = list("On" = "facet_wrap", "Off" = ""),
selected = "", inline = FALSE)),
# Show a plot of the generated distribution
dashboardBody(
fluidRow(box(
plotOutput("densityPlot"),
width=12,
headerBorder = FALSE,
collapsible = FALSE))
))
# Define server logic required to draw a histogram
server <- function(input, output, session) {
observeEvent(input$species,
{updateSelectInput(session,
"features",
choices = unique(iris_df$feature[iris_df$species == input$species]),
selected = iris_df$feature[1])
})
design <- matrix(c(1:12), 2, 6, byrow = FALSE)
output$densityPlot <- renderPlot({
if (input$facets == '') {
p1 <- iris_df %>%
filter(species %in% input$species) %>%
filter(feature %in% input$features) %>%
ggplot(aes(value, fill = species)) +
geom_density(alpha = .5) +
theme_light() +
facet_manual(~name, scales = "free", design = t(design), respect = FALSE)
#facet_wrap(~name, scales = "free")
p1
}
else {
iris_df %>%
filter(species %in% input$species) %>%
ggplot(aes(value, fill = species)) +
geom_density(alpha = .5) +
theme_light() +
facet_wrap(~name, ncol = 2, scales = "free")
}
})
}
# Run the application
shinyApp(ui = ui, server = server)
Attempting to build off of Stack Exchange Question:
R Highcharter: tooltip customization
Have a R module (below). That ingests some data and provides the UI including highcharter visualizations.
consolidatedlogModuleUI <- function(id){
ns <- NS(id)
tagList(
fluidRow(
bs4Card(highchartOutput(ns("fundedbydayChart")),
width = 12,
collapsible = TRUE)
),
fluidRow(
bs4TabCard(title = "Consolidated Log",
elevation = 2,
width = 12,
bs4TabPanel(
tabName = "tab1",
active = TRUE,
DT::dataTableOutput(ns("consolidatedlogTable"))
),
bs4TabPanel(
tabName = "tab2",
active = FALSE,
DT::dataTableOutput(ns("daysummaryTable"))
)
)
)
)
}
#######
# Consolidated Log Server Module
#######
consolidatedlogModule <- function(input,output,session,data){
ns <- session$ns
data$HasGap <- ifelse(data$GAPGrossRevenue > 0, 1, 0)
data$HasESC <- ifelse(data$ESCGrossRevenue > 0, 1, 0)
consolidatedLogVariables <- c("AcctID", "FSR", "DocSentDate", "DocsToLenderDate",
"FundedDate", "HasGap", "HasESC", "LoanRevenue")
logSummary <- data %>%
group_by(FundedMonthGroup) %>%
summarise(TotalCount = n()
, TotalAmount = sum(LoanRevenue)
, TotalGAP = sum(HasGap)
, TotalESC = sum(HasESC))
daySummary <- data %>%
group_by(FundedDayGroup) %>%
summarise(TotalCount = n()
,TotalAmount = sum(LoanRevenue))
### Consolidated Log Table
output$consolidatedlogTable = DT::renderDataTable({
data[consolidatedLogVariables]
}, extensions = "Responsive", rownames = FALSE,
caption = "Current Consolidated Log",
filter = "bottom"
)
output$daysummaryTable = DT::renderDataTable({
daySummary
}, extensions = "Responsive", rownames = FALSE,
caption = "Current Consolidated Log",
filter = "bottom"
)
### Charts
#Fundedbyday Chart
output$fundedbydayChart = renderHighchart({
highchart() %>%
hc_add_theme(hc_theme_ffx()) %>%
hc_title(text = "Loans Funded By Day") %>%
hc_add_series(data = daySummary, mapping = hcaes(x=FundedDayGroup, y=TotalAmount), type = "column", name = "Daily Loan Revenue",
tooltip = list(pointFormat = "Daily Revenue ${point.TotalAmount} across {point.TotalCount} deals")) %>%
hc_tooltip(crosshairs = TRUE)
# highchart() %>%
# hc_add_theme(hc_theme_ffx()) %>%
# hc_title(text = "Loans Funded By Day") %>%
# hc_add_series(daySummary$TotalAmount, type = "column", name = "Daily Loan Revenue",
# tooltip = list(pointFormat = "Daily Revenue ${point.TotalAmount} across {point.TotalCount} deals")) %>%
# hc_tooltip()
#hchart(daySummary, "column", hcaes(daySummary$FundedDayGroup, daySummary$TotalAmount))
})
}
The highChart function that is commented out works correctly in displaying the columns wanted. The Axis is incorrect and the tooltips is unformatted but the data displays.
Using the Non-commented highchart with the HCAES call and other items, the plot is displayed without any data.
Below is code to reproduce the test data set for the daySummary, the dataframe in question.
FundedDayGroup <- as.Date(c('2019-02-01', '2019-02-4', '2019-02-05'))
TotalCount <- c(1,13,18)
TotalAmount <- c(0, 13166, 15625)
daySummary <- data.frame(FundedDayGroup, TotalCount, TotalAmount)
The issue ended up being Highcharter not interpreting the POSIXct format of the dates and needing to cast the date variable using as.Date. Additionally added some logic to handle the xAxis and setting the datetime. Code below
highchart() %>%
hc_add_theme(hc_theme_ffx()) %>%
hc_title(text = "Loans Funded By Day") %>%
hc_add_series(data = daySummary, mapping = hcaes(x=as.Date(FundedDayGroup), y=TotalAmount), type = "column", name = "Daily Loan Revenue",
tooltip = list(pointFormat = "Daily Revenue ${point.TotalAmount} across {point.TotalCount} deals")) %>%
hc_xAxis(type = "datetime", labels=list(rotation = -45, y = 40) ) %>%
hc_yAxis(title=list(text = "Revenue")) %>%
hc_tooltip(crosshairs = TRUE)
I'm just learning Shiny.
Here's the code that doesn't work (along with some sample data built-in):
library(tidyverse)
library(shiny)
library(plotly)
library(shinyjs)
analysis_df<- data.frame(
report_month = c("jan","jan","jan","jan","jan","jan"),
payee_id = c("59","59","59","59","59","59"),
Payee = sample(LETTERS[1:5],6,replace = TRUE),
Attrib_1 = sample(LETTERS[6:10],6,replace = TRUE),
Attrib_2 = sample(LETTERS[11:15],6,replace = TRUE),
country_of_sale_iso2 = c("AU","AU","AU","NZ","AU","AU"),
currency = c("USD","USD","USD","USD","USD","USD"),
Attrib_3 = c("Pandora-AU","Pandora-AU","Pandora-AU","Pandora-AU","Pandora-AU","Pandora-AU"),
month_paid = c("jun","jun","jun","jun","jun","jun"),
Attrib_4 = sample(LETTERS[16:20],6,replace = TRUE),
Attrib_5 = sample(LETTERS[21:25],6,replace = TRUE),
units = c("2","8","6","2","10","4"),
gross = c("0.003254785","0.013019141","0.009764356","0.003254785","0.016273926","0.00650957"),
reserves_wh = c("0","0","0","0","0","0"),
rsrv_liq = c("0","0","0","0","0","0"),
Attrib_7 = c("0.002753548","0.011014193","0.008260645","0.002753548","0.013767741","0.005507097"),
Attrib_8 = c("3.25E-04","0.001301914","9.76E-04","3.25E-04","0.001627393","6.51E-04"),
Attrib_9 = c("1.76E-04","7.03E-04","5.27E-04","1.76E-04","8.79E-04","3.52E-04"),
Attrib_10 = c("0.03","0.03","0.03","0.03","0.03","0.03"),
Attrib_11 = c("1","1","1","1","1","1"),
Attrib_12 = c("0.003254785","0.013019141","0.009764356","0.003254785","0.016273926","0.00650957")
)
attribs <- c("Attrib_1","Attrib_2","Attrib_3","Attrib_4")
payees <- analysis_df %>% distinct(Payee) %>% as.vector()
ui <- fluidPage(
headerPanel("Product Explorer"),
sidebarPanel(
selectInput('slice_by', 'Color the Bars By:', choices = attribs, selected = "Attrib_1"),
sliderInput('plotHeight', 'Adjust Chart Size',
min = 100, max = 2000, value = 425)
),
mainPanel(
plotlyOutput('Plot', height = "900px")
)
)
server <- function(input, output) {
output$Plot <- renderPlotly({
col_cht <- analysis_df %>%
filter(payee_id == 59) %>%
plot_ly(x = ~report_month,
y = ~gross) %>%
add_bars(color = input$slice_by) %>%
layout(barmode = "stack",
height = input$plotHeight)
})
}
shinyApp(ui, server)
I want the SelectInput to work, and it doesn't.
However, if I replace
add_bars(color = input$slice_by) %>%
with
add_bars(color = ~Attrib_1) %>%
i.e., hard-code it, the plot looks the way it should.
When you are piping with
> analysis_df %>%
the analysis_df dataframe is passed to the functions. So when using ~Attrib_1 you are passing the values in the Attrib_1 column, which are
# > analysis_df$Attrib_1
# [1] H J J H H G
So the plot gets different colors for the levels in analysis_df$Attrib_1.
When you are using input$slice_by that returns only one value, the value selected in Select. So you are getting just one color in the plot.
To get it to work use
color = analysis_df[, input$slice_by]
If you don't want to use analysis_df inside pipe, search about Non-standard Evaluation in R. With lazyeval you can do this,
color = interp(~x, x = as.name(input$slice_by))
I created a chart using highcharter in a shiny dashboard and I am trying to customize the tooltip. The chart is combined line and scatter plot. I would like it to do the following:
1) Have a single box for hover information (it currently has one for the line and one for scatter)
2) Be able to use different column of information that is not used in the series x or y values
I would like the tooltip to display the following information (whether I hover over the scatter point or line) for each particular x-axis value.
Overall
Mean: 2 [Mean: data$avghours]
Dog: 1 [data$animal: data$hours]
Below is the example code I've written that demonstrates my problem:
library (shiny)
library (shinydashboard)
library (highcharter)
header <- dashboardHeader(title = "Example")
body <- dashboardBody(
fluidRow(
box(title = "example", status = "primary", solidHeader = TRUE,
highchartOutput("chart")
)
)
)
sidebar <- dashboardSidebar()
ui <- dashboardPage(header, sidebar, body)
server <- function(input, output) {
date <- c(1,2,3,4,5,6,7,8,9,10)
hours <- c(1,5,4,1,6,5,7,5,4,3)
avghours <- c(2,2,2,3,3,3,2,2,2,2)
animal <- c("dog","cat","cat","cat","cat","cat","cat","cat","dog","dog")
data <- data.frame(date,hours,avghours,animal)
output$chart <- renderHighchart({
highchart() %>%
hc_add_series(name = "Shipments", data=data$hours, type = "scatter", color = "#2670FF", marker = list(radius = 2), alpha = 0.5) %>%
hc_add_series(name = "Rolling Mean", data=data$avghours, color = "#FF7900") %>%
hc_yAxis(min = 0, title = list(text = "Hours")) %>%
hc_tooltip(crosshairs = TRUE)
})
}
shinyApp(ui, server)
Firt of all, you need to add all the data instead give only the vector (the vector DON´T have all the information to the tooltip you want).
To do this you need change the data argument using the data.frame with the hcaes helper function in the mapping argument to define which variable use in every axis:
highchart() %>%
hc_add_series(data = data, mapping = hcaes(x=date, y=hours), name = "Shipments", type = "scatter", color = "#2670FF", marker = list(radius = 2), alpha = 0.5) %>%
hc_add_series(data = data, hcaes(date, avghours), name = "Rolling Mean", type = "line", color = "#FF7900") %>%
hc_yAxis(min = 0, title = list(text = "Hours")) %>%
hc_tooltip(crosshairs = TRUE)
Then you can use the tooltip argument in every hc_add_series to define the tooltip in each series:
highchart() %>%
hc_add_series(data = data, hcaes(date, hours), name = "Shipments", type = "scatter",
tooltip = list(pointFormat = "tooltip with 2 values {point.animal}: {point.hours}")) %>%
hc_add_series(data = data, hcaes(date, avghours), name = "Rolling Mean", type = "line",
tooltip = list(pointFormat = "Avg hour text! {point.avghours}")) %>%
hc_yAxis(min = 0, title = list(text = "Hours")) %>%
hc_tooltip(crosshairs = TRUE)
I have the following code on my Server. R
data_agg_plot1<- reactive({
brush1 <- linked_brush(keys = data_agg()$id, "navy" )
data_agg <- data_agg()
plot1<-data_agg%>%
ggvis(x = ~dates_all) %>%
group_by(factor(dates_all.1)) %>%
layer_points(y = ~ value, fill =~dates_all.1, shape =~dates_all.1) %>%
layer_paths(y = ~ value, stroke = ~dates_all.1 , strokeOpacity := 0.5) %>%
scale_ordinal("fill", range = c("green", "red", "blue"))%>%
scale_ordinal("shape", range = c("triangle-up","triangle-down","circle")) %>%
scale_ordinal("stroke",range=c("green","red","blue")) %>%
brush1$input() %>%
hide_legend(c('stroke','fill'))%>%
add_legend(c('shape','fill'),
title = "Symbol", orient = "left",
values = c("New hires", "Attrition" , "Net Growth"),
properties = legend_props(
title = list(fontSize = 16))) %>%
add_axis("x",properties= axis_props(labels = list(angle=60,align = "left")),
tick_padding =0,
title = "") %>%
add_axis("y", title = "Total Count") %>%
set_options(width = "auto",height = 400) %>%
scale_numeric('y',clamp = TRUE)
return(list(plot1,brush1))
})
so this is a reactive function that returns me a list of 2 functions, a plot and my brush object.
the purpose of doing so is so that I can make my keys reactive - this is so that I can make an additional plot based on my user's selection. think of it as the second plot depends on what the first user highlights in the first plot.
this is my following code:
plot1_data<-reactive({
data_agg_plot1()[[1]]
})
plot1_data%>%bind_shiny("plot1")
selected_plot1 <- reactive({
data_agg_plot1()[[2]]
})
output$test <- renderPrint({
temp <- selected_plot1()$selected()
print(temp)
})
however, when I print out the selection, it is all false,
please refer to the image below:
can anybody explain to me how to overcome this?
I highly suspect I have to re-write my linkedbrush function,
I have tried both solutions from:
linked_brush in ggvis cannot work in Shiny when data change
but it does not work.