Why won't the plot update even though the data has changed - r

In the demo app below, the user can change the Selected state of the data rows by either clicking input$Go1 or select a region in the plot.
Selection the region in the plot is my intended functionality.
However, for a reason I fail to understand, the button does cause a re-render of the plot while select does not, even though both approaches have the same effect, i.e. a change in the values in column Selected of data.table RFImp_FP1
Why is it not working when I select points in the plot?
ui <- fluidPage(
actionButton(inputId = 'Go', label = 'Go'),
actionButton(inputId = 'Go2', label = 'Go2'),
plotlyOutput('RFAcc_FP1', width = 450)
)
server <- function(input, output, session) {
values <- reactiveValues()
observeEvent(input$Go, {
values$RFImp_FP1 <- data.table(MeanDecreaseAccuracy = runif(10, min = 0, max = 1), Variables = letters[1:10])
values$RFImp_FP1$Selected <- 1
})
observeEvent(input$Go2,{
values$RFImp_FP1$Selected[1:4] <- 1-values$RFImp_FP1$Selected[1:4]
print(values$RFImp_FP1$Selected)
})
observe({
if(!is.null(values$RFImp_FP1)) {
parsToChange <- event_data("plotly_selected", source = 'RFAcc_FP1')$y
if(!is.null(event_data("plotly_selected", source = 'RFAcc_FP1'))){
data_df <- values$RFImp_FP1
data_df <- data_df %>% .[, Selected := if_else(Variables %in% parsToChange, 1-Selected, Selected)]
values$RFImp_FP1$Selected <- data_df$Selected
print(values$RFImp_FP1)
}
}
})
observeEvent(values$RFImp_FP1, {
print('seeing change')
})
output$RFAcc_FP1 <- renderPlotly({
values$RFImp_FP1
if(!is.null(values$RFImp_FP1)) {
RFImp_score <- values$RFImp_FP1[order(MeanDecreaseAccuracy)]
plotheight <- length(RFImp_score$Variables) * input$testme
p <- plot_ly(data = RFImp_score,
source = 'RFAcc_FP1',
height = plotheight,
width = 450) %>%
add_trace(x = RFImp_score$MeanDecreaseAccuracy,
y = RFImp_score$Variables,
type = 'scatter',
mode = 'markers',
color = factor(RFImp_score$Selected),
colors = c('#1b73c1', '#797979'),
symbol = factor(RFImp_score$Selected),
symbols = c('circle','x'),
marker = list(size = 6),
hoverinfo = "text",
text = ~paste ('<br>', 'Parameter: ', RFImp_score$Variables,
'<br>', 'Mean decrease accuracy: ', format(round(RFImp_score$MeanDecreaseAccuracy*100, digits = 2), nsmall = 2),'%',
sep = '')) %>%
layout(
margin = list(l = 160, r= 20, b = 70, t = 50),
hoverlabel = list(font=list( color = '#1b73c1'), bgcolor='#f7fbff'),
xaxis = list(title = 'Mean decrease accuracy index (%)',
tickformat = "%",
showgrid = F,
showline = T,
zeroline = F,
nticks = 5,
font = list(size = 8),
ticks = "outside",
ticklen = 5,
tickwidth = 2,
tickcolor = toRGB("black")
),
yaxis = list(categoryarray = RFImp_score$Variables,
autorange = T,
showgrid = F,
showline = T,
autotick = T,
font = list(size = 8),
ticks = "outside",
ticklen = 5,
tickwidth = 2,
tickcolor = toRGB("black")
),
dragmode = "select"
) %>% add_annotations(x = 0.5,
y = 1.05,
textangle = 0,
font = list(size = 14,
color = 'black'),
text = "Contribution to accuracy",
showarrow = F,
xref='paper',
yref='paper')
p$elementId <- NULL ## to surpress warning of widgetid
p <- p %>% config(displayModeBar = F)
p
} else {
p <- plot_ly( type = 'scatter', mode = 'markers', height = '400px', width = 450) %>% layout(
margin = list(l = 160, r= 20, b = 70, t = 50),
xaxis = list(title = 'Mean decrease accuracy index', range= c(0,1), nticks = 2, showline = TRUE),
yaxis = list(title = 'Model input variables', range = c(0,1), nticks = 2, showline = TRUE)) %>%
add_annotations(x = 0.5, y = 1.1, textangle = 0, font = list(size = 14, color = 'black'),
text = 'Contribution to accuracy',
showarrow = F, xref='paper', yref='paper')
p$elementId <- NULL
p <- p %>% config(displayModeBar = F)
p}
})
}
shinyApp(ui, server)
select vs button result:

Don't ask me why, but I managed to get it to work with observeEvent and assigning NULL to the values$RFImp_FP1 before reassigning the altered data.table to it
values$RFImp_FP1 <- NULL
values$RFImp_FP1<- resDF
Full version:
library(shiny)
library(plotly)
library(dplyr)
library(data.table)
testDF <- data.table( MeanDecreaseAccuracy = runif(10, min = 0, max = 1), Variables = letters[1:10])
testDF$Selected <- T
ui <- fluidPage(
plotlyOutput('RFAcc_FP1', width = 450)
)
server <- function(input, output, session) {
values <- reactiveValues(RFImp_FP1 = testDF)
observeEvent(event_data("plotly_selected", source = 'RFAcc_FP1')$y, {
parsToChange <- event_data("plotly_selected", source = 'RFAcc_FP1')$y
resDF <- values$RFImp_FP1 %>% .[, Selected := if_else(Variables %in% parsToChange, !Selected, Selected)]
values$RFImp_FP1 <- NULL ## without this line the plot does not react
values$RFImp_FP1<- resDF ## re-assign the altered data.table to the reactiveValue
})
output$RFAcc_FP1 <- renderPlotly({
RFImp_score <- values$RFImp_FP1[order(MeanDecreaseAccuracy)]
plotheight <- length(RFImp_score$Variables) * 80
p <- plot_ly(data = RFImp_score,
source = 'RFAcc_FP1',
height = plotheight,
width = 450) %>%
add_trace(x = RFImp_score$MeanDecreaseAccuracy,
y = RFImp_score$Variables,
type = 'scatter',
mode = 'markers',
color = factor(RFImp_score$Selected),
colors = c('#F0F0F0', '#1b73c1'),
symbol = factor(RFImp_score$Selected),
symbols = c('x', 'circle'),
marker = list(size = 6),
hoverinfo = "text",
text = ~paste ('<br>', 'Parameter: ', RFImp_score$Variables,
'<br>', 'Mean decrease accuracy: ', format(round(RFImp_score$MeanDecreaseAccuracy*100, digits = 2), nsmall = 2),'%',
sep = '')) %>%
layout(
margin = list(l = 160, r= 20, b = 70, t = 50),
hoverlabel = list(font=list( color = '#1b73c1'), bgcolor='#f7fbff'),
xaxis = list(title = 'Mean decrease accuracy index (%)',
tickformat = "%",
showgrid = F,
showline = T,
zeroline = F,
nticks = 5,
font = list(size = 8),
ticks = "outside",
ticklen = 5,
tickwidth = 2,
tickcolor = toRGB("black")
),
yaxis = list(categoryarray = RFImp_score$Variables,
autorange = T,
showgrid = F,
showline = T,
autotick = T,
font = list(size = 8),
ticks = "outside",
ticklen = 5,
tickwidth = 2,
tickcolor = toRGB("black")
),
dragmode = "select"
) %>% add_annotations(x = 0.5,
y = 1.05,
textangle = 0,
font = list(size = 14,
color = 'black'),
text = "Contribution to accuracy",
showarrow = F,
xref='paper',
yref='paper')
p <- p %>% config(displayModeBar = F)
p
})
}
shinyApp(ui, server)
and to avoid the plotly warnings about not being registered, we can change the observe structure to
observe({
if(!is.null( values$RFImp_FP1)) {
values$Selected <- event_data("plotly_selected", source = 'RFAcc_FP1')$y
}
})
observeEvent(values$Selected, {
parsToChange <- event_data("plotly_selected", source = 'RFAcc_FP1')$y
if(!is.null(event_data("plotly_selected", source = 'RFAcc_FP1'))){
data_df <- values$RFImp_FP1
data_df <- data_df %>% .[, Selected := if_else(Variables %in% parsToChange, !Selected, Selected)]
values$RFImp_FP1 <- NULL
values$RFImp_FP1 <- data_df
}
})
One problem remains: making the same selection twice in a row does not trigger the observers as the selection is identical....

Related

Set text size within marker in r plotly bubble chart

The labels within bubbles are appearing with size proportional to size argument. However I want to keep the labels in constant sizes.
Which argument should I use to keep them at constant size ?
Code that I am using is provided below.
df = data.frame( x = c( 3, 2, 2, 4, 6, 8 ), y = c( 3, 2, 3, 4, 6, 7 ), size = c( 20, 20, 20, 30, 40, 40 ), labels = letters[1:6] )
evo_bubble <- function(plot_data ,x_var, y_var, z_var, t_var) {
# Trasform data into dataframe and quos
df <- data.frame(plot_data)
xenc <- enquo(x_var)
yenc <- enquo(y_var)
zenc <- enquo(z_var)
tenc <- enquo(t_var)
df <- df %>% mutate( bubble_size = !!zenc*50 ) # Modify the denominator if you want to change the dimension of the bubble
# Set parameters for the plot
bubble_pal <- c("white", "#AECEE8" )
gray_axis <- '#dadada'
font_size <- list(size = 12, family = 'Lato')
width <- 0.5
legend_name <- Hmisc::capitalize( quo_name(zenc) ) # WATCH OUT! it works only with the package with Hmisc
decimal <- ',.2f'
sep <- ','
#x_name <- capitalize(quo_name(xenc))
y_name <- Hmisc::capitalize(quo_name(yenc))
p <- plot_ly(df, x = xenc, y = yenc, name = '', text = tenc, type = "scatter", mode = 'markers+text',
hoverlabel = list(font = font_size), size = zenc, color = zenc, hoverinfo = "text+y", colors= bubble_pal,
marker = list(size = df$bubble_size, line = list(color = gray_axis)) ) %>% hide_colorbar()
p <- p %>% layout(xaxis = list(zeroline = F,
title = '',
linecolor = gray_axis,
titlefont = font_size,
tickfont = font_size,
rangemode='tozero',
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yaxis = list(title = y_name,
zerolinecolor = gray_axis,
linecolor = gray_axis,
mirror = "ticks",
hoverformat = '.2f',
linewidth = width,
tickcolor = gray_axis,
tickformat = '.2f',
titlefont = font_size,
tickfont = font_size,
showgrid = FALSE) ) %>%
config(displayModeBar = F)
return(p)
}
evo_bubble( df, x, y, size, labels )
Expected image :
Obtained image :
Please ignore the colors in plot.
You can use add_text to get the desired result:
library(plotly)
library(dplyr)
DF = data.frame( x = c( 3, 2, 2, 4, 6, 8 ), y = c( 3, 2, 3, 4, 6, 7 ), size = c( 20, 20, 20, 30, 40, 40 ), labels = letters[1:6] )
evo_bubble <- function(plot_data, x_var, y_var, z_var, t_var) {
# browser()
# Trasform data into dataframe and quos
DF <- data.frame(plot_data)
xenc <- enquo(x_var)
yenc <- enquo(y_var)
zenc <- enquo(z_var)
tenc <- enquo(t_var)
DF <- DF %>% mutate( bubble_size = !!zenc*50 ) # Modify the denominator if you want to change the dimension of the bubble
# Set parameters for the plot
bubble_pal <- c("white", "#AECEE8" )
gray_axis <- '#dadada'
font_size <- list(size = 12, family = 'Lato')
width <- 0.5
legend_name <- Hmisc::capitalize( quo_name(zenc) ) # WATCH OUT! it works only with the package with Hmisc
decimal <- ',.2f'
sep <- ','
#x_name <- capitalize(quo_name(xenc))
y_name <- Hmisc::capitalize(quo_name(yenc))
p <- plot_ly(DF, x = xenc, y = yenc, name = '', type = "scatter", mode = 'markers',
hoverlabel = list(font = font_size), size = zenc, color = zenc, hoverinfo = "text+y", colors= bubble_pal,
marker = list(size = DF$bubble_size, line = list(color = gray_axis))) %>%
add_text(text = tenc, textfont = font_size, textposition = "middle center") %>% hide_colorbar()
p <- p %>% layout(xaxis = list(zeroline = F,
title = '',
linecolor = gray_axis,
titlefont = font_size,
tickfont = font_size,
rangemode='tozero',
gridcolor = gray_axis,
gridwidth = width,
hoverformat = decimal,
mirror = "ticks",
tickmode = 'array',
tickcolor = gray_axis,
linewidth = width,
showgrid = F ),
yaxis = list(title = y_name,
zerolinecolor = gray_axis,
linecolor = gray_axis,
mirror = "ticks",
hoverformat = '.2f',
linewidth = width,
tickcolor = gray_axis,
tickformat = '.2f',
titlefont = font_size,
tickfont = font_size,
showgrid = FALSE) ) %>%
config(displayModeBar = F)
return(p)
}
evo_bubble( DF, x, y, size, labels )

How to trigger a re-render of a plot when a column content of the plotted data changes with the use of reactive element

In the following app the user can select points in the plot by dragging, which should swap their Selected state between 0 and 1
points will get a shape and color depending on their 0 / 1 state, as a visual support for a user to select/deselect model parameters for the next model run.
in the version of the plots I had in my real app, the plotted data is a reactive variable values$RFImp_FP1 but I found out that the plot does not re-render when the content of column Selected of that data.table (or data.frame) changes.
Therefore I am trying to change it to a reactive object, but I'm failing to figure out how to change the Selected column of reactive data.table `RFImp
my attempts (comments in the code) so far produce either an assign error, or an infinite loop.
P.S.: Since i'm coding the stuff with lapply as I am using the code block several times in my app (identical "modules" with different serial number and using different data as the app takes the user through sequential stages of processing data), the second approach with values (app 2) has my preference as this allows me to do things like this:
lapply(c('FP1', 'FP2'), function(FP){
values[[paste('RFAcc', FP, sep = '_')]] <- ".... code to select a dataframe from model result list object values[[paste('RFResults', FP, sep = '_']]$Accuracy...."
which as far as I know can't be done with objectname <- reactive({....}) as you can't paste on the left side of the <- here
REACTIVE OBJECT APPROACH:
library(shiny)
library(plotly)
library(dplyr)
library(data.table)
ui <- fluidPage(
plotlyOutput('RFAcc_FP1', width = 450)
)
server <- function(input, output, session) {
values <- reactiveValues()
observe({
if(!is.null(RFImp_FP1()$Selected)) {
parsToChange <- event_data("plotly_selected", source = 'RFAcc_FP1')$y
if(!is.null(event_data("plotly_selected", source = 'RFAcc_FP1'))){
data_df <- RFImp_FP1()
data_df <- data_df %>% .[, Selected := if_else(Variables %in% parsToChange, 1-Selected, Selected)]
# how to get the reactive Data frame to update the selected
# values$Selected <- data_df$Selected #creates infinite loop.....
# RFImp_FP1$Selected <- data_df$Selected # throws an error
}
}
})
RFImp_FP1 <- reactive({
# in real app the dataframe RFImp_FP1 is a part of a list with randomForest results,
RFImp_FP1 <- data.table( MeanDecreaseAccuracy = runif(10, min = 0, max = 1), Variables = letters[1:10])
RFImp_FP1$Selected <- 1
# RFImp_FP1$Selected <- if(!is.null(values$Selected)){
# values$Selected } else {1 }
RFImp_FP1
})
output$RFAcc_FP1 <- renderPlotly({
RFImp_FP1()[order(MeanDecreaseAccuracy)]
RFImp_score <- RFImp_FP1()
plotheight <- length(RFImp_score$Variables) * 80
p <- plot_ly(data = RFImp_score,
source = 'RFAcc_FP1',
height = plotheight,
width = 450) %>%
add_trace(x = RFImp_score$MeanDecreaseAccuracy,
y = RFImp_score$Variables,
type = 'scatter',
mode = 'markers',
color = factor(RFImp_score$Selected),
colors = c('#1b73c1', '#797979'),
symbol = factor(RFImp_score$Selected),
symbols = c('circle','x'),
marker = list(size = 6),
hoverinfo = "text",
text = ~paste ('<br>', 'Parameter: ', RFImp_score$Variables,
'<br>', 'Mean decrease accuracy: ', format(round(RFImp_score$MeanDecreaseAccuracy*100, digits = 2), nsmall = 2),'%',
sep = '')) %>%
layout(
margin = list(l = 160, r= 20, b = 70, t = 50),
hoverlabel = list(font=list( color = '#1b73c1'), bgcolor='#f7fbff'),
xaxis = list(title = 'Mean decrease accuracy index (%)',
tickformat = "%",
showgrid = F,
showline = T,
zeroline = F,
nticks = 5,
font = list(size = 8),
ticks = "outside",
ticklen = 5,
tickwidth = 2,
tickcolor = toRGB("black")
),
yaxis = list(categoryarray = RFImp_score$Variables,
autorange = T,
showgrid = F,
showline = T,
autotick = T,
font = list(size = 8),
ticks = "outside",
ticklen = 5,
tickwidth = 2,
tickcolor = toRGB("black")
),
dragmode = "select"
) %>% add_annotations(x = 0.5,
y = 1.05,
textangle = 0,
font = list(size = 14,
color = 'black'),
text = "Contribution to accuracy",
showarrow = F,
xref='paper',
yref='paper')
p <- p %>% config(displayModeBar = F)
p
})
}
shinyApp(ui, server)
PREVIOUS reactiveValues() approach:
as you can see, with this app, the plot does not update when selecting a region in the plot even though the code changes the content of column Selected
ui <- fluidPage(
actionButton(inputId = 'Go', label = 'Go'),
plotlyOutput('RFAcc_FP1', width = 450)
)
server <- function(input, output, session) {
values <- reactiveValues()
observe({
if(!is.null(values$RFImp_FP1)) {
parsToChange <- event_data("plotly_selected", source = 'RFAcc_FP1')$y
if(!is.null(event_data("plotly_selected", source = 'RFAcc_FP1'))){
data_df <- values$RFImp_FP1
data_df <- data_df %>% .[, Selected := if_else(Variables %in% parsToChange, 1-Selected, Selected)]
values$RFImp_FP1 <- data_df
}
}
})
observeEvent(input$Go, {
values$RFImp_FP1 <- data.table(MeanDecreaseAccuracy = runif(10, min = 0, max = 1), Variables = letters[1:10])
values$RFImp_FP1$Selected <- 1
})
output$RFAcc_FP1 <- renderPlotly({
if(!is.null(values$RFImp_FP1)) {
RFImp_score <- values$RFImp_FP1[order(MeanDecreaseAccuracy)]
plotheight <- length(RFImp_score$Variables) * input$testme
p <- plot_ly(data = RFImp_score,
source = 'RFAcc_FP1',
height = plotheight,
width = 450) %>%
add_trace(x = RFImp_score$MeanDecreaseAccuracy,
y = RFImp_score$Variables,
type = 'scatter',
mode = 'markers',
color = factor(RFImp_score$Selected),
colors = c('#1b73c1', '#797979'),
symbol = factor(RFImp_score$Selected),
symbols = c('circle','x'),
marker = list(size = 6),
hoverinfo = "text",
text = ~paste ('<br>', 'Parameter: ', RFImp_score$Variables,
'<br>', 'Mean decrease accuracy: ', format(round(RFImp_score$MeanDecreaseAccuracy*100, digits = 2), nsmall = 2),'%',
sep = '')) %>%
layout(
margin = list(l = 160, r= 20, b = 70, t = 50),
hoverlabel = list(font=list( color = '#1b73c1'), bgcolor='#f7fbff'),
xaxis = list(title = 'Mean decrease accuracy index (%)',
tickformat = "%",
showgrid = F,
showline = T,
zeroline = F,
nticks = 5,
font = list(size = 8),
ticks = "outside",
ticklen = 5,
tickwidth = 2,
tickcolor = toRGB("black")
),
yaxis = list(categoryarray = RFImp_score$Variables,
autorange = T,
showgrid = F,
showline = T,
autotick = T,
font = list(size = 8),
ticks = "outside",
ticklen = 5,
tickwidth = 2,
tickcolor = toRGB("black")
),
dragmode = "select"
) %>% add_annotations(x = 0.5,
y = 1.05,
textangle = 0,
font = list(size = 14,
color = 'black'),
text = "Contribution to accuracy",
showarrow = F,
xref='paper',
yref='paper')
p$elementId <- NULL ## to surpress warning of widgetid
p <- p %>% config(displayModeBar = F)
p
} else {
p <- plot_ly( type = 'scatter', mode = 'markers', height = '400px', width = 450) %>% layout(
margin = list(l = 160, r= 20, b = 70, t = 50),
xaxis = list(title = 'Mean decrease accuracy index', range= c(0,1), nticks = 2, showline = TRUE),
yaxis = list(title = 'Model input variables', range = c(0,1), nticks = 2, showline = TRUE)) %>%
add_annotations(x = 0.5, y = 1.1, textangle = 0, font = list(size = 14, color = 'black'),
text = 'Contribution to accuracy',
showarrow = F, xref='paper', yref='paper')
p$elementId <- NULL
p <- p %>% config(displayModeBar = F)
p}
})
}
shinyApp(ui, server)
Not sure if this is what you want (it´s a bit weird that the plot updates with random numbers after selecting points ;-) ), but I hope it helps.
Instead of using a normal observer I use observeEvent that fires when selecting something in the plot. I generally prefer observeEvent to catch an event. This triggers an update ob a reactiveValues value, which will initially be NULL
library(shiny)
library(plotly)
library(dplyr)
library(data.table)
testDF <- data.table( MeanDecreaseAccuracy = runif(10, min = 0, max = 1), Variables = letters[1:10])
testDF$Selected <- T
ui <- fluidPage(
plotlyOutput('RFAcc_FP1', width = 450)
)
server <- function(input, output, session) {
values <- reactiveValues(val = NULL)
observeEvent(event_data("plotly_selected", source = 'RFAcc_FP1')$y, {
values$val <- runif(1, min = 0, max = 1)
})
RFImp_FP1 <- reactive({
RFImp_FP1 <- testDF
if(!is.null(values$val)) {
parsToChange <- event_data("plotly_selected", source = 'RFAcc_FP1')$y
RFImp_FP1 <- RFImp_FP1 %>% .[, Selected := if_else(Variables %in% parsToChange, 1-Selected, Selected)]
} else { }
# in real app the dataframe RFImp_FP1 is a part of a list with randomForest results,
RFImp_FP1
# RFImp_FP1$Selected <- if(!is.null(values$Selected)){
# values$Selected } else {1 }
})
output$RFAcc_FP1 <- renderPlotly({
RFImp_score <- RFImp_FP1()[order(MeanDecreaseAccuracy)]
plotheight <- length(RFImp_score$Variables) * 80
p <- plot_ly(data = RFImp_score,
source = 'RFAcc_FP1',
height = plotheight,
width = 450) %>%
add_trace(x = RFImp_score$MeanDecreaseAccuracy,
y = RFImp_score$Variables,
type = 'scatter',
mode = 'markers',
color = factor(RFImp_score$Selected),
colors = c('#1b73c1', '#797979'),
symbol = factor(RFImp_score$Selected),
symbols = c('circle','x'),
marker = list(size = 6),
hoverinfo = "text",
text = ~paste ('<br>', 'Parameter: ', RFImp_score$Variables,
'<br>', 'Mean decrease accuracy: ', format(round(RFImp_score$MeanDecreaseAccuracy*100, digits = 2), nsmall = 2),'%',
sep = '')) %>%
layout(
margin = list(l = 160, r= 20, b = 70, t = 50),
hoverlabel = list(font=list( color = '#1b73c1'), bgcolor='#f7fbff'),
xaxis = list(title = 'Mean decrease accuracy index (%)',
tickformat = "%",
showgrid = F,
showline = T,
zeroline = F,
nticks = 5,
font = list(size = 8),
ticks = "outside",
ticklen = 5,
tickwidth = 2,
tickcolor = toRGB("black")
),
yaxis = list(categoryarray = RFImp_score$Variables,
autorange = T,
showgrid = F,
showline = T,
autotick = T,
font = list(size = 8),
ticks = "outside",
ticklen = 5,
tickwidth = 2,
tickcolor = toRGB("black")
),
dragmode = "select"
) %>% add_annotations(x = 0.5,
y = 1.05,
textangle = 0,
font = list(size = 14,
color = 'black'),
text = "Contribution to accuracy",
showarrow = F,
xref='paper',
yref='paper')
p <- p %>% config(displayModeBar = F)
p
})
}
shinyApp(ui, server)

Shiny Plotly output that changes depending on conditions

I'm trying to make a shiny app for some user-friendly data analysis of some data I have, and I'd like to change the outputted Plotly plot depending on which file i'm looking at. Basically, I'd like to have one plot outputted at a time, where I can cycle through several plots (that don't change place in my shiny app) depending on which folder and criteria i'm using. Currently I'm struggeling with this, and I don't know exactly what to do from here. I've attached a few images to clarify what I mean and what I want.
This photo shows my UI and how I want my figures to be displayed. I'd like all figures to show in that same location, depending on the selected file.
When I switch to 'Datalogger', a new plot is generated, and it is outputted below the first one. I'd like it to be placed on top of it, in the exact same location.
Any help you can offer would be very welcome.
Best,
T.
Script:
# Load packages
library(shiny)
library(shinythemes)
library(dplyr)
library(readr)
library(lubridate)
library(plotly)
#picarro
time = as.character(seq(as.POSIXct("2018-06-01 12:00:00"), as.POSIXct("2018-06-01 12:10:00"), by=seconds() )); ch4.corr = runif(length(time), 1980, 2000);
data = data.frame(time, ch4.corr); data$time = as.POSIXct(time);
#datalogger
time = as.character(seq(as.POSIXct("2018-06-01 12:00:00"), as.POSIXct("2018-06-01 12:10:00"), by=seconds() )); PressureOut = runif(length(time), 1010, 1020);
dlog = data.frame(time, PressureOut); dlog$time = as.POSIXct(time);
#dronelog
time = as.character(seq(as.POSIXct("2018-06-01 12:00:00"), as.POSIXct("2018-06-01 12:10:00"), by=seconds() ));
ulog = data.frame(time); ulog$time = as.POSIXct(time);
#------------------------------------------------------------------------------
ui <- fluidPage(
titlePanel("Active AirCore analysis"),
hr(),
fluidRow(
column(3,
radioButtons("fileInput", "File",
choices = c("Picarro", "Datalogger", "Dronelog"),
selected = "Picarro"),
hr(),
conditionalPanel(
condition = "input.fileInput == 'Picarro'",
sliderInput("timeInputPicarro", "Time", as.POSIXct(data$time[1]), as.POSIXct(data$time[length(data$time)]), c(as.POSIXct(data$time[1])+minutes(1), as.POSIXct(data$time[length(data$time)])-minutes(1)), timeFormat = "%H:%M:%S", ticks = T, step = seconds(1), pre = "")),
conditionalPanel(
condition = "input.fileInput == 'Datalogger'",
sliderInput("timeInputDatalogger", "Time", as.POSIXct(dlog$time[1]), as.POSIXct(dlog$time[length(dlog$time)]), c(as.POSIXct(dlog$time[1]), as.POSIXct(dlog$time[length(dlog$time)])), timeFormat = "%H:%M:%S", ticks = T, step = seconds(1), pre = "")),
conditionalPanel(
condition = "input.fileInput == 'Dronelog'",
sliderInput("timeInputDronelog", "Time", as.POSIXct(ulog$time[1]), as.POSIXct(ulog$time[length(ulog$time)]), c(as.POSIXct(ulog$time[1])+minutes(1), as.POSIXct(ulog$time[length(ulog$time)])-minutes(1)), timeFormat = "%H:%M:%S", ticks = T, step = seconds(1), pre = "")),
hr(),
conditionalPanel(
condition = "input.fileInput == 'Picarro'",
radioButtons("picarroPlotInput", "Plot type",
choices = c("Time-series", "Process"),
selected = "Time-series")),
conditionalPanel(
condition = "input.fileInput == 'Datalogger'",
radioButtons("dataloggerPlotInput", "Plot type",
choices = c("Time-series", "Altitude"),
selected = "Time-series")),
hr(),
checkboxGroupInput(inputId='sidebarOptions',
label=('Options'),
choices=c('Blabla', 'Store data', 'BlablaBla')),
hr()),
br(),
mainPanel(
plotlyOutput("dataplot"),
hr(),
plotlyOutput("dlogplot")
)
)
)
server <- function(input, output, session) {
datasetInputPic <- reactive({ data = data; })
datasetInputPicSamp <- reactive({ dat = data[(data$time>=input$timeInputPicarro[1]) & (data$time<=input$timeInputPicarro[2]),]; })
datasetInputDatalogger <- reactive({ dlog = dlog })
datasetInputDronelog <- reactive({ ulog = ulog })
output$dataplot <- renderPlotly({
if( (input$fileInput == 'Picarro' ) & (input$picarroPlotInput == 'Time-series')){
data = datasetInputPic();
data$time = as.POSIXct(data$time);
dat = datasetInputPicSamp();
dat$time = as.POSIXct(dat$time);
sec.col = "red";
f = list(size = 8);
x <- list(title = " ")
y <- list(title = "CH<sub>4</sub> [ppb]")
p2 = plot_ly() %>%
add_trace(data = data,
x = ~time,
y = ~ch4.corr,
type = 'scatter',
mode = "markers",
marker = list(size = 3, color = 'black')) %>%
add_trace(data = dat,
x = ~time,
y = ~ch4.corr,
type = 'scatter',
mode = "markers",
marker = list(size = 3, color = sec.col)) %>%
layout(xaxis = x, yaxis = y, title = '', showlegend = F, titlefont = f);
s1 = subplot(p2, margin = 0.06,nrows=1,titleY = TRUE) %>%
layout(showlegend = F, margin = list(l=50, r=0, b=50, t=10), titlefont = f);
s1
}
})
output$dlogplot <- renderPlotly({
if( (input$fileInput == 'Datalogger' ) & (input$dataloggerPlotInput == 'Time-series')){
data = datasetInputDatalogger();
data$time = as.POSIXct(data$time);
x <- list(title = " ")
y <- list(title = "Outside pressure [mbar]")
p1 = plot_ly() %>%
add_trace(data = data,
y = ~PressureOut,
x = ~time,
type = 'scatter',
mode = "markers",
marker = list(size = 3, color = 'black'));
s1 = subplot(p1, margin = 0.07, nrows=2, titleY = TRUE, titleX = FALSE)
layout(s1, showlegend = F, margin = list(l=100, r=100, b=0, t=100), title = "Datalogger data")
s1
}
})
outputOptions(output, c("dataplot", "dlogplot"), suspendWhenHidden = TRUE)
}
runApp(list(ui = ui, server = server))
Your issue is that in your ui you have written:
mainPanel(
plotlyOutput("dataplot"),
hr(),
plotlyOutput("dlogplot")
)
Using this structure, the "dlogplot" will always display below the "dataplot" because you essentially gave it its own position in the main panel that is below the "dataplot". One solution, if you want the plots to be displayed in the same exact spot when clicking the various buttons, is to give only one plotlyOutput. Next you would put conditional if, else if and else in renderPlotly. For example:
output$dataplot <- renderPlotly({
if( (input$fileInput == 'Picarro' ) & (input$picarroPlotInput == 'Time-series')){
data = datasetInputPic();
data$time = as.POSIXct(data$time);
dat = datasetInputPicSamp();
dat$time = as.POSIXct(dat$time);
sec.col = "red";
f = list(size = 8);
x <- list(title = " ")
y <- list(title = "CH<sub>4</sub> [ppb]")
p2 = plot_ly() %>%
add_trace(data = data,
x = ~time,
y = ~ch4.corr,
type = 'scatter',
mode = "markers",
marker = list(size = 3, color = 'black')) %>%
add_trace(data = dat,
x = ~time,
y = ~ch4.corr,
type = 'scatter',
mode = "markers",
marker = list(size = 3, color = sec.col)) %>%
layout(xaxis = x, yaxis = y, title = '', showlegend = F, titlefont = f);
s1 = subplot(p2, margin = 0.06,nrows=1,titleY = TRUE) %>%
layout(showlegend = F, margin = list(l=50, r=0, b=50, t=10), titlefont = f);
s1
}
else if( (input$fileInput == 'Datalogger' ) & (input$dataloggerPlotInput == 'Time-series')){
data = datasetInputDatalogger();
data$time = as.POSIXct(data$time);
x <- list(title = " ")
y <- list(title = "Outside pressure [mbar]")
p1 = plot_ly() %>%
add_trace(data = data,
y = ~PressureOut,
x = ~time,
type = 'scatter',
mode = "markers",
marker = list(size = 3, color = 'black'));
s1 = subplot(p1, margin = 0.07, nrows=2, titleY = TRUE, titleX = FALSE)
layout(s1, showlegend = F, margin = list(l=100, r=100, b=0, t=100), title = "Datalogger data")
s1
}
})
This code will put the "dlogplot" and the "dataplot" in the same position in your main panel. (You would also need to get rid of output$dlogplot <- renderPlotly({...}) so that it isn't also trying to make that plot.)
Try this out and see if it works for your purposes.

How to add Data markers in Waterfall chart in Plotly

I am trying to plot waterfall chart with the following code. The only issue I am facing currently is the data marker which is not at the correct place. I want the data marker to be just below the end of each bar.
source('./r_files/flatten_HTML.r')
library("plotly")
dataset <- data.frame(Category = c("Akash Jain","Ankit Jain","Pankaj Jain","Nitin Pandey","Gopal Pandit","Ramnath Agarwal"),
TH = c(-62,-71,-1010,44,-44,200))
#dataset <- data.frame(Category = Values$Category, TH = Values$TH)
#dataset <- as.data.frame(cbind(Values$Category,Values$TH))
dataset$Category = dataset$Category
dataset$TH = dataset$TH
dataset$SortedCategoryLabel <- sapply(dataset$Category, function(x) gsub(" ", " <br> ", x))
dataset$SortedCategory <- factor(dataset$SortedCategoryLabel, levels = dataset$SortedCategoryLabel)
dataset$id <- seq_along(dataset$TH)
dataset$type <- ifelse(dataset$TH > 0, "in", "out")
dataset$type <- factor(dataset$type, levels = c("out", "in"))
dataset$end <- cumsum(dataset$TH)
dataset$start <- c(0, head(dataset$end, -1))
Hover_Text <- paste(dataset$Category, "= ", dataset$TH, "<br>")
dataset$colors <- ifelse(dataset$type =="out","red","green")
g <- plot_ly(dataset, x = ~SortedCategory, y = ~start, type = 'bar', marker = list(color = 'rgba(1,1,1, 0.0)'), hoverinfo = 'text') %>%
add_trace(y = dataset$TH , marker = list(color = ~colors), hoverinfo = "text", text = Hover_Text ) %>%
layout(title = '',
xaxis = list(title = ""),
yaxis = list(title = ""),
barmode = 'stack',
margin = list(l = 50, r = 30, b = 50, t = 20),
showlegend = FALSE) %>%
add_annotations(text = dataset$TH,
x = dataset$SortedCategoryLabel,
y = dataset$end,
xref = "dataset$SortedCategoryLabel",
yref = "dataset$end",
font = list(family = 'Arial',
size = 14,
color = "black"),
showarrow = FALSE)
g
Attached the screenshot of the waterfall chart.
So for the first bar, I need the data marker to be just below the end of red bar. Currently it is overlapping with the bar. And similarly for others.
Any help would be really appreciated.
Regards,
Akash
You should specify valign and height inside add_annotations:
vert.align <- c("bottom","top")[as.numeric(dataset$TH>0)+1]
g <- plot_ly(dataset, x = ~SortedCategory, y = ~start, type = 'bar',
marker = list(color = 'rgba(1,1,1, 0.0)'), hoverinfo = 'text') %>%
add_trace(y = dataset$TH , marker = list(color = ~colors), hoverinfo = "text",
text = Hover_Text ) %>%
layout(title = '',
xaxis = list(title = ""),
yaxis = list(title = ""),
barmode = 'stack',
margin = list(l = 50, r = 30, b = 50, t = 20),
showlegend = FALSE) %>%
add_annotations(text = dataset$TH,
x = dataset$SortedCategoryLabel,
y = dataset$end,
xref = "x",
yref = "y",
valign=vert.align, height=40,
font = list(family = 'Arial',
size = 14,
color = "black"),
showarrow = FALSE)
g

Two X-axis in Plotly for R

https://community.plot.ly/t/how-to-plot-multiple-x-axis-in-plotly-in-r/3014/3?u=him4u324
I have posted my question on Plotly community as well
I am trying to display two x-axis with common Y-axis on plotly for R. I was able to do so as well but starting point for each x-axis is separated from each other Whereas I wish them to be represented a common y-axis.
f1 <- list(
family = "Arial, sans-serif",
size = 18,
color = "grey"
)
f2 <- list(
family = "Old Standard TT, serif",
size = 14,
color = "#4477AA"
)
# bottom x-axis
ax <- list(
title = "Number of PBIs",
titlefont = f1,
anchor = "y",
side = "bottom",
showticklabels = TRUE,
tickangle = 0,
tickfont = f2
)
# top x-axis
ax2 <- list(
title = " ",
overlaying = "x",
anchor = "y",
side = "top",
titlefont = f1,
showticklabels = TRUE,
tickangle = 0,
tickfont = f2
)
# common y-axis
ay <- list(
title = "Process & Sub-Process Areas",
titlefont = f1,
showticklabels = TRUE,
tickangle = 0,
tickfont = f2
)
plot_ly(data = scrum %>%
group_by(Master.Project) %>%
summarise(Total_PBIs_Planned=sum(PBIs.Planned.in.Sprint, na.rm = TRUE),
Total_PBIs_Delivered = sum(Actual.PBI.Delivery,na.rm = TRUE)) %>% inner_join(scrum %>% count(Master.Project)), # creating the desired data frame
color = I("#149EF7")) %>%
# for bottom x-axis
add_segments(x = ~Total_PBIs_Planned, xend = ~Total_PBIs_Delivered,
y = ~Master.Project, yend = ~Master.Project, showlegend = FALSE) %>%
add_trace(x = ~Total_PBIs_Planned, y = ~Master.Project,
name = "Total_PBIs_Planned", type = 'scatter',mode = "markers",
marker = list(color = "#149EF7", size = 15,
line = list(color = '#FFFFFF', width = 1))) %>%
add_trace(x = ~Total_PBIs_Delivered, y = ~Master.Project,
name = "Total_PBIs_Delivered",type = 'scatter',mode = "markers",
marker = list(symbol ="circle-dot",color = "#F71430", size = 10,
line = list(color = '#FFFFFF', width = 1))) %>%
# for top x-axis
add_trace(x = ~n, y = ~Master.Project, xaxis = "x2",
name = "No._of_Sub_projects",type = 'bar',
marker = list(color = "#149EF7"),
opacity = 0.1,
hoverinfo = 'text',
text = ~paste(
Master.Project,
'<br> Total Sub Projects: ',n,
'<br> PBIs Planned: ',Total_PBIs_Planned,
'<br> PBIs Delivered: ',Total_PBIs_Delivered
)
) %>%
plotly::layout(
title = "Product Backlog Items - Planned Vs Delivered", titlefont = f1,
xaxis = ax,
yaxis = ay,
xaxis2 = ax2,
margin = list(l = 250)
)
You want to use rangemode = "tozero" in your axis layout.
ax <- list(
title = "Number of PBIs",
titlefont = f1,
anchor = "y",
side = "bottom",
showticklabels = TRUE,
tickangle = 0,
tickfont = f2,
rangemode = 'tozero')
Or you can specify your specific ranges using
range = c(0, 5)
See Plotly help here: https://plot.ly/r/axes/#rangemode

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