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Converting our flexdashboard to use Shiny

Let's make this dashboard shiny!

Deze oefening maakt deel uit van de cursus

Building Dashboards with flexdashboard

Cursus bekijken

Oefeninstructies

  • Change the YAML header to make this an interactive RMarkdown document.
  • Generate the dashboard and try selecting different regions in the input sidebar. Observe the changes in the dashboard.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

{"my_document.Rmd":"---\ntitle: \"Bike Shares Daily\"\noutput: \n  flexdashboard::flex_dashboard:\n    orientation: columns\n    vertical_layout: fill\n---\n\n```{r global, include=FALSE}\nlibrary(flexdashboard)\nlibrary(readr)\nlibrary(leaflet)\nlibrary(DT)\nlibrary(tidyverse)\nlibrary(lubridate)\nlibrary(plotly)\n\noptions(shiny.sanitize.errors = FALSE)\n\ntrips_df <- read_csv('https://assets.datacamp.com/production/course_6961/datasets/sanfran_bikeshare_joined_oneday.csv') %>%\n  mutate(duration_min = duration_sec / 60)\n\nsf_bbox <- c(-123.0137, 37.6040, -122.3549, 37.8324)\nsj_bbox <- c(-122.0457, 37.1255, -121.5891, 37.4692)\n\ntrips_df <- trips_df %>%\n  mutate(city = ifelse((start_latitude >= sf_bbox[2] & start_latitude <= sf_bbox[4]) &\n                         (start_longitude >= sf_bbox[1] & start_longitude <= sf_bbox[3]),\n                       'San Francisco', ifelse((start_latitude >= sj_bbox[2] & start_latitude <= sj_bbox[4]) &\n                                                 (start_longitude >= sj_bbox[1] & start_longitude <= sj_bbox[3]),\n                                               'San Jose', 'Other')))\n```\n\nColumn {data-width=200 .sidebar}\n-----------------------------------------------------------------------\n\n```{r}\n\nradioButtons(\"origin_location\", label = \"Select trip origin region to display:\", \n             choices = c('All' = 'all', 'San Francisco' = 'sf', 'San Jose' = 'sj'), \n             selected = c('all'))\n\ntrips <- reactive({\n\n  if(input$origin_location == 'sf') {\n    trips_df <- trips_df %>% filter(city == 'San Francisco')\n  } else if(input$origin_location == 'sj') {\n    trips_df <- trips_df %>% filter(city == 'San Jose')\n  }\n\n  trips_df\n\n})\n\n```\n\nColumn {data-width=450}\n-----------------------------------------------------------------------\n\n### Origins\n\n```{r}\n\nrenderLeaflet({\n  trips() %>%\n    rename(latitude = start_latitude,\n           longitude = start_longitude) %>%\n    group_by(start_station_id, latitude, longitude) %>%\n    count() %>%\n    leaflet() %>%\n    addTiles() %>%\n    addCircles(radius = ~n)\n})\n```\n\nColumn {data-width=350}\n-----------------------------------------------------------------------\n\n### Total Trips\n\n```{r}\n\nrenderValueBox({\n  valueBox(prettyNum(trips() %>%\n                       nrow(), big.mark = ','), \n           icon = 'fa-bicycle')\n})\n\n\n# valueBox(prettyNum(nrow(trips_df), big.mark = ','), icon = 'fa-bicycle')\n```\n\n### Trips by Start Time\n\n```{r}\n\nrenderPlot({trips() %>%\n    mutate(hour = hour(start_date)) %>%\n    group_by(hour) %>%\n    summarize(`Trips Started` = n()) %>%\n    ggplot(aes(x = hour, y = `Trips Started`)) +\n    theme_bw() +\n    ylab('Trips Started \\n') +\n    geom_bar(stat = 'identity') \n})\n\n\n```\n\n\n"}
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