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#' A simple shiny app for pedestrian data
#'
#' Provides a GUI to download data of selected sensors over a specified period
#' as a CSV file, accompanied with basic visualisation.
#'
#' @details It offers some basic plots to give a glimpse of the data over a
#' short time period. In order to be reproducible, scripting using `walk_melb`
#' or `run_melb` is recommended.
#'
#' @return A shiny app.
#' @export
#' @seealso [walk_melb], [run_melb]
#'
#' @examples
#' \dontrun{
#' shine_melb()
#' }
shine_melb <- function() {
if (!(requireNamespace("shiny", quietly = TRUE) &&
utils::packageVersion("shiny") >= "1.0.4")) {
stop(
"Packages shiny (>= v1.0.4) required for shine_melb()", ".\n",
"Please install and try again.", call. = FALSE
)
}
if (!requireNamespace("plotly", quietly = TRUE)) {
stop(
"Packages plotly required for shine_melb()", ".\n",
"Please install and try again.", call. = FALSE
)
}
`%>%` <- plotly::`%>%`
ui <- shiny::fluidPage(
shiny::br(),
shiny::fluidRow(
shiny::column(
width = 4,
shiny::dateRangeInput(
"date_rng", "Date range:",
start = Sys.Date() - 3L,
end = Sys.Date() - 1L,
min = "2009-06-01",
max = Sys.Date() - 1L
),
shiny::actionButton(
"goButton", "Update Date",
icon = shiny::icon("refresh")
),
shiny::hr(),
shiny::selectizeInput(
"SensorInfo", "Sensor filter:",
choices = sensor_df$sensor,
multiple = TRUE
),
shiny::downloadButton("downloadCSV", "Download CSV")
),
shiny::column(
width = 7,
plotly::plotlyOutput("drawOverlay", height = 320),
shiny::hr(),
plotly::plotlyOutput("drawMarker", height = 480)
)
)
)
server <- function(input, output, session) {
all_df <- shiny::reactive({
input$goButton
shiny::isolate(walk_melb(
from = input$date_rng[1], to = input$date_rng[2], session = "shiny"
))
})
ped_df <- shiny::reactive({
if (is.null(input$SensorInfo)) {
all_df()
} else {
dplyr::filter(all_df(), Sensor %in% input$SensorInfo)
}
})
output$downloadCSV <- shiny::downloadHandler(
filename = function() {
paste0("pedestrian-", Sys.Date(), ".csv")
},
content = function(file) {
utils::write.csv(ped_df(), file, quote = FALSE, row.names = FALSE)
}
)
output$drawOverlay <- plotly::renderPlotly({
ped_dat <- ped_df() %>%
dplyr::filter(!is.na(Count))
if (NROW(ped_dat) == 0) {
plotly::plot_ly(
x = 1, y = 1, text = "Oops! No data points available."
) %>%
plotly::add_text()
} else {
ped_key <- row.names(ped_dat)
tsplot <- ped_dat %>%
dplyr::group_by(Sensor) %>%
plotly::plot_ly(
x = ~ Date_Time, y = ~ Count,
hoverinfo = "text",
text = ~ paste(
"Sensor: ", Sensor,
"<br> Date Time: ", Date_Time,
"<br> Count:", Count
),
source = "tsplot"
) %>%
plotly::add_lines(alpha = 0.8, key = ~ ped_key)
click <- plotly::event_data("plotly_click", source = "tsplot")
if (!is.null(click)) {
hl_line <- ped_dat[ped_key %in% click$key[1], "Sensor"]
hl_sensor <- ped_dat %>% dplyr::filter(Sensor %in% hl_line)
if (nrow(hl_sensor) != 0) # if it's an empty data frame
tsplot <- plotly::add_lines(
tsplot, data = hl_sensor, color = I("#d73027")
)
}
plotly::layout(
tsplot, title = "Time series plot", showlegend = FALSE,
xaxis = list(title = "Date Time"), yaxis = list(title = "Count")
)
}
})
output$drawMarker <- plotly::renderPlotly({
na_df <- ped_df() %>%
dplyr::left_join(sensor_df, by = c("Sensor" = "sensor")) %>%
dplyr::mutate(NA_ind = is.na(Count))
miss_marker <- plotly::plot_ly(
na_df, hoverinfo = "text",
text = ~ paste(
"Sensor:", Sensor,
"<br> Date Time: ", Date_Time,
"<br> Missing: ", NA_ind
)
) %>%
plotly::add_markers(
x = ~ Date_Time, y = ~ abbr, color = ~ NA_ind,
colors = c("#1b9e77", "#7570b3")
)
plotly::layout(
miss_marker, title = "Missing value indicator",
showlegend = FALSE,
xaxis = list(title = "Date Time"), yaxis = list(title = "")
)
})
}
shiny::shinyApp(ui, server)
}