GPC_Capstone_Project-1_Bike-Share_Viz_Map.knit

Document Description

In this document, steps to create interactive visualizations (maps) of the popularity of Cyclistics’ stations are detailed. This is the first in the two-part data-visualization process. The creation of other visualizations is executed and documented separately due to the length of the written codes and texts.

Summary of Process Input and Output

The inputs of the overall data-visualization process are the ridership data tables produced and saved in the previous data-transformation process.

The outputs are visualizations of this data set in the forms of various plots, charts, and maps. Subsequent analyses and answering of the Case Study’s core question (“How do annual members & casual riders use Cyclistic bikes differently?”) are based on the insights provided by these visualizations.

Methodology

The visualization of Cyclistics’ ridership data are carried out to help answer the Case Study’s core question:

How do annual members & casual riders use Cyclistics bikes differently?

As explained in the previous data-transformation, the core question is addressed from the usage intensity, location, and time aspects of Cyclistics’ service from 08.01.2021 to 07.31.2022. Based on this framework, a total of 14 visualizations was created using R (see table below). Unless specified, all visualization facilitate the comparison in the usage of Cyclistics’ bike/services between member and casual riders.:

Table 1. Visualizations for the Bike Share Case Study
Nr.	Aspects	Visualization	Visualization Type	Interactive?
1	Intensity of Use	Overall ridership	Pie Chart	No
2	Intensity of Use	Ridership per bike type	Stacked bar chart	No
3	Intensity of Use	Monthly ridership	Side-by-side bar chart	No
4	Intensity of Use	Weekly ridership by weekday	Heat maps	No
5	Intensity of Use	Daily ridership by hour	Circular bar chart	No
6	Intensity of Use	Effect of weather on ridership	Scatter plots	No
7	Location	Popularity of start stations among all ridership types by number of departures	Interactive maps and reference table	Yes
8	Location	Popularity of end stations among all ridership types by number of arrivals	Interactive maps and reference table	Yes
9	Location	Popularity of start stations among member riders by number of departures	Interactive maps and reference table	Yes
10	Location	Popularity of end stations among member riders by number of arrivals	Interactive maps and reference table	Yes
11	Location	Popularity of start stations among casual riders by number of departures	Interactive maps and reference table	Yes
12	Location	Popularity of end stations among casual riders by number of arrivals	Interactive maps and reference table	Yes
13	Location	Monthly mean ride distance with corresponding standard deviation values	Side-by-side bar charts	No
14	Time	Monthly mean ride duration with corresponding standard deviation values	Side-by-side bar charts	No

This document details the creation of the interactive maps (visualizations 7-12 in the table above). The idea to analyze, transform and visualize data in this direction as well as its execution is adapted and expanded from the previous work of Isabella Peel.

Terminology

As clarified in the previous document, the following terms are often used in completing the Bike Share Case Study :

Rides (or trips): a single, recorded use of Cyclistics’ bike for traveling
Ridership: the number of rides
Ridership Type: the type of the rider (casual or member) who completed a ride

Data-visualization Process in R

1. Preparing R for data visualization

1.1 Install and load necessary R packages

# Install tidyverse to get packages for everyday data analyses
install.packages("tidyverse", repos = "http://cran.us.r-project.org")

# Install skimr to provide summary statistics about variables in data frames, tibbles, data tables and vectors
install.packages("skimr", repos = "http://cran.us.r-project.org")

# Install janitor for Examining and Cleaning Dirty Data (when needed)
install.packages("janitor", repos = "http://cran.us.r-project.org")

# Install geosphere for functions that compute various aspects of distance, direction, area, etc. for geographic (geodetic) coordinates (when needed)
install.packages("geosphere", repos = "http://cran.us.r-project.org")

# Install viridis to get various color palettes for used visualizations
install.packages("viridis", repos = "http://cran.us.r-project.org")

# Install leaflet for the creation of interactive maps
install.packages("leaflet", repos = "http://cran.us.r-project.org")

# Load packages
library(tidyverse)
library(data.table)
library(lubridate)
library(ggplot2)
library(skimr)
library(janitor)
library(dplyr)
library(geosphere)
library(viridis) # color palettes
library(ggpubr) # added functions to ggplot2
library(leaflet) # for geographical map
library(crosstalk) # for sliders on geographical map
library(DT) #for visualized data tables 
library(htmlwidgets) # for widgets on maps
library(htmltools) #tools for creating, manipulating, and writing HTML from R

1.2. Change and view working directory

getwd() #view path of current (initial) working directory

setwd("C:/Users/phucm/OneDrive/My Stuffs/Academic Materials/Books and Materials_by time period/2018-2020_RWTH-AACHEN/01_ADDITIONAL CLASSES/Coursera_Google-CC_Data-Analytics/C08_Capstone")

getwd() #view and confirm path of working directory

1.3. [Optional] View the content of the working directory

list.dirs()

2. Import cleaned ridership data as dataframes

# Load all dataframes from analysis
# Use fread instead of read.csv to achieve the same outcome faster
df_names <- df_names <- c("rides_cleaned",
                          
                          "rides_v_riderships",
                          
                          "rides_v_YMD",
                          "rides_v_YMD_casual",
                          "rides_v_YMD_member",
                          
                          "rides_v_hr",
                          "rides_v_hr_casual",
                          "rides_v_hr_member",
                          
                          "rides_v_month",
                          "rides_v_month_casual",
                          "rides_v_month_member",
                          
                          "rides_v_type",
                          "rides_v_type_casual",
                          "rides_v_type_member",
                          
                          "rides_v_weather",
                          
                          "rides_v_startstation",
                          "rides_v_startstation_all",
                          "rides_v_startstation_casual",
                          "rides_v_startstation_member",
                          
                          "rides_v_endstation",
                          "rides_v_endstation_all",
                          "rides_v_endstation_casual",
                          "rides_v_endstation_member",
                          
                          "rides_duration_distance",
                          "rides_duration_distance_perMonth",
                          "rides_duration_distance_byHr",
                          "rides_duration_distance_perWeekday"
                          )

for(i in 1:length(df_names)){
  assign(df_names[i], 
         fread(paste0("C:/Users/phucm/OneDrive/My Stuffs/Academic Materials/Books and Materials_by time period/2018-2020_RWTH-AACHEN/01_ADDITIONAL CLASSES/Coursera_Google-CC_Data-Analytics/C08_Capstone/CS1_Produced-Dataframes/",
                         df_names[i],
                         ".csv")))}

3. Interactive maps (IMs) showing the popularity of Cyclistics’ stations

All interactive maps (IMs) created here show the popularity of stations for different ridership types. As such, their creations are the same in terms of steps and used functions. The general procedure used to create an IM is:

Prepare visualization elements (i.e., value scale, color palettes, and interactive texts)
Load data into the “crosstalk” widget
Set up the crosstalk widget (i.e., division and dimension of columns)
Populate each column of the widget with the desired interactive element (i.e., slider bar, geographical map, reference table)
Display the resulting widget/IM
(Manually) Save the resulting widget/IM as an .html file

For the sake of retaining knowldge, the codes to create the first four IMs (visualizations 7-10 in Table 1) are comprehensively commented. Comments are omitted for the remaining codes (visualizations 11 and 12 in Table 1) to reduce redundancy, as they are practically the same as the preceding codes.

3.1 IM: Popularity of start stations among all ridership types by number of departures

# Create a value scale showing the number of rides (departures/arrivals) of each station
mybins_startstation <- seq(0, 77040, by = 12840)

# Apply the viridis colour palette to reflect the stations' popularity among riders 
mypalette <- colorBin(
  palette ="viridis",
  domain = rides_v_startstation_all$numtrips,
  na.color = "transparent", 
  bins = mybins_startstation
)

# Prepare interactive texts for each station
mytext <- paste(
  "Station name: ", rides_v_startstation_all$start_station_name, "<br/>",
  "Number of trips: ", rides_v_startstation_all$numtrips, sep = "" 
) %>%
  lapply(htmltools::HTML)

# Load desired dataframes into the interactive crosstalk environment (package: crosstalk)
pop_startstation <- SharedData$new(rides_v_startstation_all)

# Create an interactive html leaflet widget to show the stations' popularity
pop_startstation_map <- bscols(
  
  ## Specify the columns and their widths to house each interactive elements (slider, map, ref. table)
  widths = c(12), # one single column with max width 12, 
  
  ## Specify what would happen to the interactive elements when the size of the html window changes
  device = c("lg"),
  
  ## In a crosstalk widget, elements are arranged by columns by default
  ## To arrange element within the same column, put them in a list()
  ## Create a list to add a slider bar on top of the interactive map and a reference table at the bottom
  list(
    ## Add a slider to interact with the number of trips (column 1 of the widget)
    filter_slider("numtrips", 
                  "Number of trips/departures", 
                  pop_startstation, 
                  column=~numtrips,
                  min = 0,
                  step=50, 
                  width=200
                  ),
    ## Create an interactive map (column 2 of the widget)
    leaflet(pop_startstation) %>%
      addTiles() %>%
      
      ### Set coordinates over the city of Chicago
      setView(lng = -87.6298, lat = 41.8781, zoom = 12.0 
              ) %>% 
      
      ### Set map style
      addProviderTiles("Esri.WorldGrayCanvas") %>% 
      
      ### Add circle markers to represent each station, 
      ### fill colour to show the popularity of each station, 
      ### and interactive tooltip
      addCircleMarkers(
        ~ start_lng, ~ start_lat, 
        fillColor = ~ mypalette(numtrips), 
        fillOpacity = 0.7, 
        color = "white", 
        radius = 6, 
        stroke = FALSE,
        label = mytext,
        labelOptions = labelOptions(
          style = list( 
            "font-weight" = "normal", 
            padding = "3px 8px"
          ), 
          textsize = "13px", 
          direction = "auto"
        ) 
      ) %>% 
      
      
      ### Add a legend
      addLegend( 
        pal = mypalette, 
        values = ~ numtrips, 
        opacity = 0.8,
        title = "Trips/Departures - All Riderships", 
        position = "bottomright"
      ),
  
    ## Add a reference data table next to the map (column 3 of the widget)
    datatable(pop_startstation, 
              extensions="Scroller", 
              width="100%",
              class="compact cell-border", 
              options=list(columnDefs = list(list(visible=FALSE, targets=c(2,3)), list(width = "10%", targets=c(1))),
                           deferRender=TRUE, 
                           scrollY=300, 
                           scroller=TRUE)
              )
    )
  )

# Display resulting interactive widget (html format) in RStudio.
pop_startstation_map

Number of trips/departures

# Note: The html widget is to be exported and saved manually from the "Viewer" tab in RStudio

3.2 IM: Popularity of end stations among all ridership types by number of arrivals

# Create a value scale showing the number of rides (departures/arrivals) of each station
mybins_endstation <- seq(0, 78600, by = 13100)

# Apply the viridis colour palette to reflect the stations' popularity among riders  
mypalette <- colorBin(
  palette ="viridis",
  domain = rides_v_endstation_all$numtrips,
  na.color = "transparent", 
  bins = mybins_endstation
)

# Prepare interactive texts for each station
mytext <- paste(
  "Station name: ", rides_v_endstation_all$end_station_name, "<br/>",
  "Number of trips: ", rides_v_endstation_all$numtrips, sep = "" 
) %>%
  lapply(htmltools::HTML)

# Load desired dataframes into the interactive crosstalk environment (package: crosstalk)
pop_endstation <- SharedData$new(rides_v_endstation_all)

# Create an interactive html leaflet widget to show the stations' popularity
pop_endstation_map <- bscols(
  
  ## Specify the columns and their widths to house each interactive elements (slider, map, ref. table)
  widths = c(12), # one single column with max width 12
  
  ## Specify what would happen to the interactive elements when the size of the html window changes
  device = c("lg"),

  ## In a crosstalk widget, elements are arranged by columns by default
  ## To arrange element within the same column, put them in a list()
  ## Create a list to add a slider bar on top of the interactive map and a reference table at the bottom
  list(
    ## Add a slider to interact with the number of trips (column 1 of the widget)
    filter_slider("numtrips", 
                  "Number of trips/arrivals", 
                  pop_endstation, 
                  column=~numtrips,
                  min = 0,
                  step=50, 
                  width=200
    ),
    
    ## Create an interactive map (column 2 of the widget)
    leaflet(pop_endstation) %>%
      addTiles() %>%
      
      ### Set coordinates over the city of Chicago
      setView(lng = -87.6298, lat = 41.8781, zoom = 12.0 
      ) %>%
      
      ### Set map style
      addProviderTiles("Esri.WorldGrayCanvas") %>%
      
      ### Add circle markers to represent each station, 
      ### fill colour to show the popularity of each station, 
      ### and interactive tooltip
      addCircleMarkers(
        ~ end_lng, ~ end_lat, 
        fillColor = ~ mypalette(numtrips), 
        fillOpacity = 0.7, 
        color = "white", 
        radius = 6, 
        stroke = FALSE,
        label = mytext,
        labelOptions = labelOptions(
          style = list( 
            "font-weight" = "normal", 
            padding = "3px 8px"
          ), 
          textsize = "13px", 
          direction = "auto"
        ) 
      ) %>%
      
      ### Add a legend
      addLegend( 
        pal = mypalette, 
        values = ~ numtrips, 
        opacity = 0.8,
        title = "Trips/Arrivals - All Riderships", 
        position = "bottomright"
      ),
  
    ## Add a reference data table next to the map (column 3 of the widget)
    datatable(pop_endstation, 
              extensions="Scroller", 
              width="100%",
              class="compact cell-border",
              options=list(columnDefs = list(list(visible=FALSE, targets=c(2,3)), 
                                             list(width = "10%", targets=c(1))),
                           deferRender=TRUE, 
                           scrollY=300, 
                           scroller=TRUE)
    )
  )
)

# Display resulting interactive widget (html format) in RStudio.
pop_endstation_map

Number of trips/arrivals

# Note: The html widget is to be exported and saved manually from the "Viewer" tab in RStudio

3.3 IM: Popularity of start stations among member riders by number of departures

# Create a value scale showing the number of rides (departures/arrivals) of each station
mybins_startstation_mem <- seq(0, 25320, by = 4220)

# Apply the viridis colour palette to reflect the stations' popularity among riders 
mypalette <- colorBin(
  palette ="viridis",
  domain = rides_v_startstation_member$numtrips,
  na.color = "transparent", 
  bins = mybins_startstation_mem
)

# Prepare interactive texts for each station
mytext <- paste(
  "Station name: ", rides_v_startstation_member$start_station_name, "<br/>",
  "Number of trips: ", rides_v_startstation_member$numtrips, sep = "" 
) %>%
  lapply(htmltools::HTML)

# Load desired dataframes into the interactive crosstalk environment (package: crosstalk)
pop_startstation_mem <- SharedData$new(rides_v_startstation_member)

# Create an interactive html leaflet widget to show the stations' popularity
pop_startstation_mem_map <- bscols(
  
  ## Specify the columns and their widths to house each interactive elements (slider, map, ref. table)
  widths = c(12), # one single column with max width 12
  
  ## Specify what would happen to the interactive elements when the size of the html window changes
  device = c("lg"),
  
  ## In a crosstalk widget, elements are arranged by columns by default
  ## To arrange element within the same column, put them in a list()
  ## Create a list to add a slider bar on top of the interactive map and a reference table at the bottom
    list(
    ## Add a slider to interact with the number of trips (column 1 of the widget)
    filter_slider("numtrips", 
                  "Number of trips/departures", 
                  pop_startstation_mem, 
                  column=~numtrips,
                  min = 0,
                  step=50, 
                  width=200
    ),
    
    ## Create an interactive map (column 2 of the widget)
    leaflet(pop_startstation_mem) %>%
      addTiles() %>%
      
      ### Set coordinates over the city of Chicago
      setView(lng = -87.6298, lat = 41.8781, zoom = 12.0 
      ) %>%
      
      ### Set map style
      addProviderTiles("Esri.WorldGrayCanvas") %>%
      
      ### Add circle markers to represent each station, 
      ### fill colour to show the popularity of each station, 
      ### and interactive tooltip
      addCircleMarkers(
        ~ start_lng, ~ start_lat, 
        fillColor = ~ mypalette(numtrips), 
        fillOpacity = 0.7, 
        color = "white", 
        radius = 6, 
        stroke = FALSE,
        label = mytext,
        labelOptions = labelOptions(
          style = list( 
            "font-weight" = "normal", 
            padding = "3px 8px"
          ), 
          textsize = "13px", 
          direction = "auto"
        ) 
      ) %>%
      
      ### Add a legend
      addLegend( 
        pal = mypalette, 
        values = ~ numtrips, 
        opacity = 0.8,
        title = "Trips/Departures - Members", 
        position = "bottomright"
      ),
  
    ## Add a reference data table next to the map (column 3 of the widget)
    datatable(pop_startstation_mem, 
              extensions="Scroller", 
              width="100%",
              class="compact cell-border",
              options=list(columnDefs = list(list(visible=FALSE, targets=c(3,4)), 
                                             list(width = "5%", targets=c(1))),
                           deferRender=TRUE, 
                           scrollY=300, 
                           scroller=TRUE)
    )
  )
)

# Display resulting interactive widget (html format) in RStudio.
pop_startstation_mem_map

Number of trips/departures

# Note: The html widget is to be exported and saved manually from the "viewer" tab in RStudio

3.4 IM: Popularity of end stations among member riders by number of arrivals

# Create a value scale showing the number of rides (departures/arrivals) of each station
mybins_endstation_mem <- seq(0, 25320, by = 4220)

# Apply the viridis colour palette to reflect the stations' popularity among riders 
mypalette <- colorBin(
  palette ="viridis",
  domain = rides_v_endstation_member$numtrips,
  na.color = "transparent", 
  bins = mybins_endstation_mem
)

# Prepare interactive texts for each station
mytext <- paste(
  "Station name: ", rides_v_endstation_member$end_station_name, "<br/>",
  "Number of trips: ", rides_v_endstation_member$numtrips, sep = "" 
) %>%
  lapply(htmltools::HTML)

# Load desired dataframes into the interactive crosstalk environment (package: crosstalk)
pop_endstation_mem <- SharedData$new(rides_v_endstation_member)

# Create an interactive html leaflet widget to show the stations' popularity
pop_endstation_mem_map <- bscols(
  
  ## Specify the columns and their widths to house each interactive elements (slider, map, ref. table)
  widths = c(12), # one single column with max width 12
  
  ## Specify what would happen to the interactive elements when the size of the html window changes
  device = c("lg"),
  
  ## In a crosstalk widget, elements are arranged by columns by default
  ## To arrange element within the same column, put them in a list()
  ## Create a list to add a slider bar on top of the interactive map and a reference table at the bottom
  list(
  ## Add a slider to interact with the number of trips (column 1 of the widget)
    filter_slider("numtrips", 
                  "Number of trips/arrivals", 
                  pop_endstation_mem, 
                  column=~numtrips,
                  min = 0,
                  step=50, 
                  width=200
    ),
    
    ## Create an interactive map (column 2 of the widget)
    leaflet(pop_endstation_mem) %>%
      addTiles() %>%
      
      ### Set coordinates over the city of Chicago
      setView(lng = -87.6298, lat = 41.8781, zoom = 12.0 
      ) %>%
      
      ### Set map style
      addProviderTiles("Esri.WorldGrayCanvas") %>%
      
      ### Add circle markers to represent each station, 
      ### fill colour to show the popularity of each station, 
      ### and interactive tooltip
      addCircleMarkers(
        ~ end_lng, ~ end_lat, 
        fillColor = ~ mypalette(numtrips), 
        fillOpacity = 0.7, 
        color = "white", 
        radius = 6, 
        stroke = FALSE,
        label = mytext,
        labelOptions = labelOptions(
          style = list( 
            "font-weight" = "normal", 
            padding = "3px 8px"
          ), 
          textsize = "13px", 
          direction = "auto"
        ) 
      ) %>%
      
      ### Add a legend
      addLegend( 
        pal = mypalette, 
        values = ~ numtrips, 
        opacity = 0.8,
        title = "Trips/Arrivals - Members", 
        position = "bottomright"
      ),
  
    ## Add a reference data table next to the map (column 3 of the widget)
    datatable(pop_endstation_mem, 
              extensions="Scroller", 
              width="100%",
              class="compact cell-border",
              options=list(columnDefs = list(list(visible=FALSE, targets=c(3,4)), 
                                             list(width = "10%", targets=c(1))),
                           deferRender=TRUE, 
                           scrollY=300, 
                           scroller=TRUE)
    )
  )
)

# Display resulting interactive widget (html format) in RStudio.
pop_endstation_mem_map

Number of trips/arrivals

# Note: The html widget is to be exported and saved manually from the "Viewer" tab in RStudio

3.5 IM: Popularity of start stations among casual riders by number of departures

mybins_startstation_cas <- seq(0, 60360, by = 10060)

mypalette <- colorBin(
  palette ="viridis",
  domain = rides_v_startstation_casual$numtrips,
  na.color = "transparent", 
  bins = mybins_startstation_cas
)

mytext <- paste(
  "Station name: ", rides_v_startstation_casual$start_station_name, "<br/>",
  "Number of trips: ", rides_v_startstation_casual$numtrips, sep = "" 
) %>%
  lapply(htmltools::HTML)


pop_startstation_cas <- SharedData$new(rides_v_startstation_casual)

pop_startstation_cas_map <- bscols(
  
  widths = c(12),
  
  device = c("lg"),
  
  list(
    filter_slider("numtrips", 
                  "Number of trips/departures", 
                  pop_startstation_cas, 
                  column=~numtrips,
                  min = 0,
                  step=50, 
                  width=200
    ),
    
    leaflet(pop_startstation_cas) %>%
      addTiles() %>%
      
      setView(lng = -87.6298, lat = 41.8781, zoom = 12.0 
      ) %>%
      
      addProviderTiles("Esri.WorldGrayCanvas") %>%
      
      addCircleMarkers(
        ~ start_lng, ~ start_lat, 
        fillColor = ~ mypalette(numtrips), 
        fillOpacity = 0.7, 
        color = "white", 
        radius = 6, 
        stroke = FALSE,
        label = mytext,
        labelOptions = labelOptions(
          style = list( 
            "font-weight" = "normal", 
            padding = "3px 8px"
          ), 
          textsize = "13px", 
          direction = "auto"
        ) 
      ) %>% 
      
      addLegend( 
        pal = mypalette, 
        values = ~ numtrips, 
        opacity = 0.8,
        title = "Trips/Departures - Casual Riders", 
        position = "bottomright"
      ),

    datatable(pop_startstation_cas, 
              extensions="Scroller", 
              width="100%",
              class="compact cell-border",
              options=list(columnDefs = list(list(visible=FALSE, targets=c(3,4)), 
                                             list(width = "5%", targets=c(1))),
                           deferRender=TRUE, 
                           scrollY=300, 
                           scroller=TRUE)
    )
  )
)

pop_startstation_cas_map

Number of trips/departures

3.6 IM: Popularity of end stations among casual riders by number of arrivals

mybins_endstation_cas <- seq(0, 63240, by = 10540)

mypalette <- colorBin(
  palette ="viridis",
  domain = rides_v_endstation_casual$numtrips,
  na.color = "transparent", 
  bins = mybins_endstation_cas
)

mytext <- paste(
  "Station name: ", rides_v_endstation_casual$end_station_name, "<br/>",
  "Number of trips: ", rides_v_endstation_casual$numtrips, sep = "" 
) %>%
  lapply(htmltools::HTML)

pop_endstation_cas <- SharedData$new(rides_v_endstation_casual)

pop_endstation_cas_map <- bscols(
  
  widths = c(12),
  
  device = c("lg"),
  
  list(
    filter_slider("numtrips", 
                  "Number of trips/arrivals", 
                  pop_endstation_cas, 
                  column=~numtrips,
                  min = 0,
                  step=50, 
                  width=200
    ),
    
    leaflet(pop_endstation_cas) %>%
      addTiles() %>%
      
      setView(lng = -87.6298, lat = 41.8781, zoom = 12.0 
      ) %>%
      
      addProviderTiles("Esri.WorldGrayCanvas") %>%
      
      addCircleMarkers(
        ~ end_lng, ~ end_lat, 
        fillColor = ~ mypalette(numtrips), 
        fillOpacity = 0.7, 
        color = "white", 
        radius = 6, 
        stroke = FALSE,
        label = mytext,
        labelOptions = labelOptions(
          style = list( 
            "font-weight" = "normal", 
            padding = "3px 8px"
          ), 
          textsize = "13px", 
          direction = "auto"
        ) 
      ) %>% 
      
      addLegend( 
        pal = mypalette, 
        values = ~ numtrips, 
        opacity = 0.8,
        title = "Trips/Arrivals - Casual Riders", 
        position = "bottomright"
      ),

    datatable(pop_endstation_cas, 
              extensions="Scroller", 
              width="100%",
              class="compact cell-border",
              options=list(columnDefs = list(list(visible=FALSE, targets=c(3,4)), 
                                             list(width = "10%", targets=c(1))),
                           deferRender=TRUE, 
                           scrollY=300, 
                           scroller=TRUE)
    )
  )
)

pop_endstation_cas_map

Number of trips/arrivals