knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
As explained before, the data loads either as a class ftw_atmp
or ftw_tmp
, which will allow two types of data exploration. To explore the data, the ftw
package has incorporated a new method: explore
. This method takes advantage of the tmap
package viewer mode to explore the data. Next, we will see the explore
method for each class.
ftw_atmp
First we will need to access the directory, to do so we use the read_ftw function:
library(ftw) path <- system.file("extdata", package = "ftw") data_atmp <- read_ftw(path, year = 2016, study_area = "Texel")
We can use the default values for the explore
method which will show a map of the edges and nodes, i.e. the network:
explore(data_atmp)
If we change the type, we can explore even more the data, for example changing it to edges
will allow us to view the edges colored by intensity
or speed
. If we change it to nodes
it will show the nodes colored by waiting time
or by count
. If we change it to trips
we can subset the data per day of the week or per hour.
explore(data_atmp, type = "edges", edges_coledges = "speed")
explore(data_atmp, type = "nodes", nodes_colnodes = "waiting time")
# Shows the trips on a monday at 13h00 explore(data_atmp, type = "trips", trips_weekday = 0, trips_hour = 13)
ftw_tmp
The explore
method for temporal data gives the same functionalities as the method for atemporal data but returns two maps, showing the data for 2015 (left) and 2016 (right).
library(ftw) path <- system.file("extdata", package = "ftw") data_tmp <- read_ftw(path, year = c(2015, 2016), study_area = "Smallingerland")
When can call the explore
method for the ftw_tmp
class like this:
explore(data_tmp)
However, the R packages vignettes fail to render it. So, we can test it on our console or view it on the popup window! The last vignette will show how the plot
method works for each class.
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