app.R
In kaitlynstrickfaden/camTS: Create Time Series Plot of Image Data
### Make time series plot of image data and display images
### Kaitlyn Strickfaden
### 4/7/2021
library(grid)
library(gridExtra)
library(htmltools)
library(htmlwidgets)
library(lubridate)
library(plotly)
library(shiny)
library(shinydashboard)
library(shinyFiles)
library(shinythemes)
library(tidyverse)
# Launch the camTS app
# Launches the app for viewing time series data and associated images.
#
#
# @import dplyr
# @import htmltools
# @import htmlwidgets
# @import lubridate
# @import plotly
# @import shiny
# @import shinydashboard
# @import shinythemes
# @import stringr
# @import tidyverse
# @return A Shiny window
# @export
#
#
# TS_launch_app <- function() {
ui <- fluidPage(theme = shinytheme("slate"),
# App title ----
titlePanel("Plot Time Series with Associated Images"),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Panel for inputs ----
sidebarPanel(
width = 5,
# Input: Choose a directory
shinyDirButton(id = "fdir",
label = "Choose a local directory:",
title = "Navigate to the parent folder:"
),
textOutput("fdirt"),
# Input: Selection of site
# Placeholder for update after selection of parent directory
checkboxGroupInput(inputId = "site",
label = "Site",
choices = NULL,
selected = NULL,
width = "50%"
),
# Input: Selection of variables for secondary y axis
# if you add more choices here, you will also have to add them
# to the "data" reactive argument in the server.
radioButtons(inputId = "secondy",
label = "Secondary Y Axis",
choices = c("None", "Turbidity (NTU)", "Temperature (C)"),
selected = "None",
width = "50%"
),
# Input: Decide if only daytime images should be rendered in side panel
checkboxInput(inputId = "dayonly",
label = "Daytime images only?",
value = FALSE
),
checkboxInput(inputId = "matchonly",
label = "Exact images only?",
value = FALSE
),
## Sidebar Panel Outputs: ---------------
textOutput("text1"), # Date and time
textOutput("text2"), # Site
textOutput("text3"), # Stream stage
textOutput("text4"), # Secondary y axis value
textOutput("text5"), # Image file path
imageOutput("image"), # Image
), # end sidebar panel
# Main panel for displaying outputs ----
mainPanel(
width = 7,
## Main Panel Output: ----
plotlyOutput("clickplot"), # Time series plot
plotOutput("densplot", height = "150px")
) # end mainPanel
) # end sidebarLayout
) # end fluidPage
server <- function(input, output, session) {
################ Choose the directory ###################
# Find a reasonable root directory to start with
# Equivalent of saying " wd <- 'C://Users/{user}' "
wd <- c(`User Directory` = file.path(Sys.getenv("USERPROFILE"), fsep = "\\"))
# Matching server-side to shinyDirButton in UI
shinyDirChoose(input, "fdir",
roots = wd,
session = session,
updateFreq = 0,
defaultRoot = getwd(),
allowDirCreate = FALSE
#filetypes = c("", "jpg", "jpeg", "png", "tif", "tiff", "gif", "csv"),
# (filetypes is apparently a required input, but shiny
# seems to be doing fine without it?)
)
# Take user input for parent directory
fdir <- reactive(input$fdir)
# Observe selection of parent directory
observeEvent(ignoreNULL = TRUE,
suspended = TRUE,
eventExpr = {
input$fdir
},
handlerExpr = {
if (!"path" %in% names(fdir())) return()
# I'm not sure if this is even helping anything, but
# the Internet suggested it is...
}
)
# Outputs parent directory file path in sidebar
output$fdirt <- renderText({
w <- parseDirPath(wd, input$fdir)
normalizePath(file.path(w))
# ShinyDirChoose outputs the file path in a goofy way, so parseDirPath
# fixes the formatting and makes it into a more recognizable file path
})
############### Render clickplot #####################
output$clickplot <- renderPlotly({
# Require a parent directory
req(input$fdir)
# Tell Shiny the right parent directory to use
w <- parseDirPath(wd, input$fdir)
inpath <- normalizePath(file.path(w))
# Update checkboxes for sites in parent directory
updateCheckboxGroupInput(session, "site",
label = "Site",
choices = list.files(inpath),
selected = list.files(inpath)[1:2]
)
# Make an empty tibble for storing data from selected sites
dat <- tibble()
for (i in seq_along(input$site)) {
# Find files corresponding to a site
myImages <- stringr::str_c(inpath, input$site[i],
list.files(stringr::str_c(inpath, input$site[i], sep = "/")),
sep = "/" )
# Find csv file in wd
d <- myImages[stringr::str_ends(myImages, ".csv")]
# Read in and clean up the data
d1 <- read.csv(d)
d1 <- d1 %>%
dplyr::mutate(Stage = dplyr::case_when(Stage > 0 ~ Stage, TRUE ~ 0),
Datetime = lubridate::ymd_hms(Datetime),
image_url = SourceFile) %>%
dplyr::arrange(Datetime)
# Bind these to the compiled tibble
dat <- rbind(dat, d1)
}
# Lets you highlight the click event
hdat <- highlight_key(dat, ~Datetime)
# Allow secondary y axis variables to be a user input
# If you add more choices here, you will also have to give them
# radioButtons in the UI and text output options below
data <- reactive({
switch(input$secondy,
"None" = NA,
"Turbidity (NTU)" = dat$WT,
"Temperature (C)" = dat$TW
)
})
# Make the plot
plotly::plot_ly(
hdat,
x = ~ Datetime,
y = ~ Stage,
color = ~ UserLabel,
colors = "Set1",
type = 'scattergl',
mode = 'lines+markers',
hoverinfo = 'y',
source = "hoverplotsource",
customdata = ~ UserLabel
) %>%
# Second y axis
plotly::add_trace(
x = dat$Datetime,
y = ~ data(),
color = ~ UserLabel,
colors = "Set1",
yaxis = "y2",
name = input$secondy,
mode = 'lines+markers',
marker = list(
symbol = "triangle-up",
size = 6
)
) %>%
# Extra plot customization
plotly::layout(
title = paste0("Time series for ", stringr::str_flatten(input$site, collapse = ", ")),
xaxis = list(
title = "Datetime"
),
yaxis = list(
title = "Stream Stage (m)"
),
yaxis2 = list(
tickfont = list(color = "blue"),
overlaying = "y",
side = "right",
title = str_glue("{input$secondy}")
),
legend = list(
font = list(size = 8)
)
) %>%
event_register('plotly_click') %>% # lets you click points
# Changes the color of the clicked point
plotly::highlight("plotly_click", off = "plotly_doubleclick",
color = toRGB("black"), opacityDim = 1,
selected = attrs_selected(showlegend = FALSE))
}) # End of clickplot renderPlotly
############# Get click information ############################
# Store click information
click_event <- reactive({
event_data(event = "plotly_click", source = "hoverplotsource")
})
# Once a click happens...
observeEvent(click_event(), {
# Tell Shiny the right parent directory to use
w <- parseDirPath(wd, input$fdir)
inpath <- normalizePath(file.path(w))
dat <- dplyr::tibble()
for (i in seq_along(input$site)) {
# Find files corresponding to a site
myImages <- stringr::str_c(inpath, input$site[i],
list.files(stringr::str_c(inpath, input$site[i], sep = "/")),
sep = "/" )
# Find csv file in wd
d <- myImages[stringr::str_ends(myImages, ".csv")]
# Read in and clean up the data
d1 <- read.csv(d)
d1 <- d1 %>%
dplyr::mutate(Stage = dplyr::case_when(Stage > 0 ~ Stage, TRUE ~ 0),
Datetime = lubridate::ymd_hms(Datetime),
Image_Datetime = lubridate::ymd_hms(Image_Datetime),
TimeofDay = "Day",
DTMatch = "Yes",
SibFolder = input$site[i],
image_url = SourceFile) %>%
dplyr::arrange(Datetime)
# Allow for only showing certain images
if (input$dayonly == TRUE) {
d1 <- d1 %>%
dplyr::mutate(TimeofDay = dplyr::case_when(
SceneCaptureType == "Night" ~ "Night",
TRUE ~ "Day")
)
}
if (input$matchonly == TRUE) {
d1 <- d1 %>%
dplyr::mutate(DTMatch = dplyr::case_when(
as.numeric(Datetime) == as.numeric(Image_Datetime) ~ "Yes",
TRUE ~ "No")
)
}
# Bind site data to the compiled tibble
dat <- rbind(dat, d1)
}
# Filter out all data except the clicked point
dat <- dat %>%
dplyr::filter(Datetime == click_event()$x & UserLabel == click_event()$customdata)
# Write the datetime, site name, primary y-axis value,
# secondary y-axis value, and image file name in sidebar
# Datetime
output$text1 <- renderText(
stringr::str_glue("Date and Time: ", as.character(dat$Datetime), sep = " ")
)
# Site name
output$text2 <- renderText(
stringr::str_glue("Site: ", as.character(dat$UserLabel), sep = " ")
)
# Primary y value
output$text3 <- renderText(
stringr::str_glue("Stream Stage: ", as.character(dat$Stage), " m", sep = " ")
)
# Secondary y value
if (input$secondy == "None") {
output$text4 <- renderText(
stringr::str_glue("No secondary y axis value")
)
}
if (input$secondy == "Turbidity (NTU)") {
output$text4 <- renderText(
stringr::str_glue("Turbidity: ", as.character(dat$WT), " NTU", sep = " ")
)
}
if (input$secondy == "Temperature (C)") {
output$text4 <- renderText(
stringr::str_glue("Stream Temperature: ", as.character(dat$TW), " C", sep = " ")
)
}
# Image file name
output$text5 <- renderText(
stringr::str_glue("File Name: ", as.character(dat$FileName), sep = " ")
)
# Draw the corresponding image in the sidebar
# Change to renderPlot with rasterGrob to display multiple images
# Nighttime image
if (input$dayonly == TRUE & dat$TimeofDay == "Night") {
output$image <- renderImage({list(src = "NA")}, deleteFile = FALSE)
output$text5 <- renderText(
stringr::str_glue("File Name: ",
as.character(dat$FileName),
" (nighttime image)", sep = " ")
)
# Non-exact image
} else if (input$matchonly == TRUE & dat$DTMatch == "No") {
output$image <- renderImage({list(src = "NA")}, deleteFile = FALSE)
output$text5 <- renderText(
stringr::str_glue("File Name: ",
as.character(dat$FileName),
" (not exact image)", sep = " ")
)
# Anything else
} else {
output$image <- renderImage({
filename <- normalizePath(file.path(
stringr::str_c(inpath, dat$SibFolder, dat$FileName, sep = "/")))
list(src = filename,
width = session$clientData$output_image_width,
height = session$clientData$output_image_height)},
deleteFile = FALSE)
}
# Some code for plotting multiple images at once that I'm not thrilled with...
# Lots of white space forces images to be very small
# output$image <- renderPlot({
#
# filename <- normalizePath(file.path(dat$SourceFile))
# filename <- filename[file.exists(filename)]
#
# ims <- lapply(filename, readJPEG)
# asGrobs <- lapply(ims, rasterGrob)
# grid.arrange(grobs = asGrobs, nrow = 1)},
#
# width = session$clientData$output_image_width,
# height = session$clientData$output_image_height
# )
}) # end of observeEvent
############ Render plot of image occurrence ####################
output$densplot <- renderPlot({
req(input$fdir)
# Tell Shiny the right parent directory to use
w <- parseDirPath(wd, input$fdir)
inpath <- normalizePath(file.path(w))
# Make an empty tibble for storing data from selected sites
dat <- dplyr::tibble()
for (i in seq_along(input$site)) {
# Find files corresponding to a site
myImages <- stringr::str_c(inpath, input$site[i],
list.files(stringr::str_c(inpath, input$site[i], sep = "/")),
sep = "/" )
# Find csv file in wd
d <- myImages[stringr::str_ends(myImages, ".csv")]
# Read in and append data to tibble
d1 <- read.csv(d)
dat <- rbind(dat, d1)
}
# Summarize image data
dat <- dat %>%
dplyr::select('FileName', 'UserLabel', 'Image_Datetime') %>%
dplyr::distinct() %>%
dplyr::mutate(Datetime = ymd_hms(Image_Datetime),
Date = as_date(Datetime),
Hour = hour(Datetime)
) %>%
dplyr::group_by(Date, Hour) %>%
dplyr::count()
# Plot image data as geom_tiles
ggplot2::ggplot(dat) +
ggplot2::geom_tile(ggplot2::aes(x = Date, y = Hour, fill = n)) +
ggplot2::ylim(0,24) +
ggplot2::scale_fill_gradient(name = "# of\nImages") +
ggplot2::theme_minimal()
}) # End of densplot renderPlotly
} # end of server
shinyApp(ui = ui, server = server)
#} # end TS_launch_app
kaitlynstrickfaden/camTS documentation built on July 11, 2021, 10:46 a.m.
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### Make time series plot of image data and display images
### Kaitlyn Strickfaden
### 4/7/2021
library(grid)
library(gridExtra)
library(htmltools)
library(htmlwidgets)
library(lubridate)
library(plotly)
library(shiny)
library(shinydashboard)
library(shinyFiles)
library(shinythemes)
library(tidyverse)
# Launch the camTS app
# Launches the app for viewing time series data and associated images.
#
#
# @import dplyr
# @import htmltools
# @import htmlwidgets
# @import lubridate
# @import plotly
# @import shiny
# @import shinydashboard
# @import shinythemes
# @import stringr
# @import tidyverse
# @return A Shiny window
# @export
#
#
# TS_launch_app <- function() {
ui <- fluidPage(theme = shinytheme("slate"),
# App title ----
titlePanel("Plot Time Series with Associated Images"),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Panel for inputs ----
sidebarPanel(
width = 5,
# Input: Choose a directory
shinyDirButton(id = "fdir",
label = "Choose a local directory:",
title = "Navigate to the parent folder:"
),
textOutput("fdirt"),
# Input: Selection of site
# Placeholder for update after selection of parent directory
checkboxGroupInput(inputId = "site",
label = "Site",
choices = NULL,
selected = NULL,
width = "50%"
),
# Input: Selection of variables for secondary y axis
# if you add more choices here, you will also have to add them
# to the "data" reactive argument in the server.
radioButtons(inputId = "secondy",
label = "Secondary Y Axis",
choices = c("None", "Turbidity (NTU)", "Temperature (C)"),
selected = "None",
width = "50%"
),
# Input: Decide if only daytime images should be rendered in side panel
checkboxInput(inputId = "dayonly",
label = "Daytime images only?",
value = FALSE
),
checkboxInput(inputId = "matchonly",
label = "Exact images only?",
value = FALSE
),
## Sidebar Panel Outputs: ---------------
textOutput("text1"), # Date and time
textOutput("text2"), # Site
textOutput("text3"), # Stream stage
textOutput("text4"), # Secondary y axis value
textOutput("text5"), # Image file path
imageOutput("image"), # Image
), # end sidebar panel
# Main panel for displaying outputs ----
mainPanel(
width = 7,
## Main Panel Output: ----
plotlyOutput("clickplot"), # Time series plot
plotOutput("densplot", height = "150px")
) # end mainPanel
) # end sidebarLayout
) # end fluidPage
server <- function(input, output, session) {
################ Choose the directory ###################
# Find a reasonable root directory to start with
# Equivalent of saying " wd <- 'C://Users/{user}' "
wd <- c(`User Directory` = file.path(Sys.getenv("USERPROFILE"), fsep = "\\"))
# Matching server-side to shinyDirButton in UI
shinyDirChoose(input, "fdir",
roots = wd,
session = session,
updateFreq = 0,
defaultRoot = getwd(),
allowDirCreate = FALSE
#filetypes = c("", "jpg", "jpeg", "png", "tif", "tiff", "gif", "csv"),
# (filetypes is apparently a required input, but shiny
# seems to be doing fine without it?)
)
# Take user input for parent directory
fdir <- reactive(input$fdir)
# Observe selection of parent directory
observeEvent(ignoreNULL = TRUE,
suspended = TRUE,
eventExpr = {
input$fdir
},
handlerExpr = {
if (!"path" %in% names(fdir())) return()
# I'm not sure if this is even helping anything, but
# the Internet suggested it is...
}
)
# Outputs parent directory file path in sidebar
output$fdirt <- renderText({
w <- parseDirPath(wd, input$fdir)
normalizePath(file.path(w))
# ShinyDirChoose outputs the file path in a goofy way, so parseDirPath
# fixes the formatting and makes it into a more recognizable file path
})
############### Render clickplot #####################
output$clickplot <- renderPlotly({
# Require a parent directory
req(input$fdir)
# Tell Shiny the right parent directory to use
w <- parseDirPath(wd, input$fdir)
inpath <- normalizePath(file.path(w))
# Update checkboxes for sites in parent directory
updateCheckboxGroupInput(session, "site",
label = "Site",
choices = list.files(inpath),
selected = list.files(inpath)[1:2]
)
# Make an empty tibble for storing data from selected sites
dat <- tibble()
for (i in seq_along(input$site)) {
# Find files corresponding to a site
myImages <- stringr::str_c(inpath, input$site[i],
list.files(stringr::str_c(inpath, input$site[i], sep = "/")),
sep = "/" )
# Find csv file in wd
d <- myImages[stringr::str_ends(myImages, ".csv")]
# Read in and clean up the data
d1 <- read.csv(d)
d1 <- d1 %>%
dplyr::mutate(Stage = dplyr::case_when(Stage > 0 ~ Stage, TRUE ~ 0),
Datetime = lubridate::ymd_hms(Datetime),
image_url = SourceFile) %>%
dplyr::arrange(Datetime)
# Bind these to the compiled tibble
dat <- rbind(dat, d1)
}
# Lets you highlight the click event
hdat <- highlight_key(dat, ~Datetime)
# Allow secondary y axis variables to be a user input
# If you add more choices here, you will also have to give them
# radioButtons in the UI and text output options below
data <- reactive({
switch(input$secondy,
"None" = NA,
"Turbidity (NTU)" = dat$WT,
"Temperature (C)" = dat$TW
)
})
# Make the plot
plotly::plot_ly(
hdat,
x = ~ Datetime,
y = ~ Stage,
color = ~ UserLabel,
colors = "Set1",
type = 'scattergl',
mode = 'lines+markers',
hoverinfo = 'y',
source = "hoverplotsource",
customdata = ~ UserLabel
) %>%
# Second y axis
plotly::add_trace(
x = dat$Datetime,
y = ~ data(),
color = ~ UserLabel,
colors = "Set1",
yaxis = "y2",
name = input$secondy,
mode = 'lines+markers',
marker = list(
symbol = "triangle-up",
size = 6
)
) %>%
# Extra plot customization
plotly::layout(
title = paste0("Time series for ", stringr::str_flatten(input$site, collapse = ", ")),
xaxis = list(
title = "Datetime"
),
yaxis = list(
title = "Stream Stage (m)"
),
yaxis2 = list(
tickfont = list(color = "blue"),
overlaying = "y",
side = "right",
title = str_glue("{input$secondy}")
),
legend = list(
font = list(size = 8)
)
) %>%
event_register('plotly_click') %>% # lets you click points
# Changes the color of the clicked point
plotly::highlight("plotly_click", off = "plotly_doubleclick",
color = toRGB("black"), opacityDim = 1,
selected = attrs_selected(showlegend = FALSE))
}) # End of clickplot renderPlotly
############# Get click information ############################
# Store click information
click_event <- reactive({
event_data(event = "plotly_click", source = "hoverplotsource")
})
# Once a click happens...
observeEvent(click_event(), {
# Tell Shiny the right parent directory to use
w <- parseDirPath(wd, input$fdir)
inpath <- normalizePath(file.path(w))
dat <- dplyr::tibble()
for (i in seq_along(input$site)) {
# Find files corresponding to a site
myImages <- stringr::str_c(inpath, input$site[i],
list.files(stringr::str_c(inpath, input$site[i], sep = "/")),
sep = "/" )
# Find csv file in wd
d <- myImages[stringr::str_ends(myImages, ".csv")]
# Read in and clean up the data
d1 <- read.csv(d)
d1 <- d1 %>%
dplyr::mutate(Stage = dplyr::case_when(Stage > 0 ~ Stage, TRUE ~ 0),
Datetime = lubridate::ymd_hms(Datetime),
Image_Datetime = lubridate::ymd_hms(Image_Datetime),
TimeofDay = "Day",
DTMatch = "Yes",
SibFolder = input$site[i],
image_url = SourceFile) %>%
dplyr::arrange(Datetime)
# Allow for only showing certain images
if (input$dayonly == TRUE) {
d1 <- d1 %>%
dplyr::mutate(TimeofDay = dplyr::case_when(
SceneCaptureType == "Night" ~ "Night",
TRUE ~ "Day")
)
}
if (input$matchonly == TRUE) {
d1 <- d1 %>%
dplyr::mutate(DTMatch = dplyr::case_when(
as.numeric(Datetime) == as.numeric(Image_Datetime) ~ "Yes",
TRUE ~ "No")
)
}
# Bind site data to the compiled tibble
dat <- rbind(dat, d1)
}
# Filter out all data except the clicked point
dat <- dat %>%
dplyr::filter(Datetime == click_event()$x & UserLabel == click_event()$customdata)
# Write the datetime, site name, primary y-axis value,
# secondary y-axis value, and image file name in sidebar
# Datetime
output$text1 <- renderText(
stringr::str_glue("Date and Time: ", as.character(dat$Datetime), sep = " ")
)
# Site name
output$text2 <- renderText(
stringr::str_glue("Site: ", as.character(dat$UserLabel), sep = " ")
)
# Primary y value
output$text3 <- renderText(
stringr::str_glue("Stream Stage: ", as.character(dat$Stage), " m", sep = " ")
)
# Secondary y value
if (input$secondy == "None") {
output$text4 <- renderText(
stringr::str_glue("No secondary y axis value")
)
}
if (input$secondy == "Turbidity (NTU)") {
output$text4 <- renderText(
stringr::str_glue("Turbidity: ", as.character(dat$WT), " NTU", sep = " ")
)
}
if (input$secondy == "Temperature (C)") {
output$text4 <- renderText(
stringr::str_glue("Stream Temperature: ", as.character(dat$TW), " C", sep = " ")
)
}
# Image file name
output$text5 <- renderText(
stringr::str_glue("File Name: ", as.character(dat$FileName), sep = " ")
)
# Draw the corresponding image in the sidebar
# Change to renderPlot with rasterGrob to display multiple images
# Nighttime image
if (input$dayonly == TRUE & dat$TimeofDay == "Night") {
output$image <- renderImage({list(src = "NA")}, deleteFile = FALSE)
output$text5 <- renderText(
stringr::str_glue("File Name: ",
as.character(dat$FileName),
" (nighttime image)", sep = " ")
)
# Non-exact image
} else if (input$matchonly == TRUE & dat$DTMatch == "No") {
output$image <- renderImage({list(src = "NA")}, deleteFile = FALSE)
output$text5 <- renderText(
stringr::str_glue("File Name: ",
as.character(dat$FileName),
" (not exact image)", sep = " ")
)
# Anything else
} else {
output$image <- renderImage({
filename <- normalizePath(file.path(
stringr::str_c(inpath, dat$SibFolder, dat$FileName, sep = "/")))
list(src = filename,
width = session$clientData$output_image_width,
height = session$clientData$output_image_height)},
deleteFile = FALSE)
}
# Some code for plotting multiple images at once that I'm not thrilled with...
# Lots of white space forces images to be very small
# output$image <- renderPlot({
#
# filename <- normalizePath(file.path(dat$SourceFile))
# filename <- filename[file.exists(filename)]
#
# ims <- lapply(filename, readJPEG)
# asGrobs <- lapply(ims, rasterGrob)
# grid.arrange(grobs = asGrobs, nrow = 1)},
#
# width = session$clientData$output_image_width,
# height = session$clientData$output_image_height
# )
}) # end of observeEvent
############ Render plot of image occurrence ####################
output$densplot <- renderPlot({
req(input$fdir)
# Tell Shiny the right parent directory to use
w <- parseDirPath(wd, input$fdir)
inpath <- normalizePath(file.path(w))
# Make an empty tibble for storing data from selected sites
dat <- dplyr::tibble()
for (i in seq_along(input$site)) {
# Find files corresponding to a site
myImages <- stringr::str_c(inpath, input$site[i],
list.files(stringr::str_c(inpath, input$site[i], sep = "/")),
sep = "/" )
# Find csv file in wd
d <- myImages[stringr::str_ends(myImages, ".csv")]
# Read in and append data to tibble
d1 <- read.csv(d)
dat <- rbind(dat, d1)
}
# Summarize image data
dat <- dat %>%
dplyr::select('FileName', 'UserLabel', 'Image_Datetime') %>%
dplyr::distinct() %>%
dplyr::mutate(Datetime = ymd_hms(Image_Datetime),
Date = as_date(Datetime),
Hour = hour(Datetime)
) %>%
dplyr::group_by(Date, Hour) %>%
dplyr::count()
# Plot image data as geom_tiles
ggplot2::ggplot(dat) +
ggplot2::geom_tile(ggplot2::aes(x = Date, y = Hour, fill = n)) +
ggplot2::ylim(0,24) +
ggplot2::scale_fill_gradient(name = "# of\nImages") +
ggplot2::theme_minimal()
}) # End of densplot renderPlotly
} # end of server
shinyApp(ui = ui, server = server)
#} # end TS_launch_app
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