Hacer el dataframe reactivo

{"my_document.Rmd":"---\ntitle: \"Bike Shares Daily\"\noutput: \n  flexdashboard::flex_dashboard:\n    orientation: columns\n    vertical_layout: fill\nruntime: shiny\n---\n\n```{r global, include=FALSE}\nlibrary(flexdashboard)\nlibrary(readr)\nlibrary(leaflet)\nlibrary(DT)\nlibrary(tidyverse)\nlibrary(lubridate)\nlibrary(plotly)\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         start_hour = hour(start_date)) %>%\n  filter(duration_min <= 8 * 60) # remove trips longer than 8 hours as suspicious data quality\n\n```\n\n\n\nColumn {data-width=200 .sidebar}\n-----------------------------------------------------------------------\n\n```{r}\nsliderInput(\"start_slider\", \n            label = \"Select trip start times to display (hour of day):\",\n            min = 0, \n            max = 24, \n            value = c(7,10), \n            step = 1)\n\nshow_trips_df <- ___({\n  ___ %>%\n    filter(start_hour >= ___ &\n             start_hour <= ___)\n})\n```\n\nColumn {data-width=450}\n-----------------------------------------------------------------------\n### Origins\n\n```{r}\n\n  trips_df %>%\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```\n\nColumn {data-width=350}\n-----------------------------------------------------------------------\n\n### Total Trips\n\n```{r}\n\nvalueBox(prettyNum(trips_df %>%\n                     nrow(), big.mark = ','), \n         icon = 'fa-bicycle')\n\n\n```\n\n### Trips by Duration\n\n```{r}\n\ntrips_df %>%\n    ggplot(aes(x = duration_min)) +\n    theme_bw() +\n    xlab('Trip Duration (min) \\n') +\n    ylab('# trips') +\n    geom_histogram() \n\n```\n\n\n"}