From b65fd36496c92a2e66a0bae3a0b45a97091d1f5f Mon Sep 17 00:00:00 2001 From: Ketsia Guichard Date: Wed, 22 May 2024 15:31:20 +0200 Subject: [PATCH] update references for CRAN --- README.md | 25 +- docs/404.html | 2 +- docs/LICENSE-text.html | 2 +- docs/articles/data-details.html | 2 +- docs/articles/data-visualization.html | 687 ++++++++++++++++++ .../figure-html/unnamed-chunk-10-1.png | Bin 0 -> 112072 bytes .../figure-html/unnamed-chunk-11-1.png | Bin 0 -> 95946 bytes .../figure-html/unnamed-chunk-12-1.png | Bin 0 -> 94470 bytes .../figure-html/unnamed-chunk-13-1.png | Bin 0 -> 79572 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 0 -> 91252 bytes .../figure-html/unnamed-chunk-15-1.png | Bin 0 -> 72800 bytes .../figure-html/unnamed-chunk-16-1.png | Bin 0 -> 72536 bytes .../figure-html/unnamed-chunk-17-1.png | Bin 0 -> 82699 bytes .../figure-html/unnamed-chunk-18-1.png | Bin 0 -> 119613 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 0 -> 66982 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 0 -> 153795 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 0 -> 108889 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 0 -> 169258 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 0 -> 99893 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 0 -> 108856 bytes docs/articles/index.html | 4 +- docs/authors.html | 2 +- docs/index.html | 20 +- docs/news/index.html | 2 +- docs/pkgdown.yml | 4 +- docs/reference/check_options_graph.html | 2 +- docs/reference/convert_string_to_list.html | 2 +- docs/reference/create_config.html | 2 +- docs/reference/enrich_dates.html | 2 +- docs/reference/enrich_name.html | 2 +- docs/reference/enrich_special_days.html | 2 +- docs/reference/enrich_traffic.html | 2 +- docs/reference/enrich_uptime.html | 2 +- docs/reference/filter_agg.html | 2 +- docs/reference/get_api_state.html | 2 +- docs/reference/get_custom_palette.html | 2 +- docs/reference/get_graph_subtitles.html | 2 +- docs/reference/get_segment_name.html | 2 +- docs/reference/get_segments.html | 2 +- docs/reference/get_telraam_token.html | 2 +- docs/reference/gg_availability.html | 2 +- docs/reference/gg_car_speed_histogram.html | 2 +- docs/reference/gg_car_speed_v85.html | 2 +- docs/reference/gg_traffic_avg.html | 2 +- docs/reference/gg_traffic_evolution.html | 2 +- docs/reference/import_sensor.html | 2 +- docs/reference/index.html | 2 +- docs/reference/is_vacation.html | 2 +- docs/reference/melt_direction_mode.html | 2 +- docs/reference/preprocess_car_speed.html | 2 +- docs/reference/retrieve_sensor.html | 2 +- docs/reference/seq_by_3_month.html | 2 +- docs/reference/set_global_vars.html | 2 +- docs/reference/set_telraam_token.html | 2 +- docs/reference/traffic.html | 2 +- docs/reference/write_update_data.html | 2 +- docs/search.json | 1 + docs/sitemap.xml | 3 + ...sualization.Rmd => data-visualization.Rmd} | 0 59 files changed, 742 insertions(+), 74 deletions(-) create mode 100644 docs/articles/data-visualization.html create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-10-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-11-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-12-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-13-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-14-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-15-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-16-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-17-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-18-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-4-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-5-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-6-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-7-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-8-1.png create mode 100644 docs/articles/data-visualization_files/figure-html/unnamed-chunk-9-1.png create mode 100644 docs/search.json rename vignettes/{data_visualization.Rmd => data-visualization.Rmd} (100%) diff --git a/README.md b/README.md index c511675..24c0f79 100644 --- a/README.md +++ b/README.md @@ -8,9 +8,7 @@ The aim of this package is to grant the user tools for data visualisation and da An example of using this package can be seen in this [`application`](https://agistaterre.shinyapps.io/mov-around/), although it also utilizes more advanced representations. -[`Link to Github repository`](https://github.com/KetsiaGuichard/telraamStats) - -This package is currently in a development version, feel free to contact us for more information ! +[`Link to Github repository`](https://github.com/agistaterre/telraamStats) # Telraam Sensors @@ -38,28 +36,17 @@ This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 Inter # Install Package -If you want to install this package from Github, you can use the devtools package: - ``` r -if(!require(devtools)){ - install.packages("devtools") -} -devtools::install_github("https://github.com/KetsiaGuichard/telraamStats", - dependencies = TRUE, - build_vignettes = TRUE) +install.packages('telraamStats') +library(telraamStats) ``` -Vignette (and the entire package) is currently in a development version. If you wish to review the vignette, the `build_vignettes` argument is mandatory. # Vignettes -Once the package is installed, you can run the following command to view the first vignette : - -``` r -vignette("data-details") -``` - -Or directly read this page : `vignette("data-details")`. +Two vignettes are currently available: +- one explaining the retrieval of Telraam data and describing the data included in the package (`vignette("data-details")`) +- the second detailing the available graphical representations (`vignette("data-visualization")`). # Future Developments diff --git a/docs/404.html b/docs/404.html index 4bbf966..51bcc10 100644 --- a/docs/404.html +++ b/docs/404.html @@ -40,7 +40,7 @@
Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 381ffa1..fa461f6 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -25,7 +25,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • diff --git a/docs/articles/data-details.html b/docs/articles/data-details.html index 11e4ab1..5ae24fc 100644 --- a/docs/articles/data-details.html +++ b/docs/articles/data-details.html @@ -42,7 +42,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • diff --git a/docs/articles/data-visualization.html b/docs/articles/data-visualization.html new file mode 100644 index 0000000..43987b1 --- /dev/null +++ b/docs/articles/data-visualization.html @@ -0,0 +1,687 @@ + + + + + + + + +Visualize and analyse traffic • telraamStats + + + + + + + + + + Skip to contents + + +
    + + + + +
    +
    +

    Visualize and analyse traffic

    + + + Source: vignettes/data-visualization.Rmd +
    data-visualization.Rmd
    +
    + + + +
    +

    Introduction +

    +

    This vignette illustrates the potential visualizations (and the +ensuing analysis) of traffic data from Telraam sensors provided by the +package.

    +

    Before we go on, we’ll attach the packages we use.

    + +

    We will also load the data: here, we will use the dataset attached to +the package, but you can certainly replicate these visualizations and +analyses using data from your own sensors. For more information on data +retrieval, you can refer to the dedicated vignette +(vignette("data-details")).

    +

    Included dataset is lazily loaded with the telraamStats package. If +you have already called library(telraamStats), the dataset +is already attached and you can call it directly :

    +
    +head(traffic)
    + ++++++++++++++++++++++++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    instance_idsegment_fullnamesegment_iddateintervaluptimeheavycarbikepedestrianheavy_lftheavy_rgtcar_lftcar_rgtbike_lftbike_rgtpedestrian_lftpedestrian_rgtdirectioncar_speed_hist_0to70pluscar_speed_hist_0to120plustimezonev85dayhourweekdayholidayvacationsegment_nameuptime_quality
    -19000001844 - RteVitre-0690000018442022-01-01 01:00:00 CEThourly000000000000010, 0, 0, 0, 0, 0, 0, 00, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0Europe/ParisNA2022-01-011saturdayTRUEVacances de NoëlRteVitre-06FALSE
    -19000001844 - RteVitre-0690000018442022-01-01 02:00:00 CEThourly000000000000010, 0, 0, 0, 0, 0, 0, 00, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0Europe/ParisNA2022-01-012saturdayTRUEVacances de NoëlRteVitre-06FALSE
    -19000001844 - RteVitre-0690000018442022-01-01 03:00:00 CEThourly000000000000010, 0, 0, 0, 0, 0, 0, 00, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0Europe/ParisNA2022-01-013saturdayTRUEVacances de NoëlRteVitre-06FALSE
    -19000001844 - RteVitre-0690000018442022-01-01 04:00:00 CEThourly000000000000010, 0, 0, 0, 0, 0, 0, 00, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0Europe/ParisNA2022-01-014saturdayTRUEVacances de NoëlRteVitre-06FALSE
    -19000001844 - RteVitre-0690000018442022-01-01 05:00:00 CEThourly000000000000010, 0, 0, 0, 0, 0, 0, 00, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0Europe/ParisNA2022-01-015saturdayTRUEVacances de NoëlRteVitre-06FALSE
    -19000001844 - RteVitre-0690000018442022-01-01 06:00:00 CEThourly000000000000010, 0, 0, 0, 0, 0, 0, 00, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0Europe/ParisNA2022-01-016saturdayTRUEVacances de NoëlRteVitre-06FALSE
    +
    +
    +

    Period availability for your segments +

    +

    Road segment-specific sensors do not operate continuously; they are +limited by daylight duration and may also face other external issues +(being unplugged, falling, software problems, etc.). The data collected +during a period may not be continuous and could have interruptions +ranging from an hour to several days.

    +

    To visualize traffic, it is important to be aware of the operating +periods of the sensors being analyzed, as well as the quality of their +data. The data reliability is provided through the uptime indicator. +Uptime, ranging from 0 to 1, corresponds to the percentage of time +during which the camera is actively counting passages. Following +Telraam’s recommendations, here an uptime lower than 0.5 is considered +associated with poor-quality data.

    +

    The graphical representation below, obtained using the +gg_availability() function, provides an overview of data +availability and quality for different sensors over the entire period. +This indicator is calculated over daytime hours (between 5 a.m. and 8 +p.m.) when the Telraam sensor is operational. It represents the average +uptime during these hours. It illustrates the daily average uptime +evolution: the higher this value, the better the data quality for that +sensor on the respective day. Its purpose is to assist you in selecting +study periods.

    +
    +plot(
+  gg_availability(traffic)
+)
    +

    +

    On this graph, for example, we can observe through black time slots +that the camera on the segment ‘ParisArcEnCiel-05’ did not report any +data at the end of June 2022 and at the end of the year 2022. We can +also notice that the average uptime is higher during the summer period. +This illustrates the fact that the duration of daylight, and therefore +the possible filming period, is longer in summer.

    +

    All the graphs generated from the functions presented below +are based on data filtered according to this uptime +parameter: only hours with an uptime > 0.5 are retained. +This filter is not editable at the moment and ensures that the data +considered have a sufficient level of reliability.

    +
    +
    +

    Traffic +

    +
    +

    Traffic evolution +

    +

    Once you have identified the specific period you want to analyze, you +may want to observe the evolution of traffic trends over time. +gg_traffic_evolution() enables the representation of +traffic for a particular period, aggregated for all or some segments, +for a specific direction, or aggregated for both, and aggregated for all +transportation modes or for selected ones.

    +

    Without specifying any options, you can view the traffic evolution of +cars and heavy vehicles for all segments over the entire period. By +default, a smoothing curve (based on a GAM model with cubic spline) is +used to represent the trend.

    +
    +plot(
+  gg_traffic_evolution(traffic)
+)
    +

    +

    You can specify a date range, one or more segments, different +transportation modes or directions and choose whether you want a +smoothing curve or not. By default, the aggregation is done by day, but +you can obtain more precise information by setting the +agg_day parameter to FALSE to aggregate by +hour.

    +

    Below is an example of all these possible filters and parameters +:

    +
    +plot(
+  gg_traffic_evolution(traffic,
+                       date_range = c('2022-01-01','2022-03-01'),
+                       segment = 'RteVitre-06',
+                       mode = c('car','pedestrian'),
+                       direction = 'lft',
+                       smoothed = FALSE,
+                       agg_day = FALSE)
+)
    +

    +
    +
    +

    Average traffic +

    +
    +

    Average traffic per weekday +

    +

    The traffic varies according to the days of the week, which can serve +as an analytical window to understand territorial mobility. The +gg_traffic_avg() function allows to examine the average +behavior per day of the week. It can be used globally or more +specifically (over a specific period, on a segment or set of segments, +for a specific direction, for a particular transportation mode).

    +

    By default, this function provides the average traffic per day of the +week :

    +
    +plot(
+  gg_traffic_avg(traffic)
+)
    +

    +

    Here, for instance, one can observe several different traffic +dynamics, with weekdays exhibiting similar patterns and weekends +displaying different ones (such as a shifted morning peak and smoother +traffic throughout the day).

    +

    As before, it’s possible to select a specific period, a specific +segment, a mode of transportation, a direction, or a day of the week +according to the needs of your study.

    +
    +plot(
+  gg_traffic_avg(traffic,
+                 date_range = c('2022-07-01','2022-09-01'),
+                 segment = 'RteVitre-06',
+                 mode = 'car',
+                 direction = 'rgt',
+                 weekday = c('monday','friday')
+                 )
+)
    +

    +

    This representation, for instance, allows us to see that the traffic +in one direction on this segment is much heavier in the evening than in +the morning, and even more so on Fridays compared to Mondays.

    +
    +
    +

    Average traffic per segment +

    +

    The same function gg_traffic_avg() can be used to +calculate the average traffic per segment and compare segments with each +other. To do this, simply use the parameter 'aggregated_by' +and replace the default value ('weekday') with +'segment_name', as shown in the example below. The function +will then plot the average traffic according to the time of day, but +this time by road segment.

    +
    +plot(
+  gg_traffic_avg(traffic, 
+                 aggregated_by = "segment_name")
+)
    +

    +

    This representation allows us to realize differences in traffic, both +in terms of volumes and dynamics.

    +

    Similarly to before, it is possible to specify a period, a direction +of travel, a mode of transportation, or here a day of the week.

    +
    +plot(
+  gg_traffic_avg(traffic, 
+                 aggregated_by = 'segment_name',
+                 weekday = 'sunday',
+                 mode = 'car',
+                 direction = 'rgt')
+)
    +

    +
    +
    +

    Average traffic per direction +

    +

    This function gg_traffic_avg() can also be used to +compare directions of travel, using the parameter +aggregated_by = "direction" as shown in the example below. +This can be particularly useful within the same road segment to observe +commuter traffic. As before, it is still possible to filter by modes of +transportation, days of the week, or segments.

    +
    +plot(
+  gg_traffic_avg(traffic, 
+                 aggregated_by = 'direction',
+                 weekday = 'monday',
+                 mode = 'car',
+                 segments = 'RteVitre-06'
+                 )
+)
    +

    +

    This example is typical of a route with significant commuter traffic, +with heavy traffic in one direction in the morning and in the other +direction in the evening.

    +
    +
    +

    Average traffic per mode +

    +

    Finally, the function gg_traffic_avg() allows for +comparing modes of transportation with each other, particularly to +observe differences in volumes or dynamics, using the parameter +aggregated_by = "direction" as shown in the example below. +As before, it’s possible to filter by directions, days of the week, +segments, hours, and of course, to select the modes of +transportation

    +
    +plot(
+  gg_traffic_avg(traffic, 
+                 aggregated_by = 'mode',
+                 mode = c('heavy','car'),
+                 segment = 'RteVitre-06')
+)
    +

    +
    +
    +
    +
    +

    Car speed +

    +

    Telraam sensors offer speed estimation for cars on two levels :

    +
      +
    • the estimated car speed distribution (ranging from 0 to 120+ km/h), +in 10 or 5 km/h bins ;
      • +
    • +v85, the estimated car speed limit in km/h that 85% +of all cars respect. 15% of drivers drive faster than this +v85 indicator.
      • +
    +

    Telraam specifies that all these speed-related measurements have +biases: their accuracy is likely not better than +/- 10%. Moreover, +these measurements pertain exclusively to cars and do not include other +modes of transportation.

    +
    +

    Car speed distribution +

    +

    The car’s speed distribution can be plotted globally or with filters +(specific period, weekdays or segments) with the function +gg_car_speed_histogram(). Refer to documentation for more +information about filters.

    + +

    +

    This function gg_car_speed_histogram() enables you to +compare speed histograms for different segments or weekdays. Below is an +example with a distinction by road segment.

    +
    +plot(
+  gg_car_speed_histogram(traffic, 
+                         aggregated_by = 'segment_name')
+)
    +

    +

    In this example, we can observe the difference between the two +segments: on the first segment (ParisArcEnCiel-05), the +speed distribution is more spread out and is characterized by a peak at +very low speeds. The second segment (RteVitre-06) has a +peak distribution in the 35-40 km/h class, with values more +concentrated around this range. During this period, 25% of cars were +driving between 35 and 40 km/h on this segment.

    +

    Here is an example by weekday with all the available filters.

    +
    +plot(
+  gg_car_speed_histogram(traffic, 
+                         weekday = c('monday','sunday'),
+                         segments = 'RteVitre-06',
+                         hours = 17:20,
+                         aggregated_by = "weekday")
+)
    +

    +

    While the shift is less significant than when distinguishing between +sensors, one can still notice a difference in speed between weekdays, +likely attributable to the traffic dynamics differences observed +earlier.

    +
    +
    +

    v85 +

    +

    v85 is the estimated car speed limit in km/h that +85% of all cars respect. 15% of drivers drive faster than +v85.

    +

    The function gg_car_speed_v85() allows plotting the +evolution of the average of this indicator according to the time of day. +This can be particularly interesting to link with traffic, to observe +possible congestion phenomena

    +
    +plot(
+  gg_car_speed_v85(traffic)
+)
    +

    +

    As before, it is possible to view this indicator by segment +(aggregated_by = 'segment_name') or by day of the week +(aggregated_by = 'weekday'). Here’s an example by +segment.

    +
    +plot(
+  gg_car_speed_v85(traffic,
+                   aggregated_by = 'segment_name')
+)
    +

    +

    Finally, it is still possible to filter by a specific period or a +particular segment.

    +
    +plot(
+  gg_car_speed_v85(traffic,
+                   aggregated_by = 'weekday',
+                   date_range = c('2022-01-01','2022-03-01'),
+                   segments = 'RteVitre-06')
+)
    +

    +
    +
    +
    +
    + + + +
    +

    +

    Developed by Lilou Augeray, Ulysse Caromel, Ketsia Guichard, Pascal Irz, Mijin Park, Tanguy Richard, Agis-ta-terre.

    +
    + +
    +

    +

    Site built with pkgdown 2.0.7.

    +
    + +
    +
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • @@ -56,7 +56,7 @@

    All vignettes

    Gathering data

    Learn how to get traffic data from a Telraam camera.

    -
    Visualize and analyse traffic
    +
    Visualize and analyse traffic

    Learn how to visualize and analyse traffic data from Telraam cameras.

    diff --git a/docs/authors.html b/docs/authors.html index 0e5f469..bda78d4 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -25,7 +25,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • diff --git a/docs/index.html b/docs/index.html index 108ca89..4d7423d 100644 --- a/docs/index.html +++ b/docs/index.html @@ -46,7 +46,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • @@ -77,8 +77,7 @@

    The aim of this package is to grant the user tools for data visualisation and data analysis of mobility data for Telraam sensors.

    An example of using this package can be seen in this application, although it also utilizes more advanced representations.

    -

    Link to Github repository

    -

    This package is currently in a development version, feel free to contact us for more information !

    +

    Link to Github repository

    Telraam Sensors @@ -108,23 +107,14 @@

    Licence

    Install Package

    -

    If you want to install this package from Github, you can use the devtools package:

    -if(!require(devtools)){
-    install.packages("devtools")
-}
-devtools::install_github("https://github.com/KetsiaGuichard/telraamStats",
-                              dependencies = TRUE, 
-                              build_vignettes = TRUE)
    -

    Vignette (and the entire package) is currently in a development version. If you wish to review the vignette, the build_vignettes argument is mandatory.

    +install.packages('telraamStats') +library(telraamStats)

    Vignettes

    -

    Once the package is installed, you can run the following command to view the first vignette :

    -
    -vignette("data-details")
    -

    Or directly read this page : vignette("data-details").

    +

    Two vignettes are currently available: - one explaining the retrieval of Telraam data and describing the data included in the package (vignette("data-details")) - the second detailing the available graphical representations (vignette("data-visualization")).

    Future Developments diff --git a/docs/news/index.html b/docs/news/index.html index 537b97d..13d7ef9 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -25,7 +25,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic

  • diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 6642777..9664d56 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -3,8 +3,8 @@ pkgdown: 2.0.7 pkgdown_sha: ~ articles: data-details: data-details.html - data_visualization: data_visualization.html -last_built: 2024-05-13T15:00Z + data-visualization: data-visualization.html +last_built: 2024-05-22T13:26Z urls: reference: https://ketsiaguichard.github.io/telraamStats/reference article: https://ketsiaguichard.github.io/telraamStats/articles diff --git a/docs/reference/check_options_graph.html b/docs/reference/check_options_graph.html index fd99c3f..dec8ed9 100644 --- a/docs/reference/check_options_graph.html +++ b/docs/reference/check_options_graph.html @@ -25,7 +25,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • diff --git a/docs/reference/enrich_traffic.html b/docs/reference/enrich_traffic.html index 54b731e..68b58e4 100644 --- a/docs/reference/enrich_traffic.html +++ b/docs/reference/enrich_traffic.html @@ -27,7 +27,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • diff --git a/docs/reference/get_telraam_token.html b/docs/reference/get_telraam_token.html index 2f907e5..48a6873 100644 --- a/docs/reference/get_telraam_token.html +++ b/docs/reference/get_telraam_token.html @@ -25,7 +25,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • diff --git a/docs/reference/gg_availability.html b/docs/reference/gg_availability.html index 78034cf..f209a9f 100644 --- a/docs/reference/gg_availability.html +++ b/docs/reference/gg_availability.html @@ -27,7 +27,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • diff --git a/docs/reference/import_sensor.html b/docs/reference/import_sensor.html index acdaf9f..e3b49a5 100644 --- a/docs/reference/import_sensor.html +++ b/docs/reference/import_sensor.html @@ -27,7 +27,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • diff --git a/docs/reference/is_vacation.html b/docs/reference/is_vacation.html index 3d68bbb..ea521ce 100644 --- a/docs/reference/is_vacation.html +++ b/docs/reference/is_vacation.html @@ -25,7 +25,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • diff --git a/docs/reference/set_global_vars.html b/docs/reference/set_global_vars.html index 524b657..afad28c 100644 --- a/docs/reference/set_global_vars.html +++ b/docs/reference/set_global_vars.html @@ -29,7 +29,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • diff --git a/docs/reference/set_telraam_token.html b/docs/reference/set_telraam_token.html index c4a5b5e..cb05f49 100644 --- a/docs/reference/set_telraam_token.html +++ b/docs/reference/set_telraam_token.html @@ -25,7 +25,7 @@
    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
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    Gathering data - Visualize and analyse traffic + Visualize and analyse traffic
  • diff --git a/docs/search.json b/docs/search.json new file mode 100644 index 0000000..d57a6f6 --- /dev/null +++ b/docs/search.json @@ -0,0 +1 @@ +[{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-details.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Gathering data","text":"vignette describes traffic data Telraam sensors get access specific data. go , ’ll attach packages use.","code":"library(telraamStats)"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-details.html","id":"telraam","dir":"Articles","previous_headings":"Introduction","what":"Telraam","title":"Gathering data","text":"Telraam Belgian company develops sensors monitoring collecting multi-modal traffic data. sensors equipped cameras count road users using various mode transport, including pedestrian, cyclists, cars heavy vehicles. diverse range stakeholders (individuals, community groups, local authoritieds, researchers…) can purchase install Telraam sensor buildings. map sensors available Telraam website. Everyone can access data web interface (example) API.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-details.html","id":"available-data","dir":"Articles","previous_headings":"Introduction > Telraam","what":"Available data","title":"Gathering data","text":"Telraam API provides information road segments sensors locations, parameters, importantly, offers traffic data. Every hour, sensor sends information traffic counts, transportation mode road direction, along confidence metric. addition traffic counts, estimated car speed car speed histogram also available. details data provided vignette.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-details.html","id":"included-dataset","dir":"Articles","previous_headings":"Introduction","what":"Included dataset","title":"Gathering data","text":"’re interested discovering Telraam data playing real traffic data, dataset two road segments city Châteaubourg (FR) included package. Châteaubourg municipality Brittany, northwestern France. population around 7,500 inhabitants area less 30km\\(^2\\). France, city largest number Telraam sensors 2023 far highest density sensors. two road segments offer hourly data January 1, 2022, December 31, 2022. data directly extracted API may contain missing extreme values.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-details.html","id":"load-included-dataset","dir":"Articles","previous_headings":"Introduction > Included dataset","what":"Load included dataset","title":"Gathering data","text":"Included dataset lazily loaded telraamStats package. already called library(telraamStats), dataset already attached can call directly : Otherwise, don’t want load entire package, can also access dataset using telraamStats::traffic.","code":"head(traffic)"},{"path":[]},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-details.html","id":"obtain-a-telraam-token","dir":"Articles","previous_headings":"Introduction > Retrieve data from a specific road segment or set of segments","what":"1. Obtain a Telraam token","title":"Gathering data","text":"work data one sensor - -, must first create Telraam (free) account obtain API token. API token serves unique identifier accessing Telraam API service, used package. can generate personal dashboard Telraam website. information Telraam API, refer Telraam Q&website.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-details.html","id":"specify-your-token-in-configuration","dir":"Articles","previous_headings":"Introduction > Retrieve data from a specific road segment or set of segments","what":"2. Specify your token in configuration","title":"Gathering data","text":"strongly recommended directly embed token code, especially code shared versioned using system like Git. two options specify Telraam token : Edit .Renviron file. .Renviron useful defining sensitive information R environment variables: contains list environment variables set. easy way edit file add Telraam token using usethis package. Run following command (either every time change API Token) : .Renviron file open RStudio can add new line form token='YourTelraamAPIToken'. comfortable previous step, can use built-function telraamStats package. However, keep mind need run function every time want retrieve data: want verify token correctly set, can use following command :","code":"if (!require(\"usethis\")) install.packages(\"usethis\") #if you haven't installed 'usethis' already usethis::edit_r_environ() set_telraam_token(token = 'YourTelraamAPIToken') get_telraam_token()"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-details.html","id":"specify-road-segment-ids-in-configuration-file","dir":"Articles","previous_headings":"Introduction > Retrieve data from a specific road segment or set of segments","what":"3. Specify road segment IDs in configuration file","title":"Gathering data","text":"road segment unique ID, can found Telraam location URL. instance, URL one Châteaubourg’s segments https://telraam.net/home/location/9000005665, ID located last part URL (9000005665). gathered IDs segments, can specify editing inst\\config.yml file. file can created using create_config() function. executed, find config.yml file inst\\ folder work directory. file already contains following template, can modify (directly yml file) add segments, adhering format template, , segment name (without quotes), followed colon, segment number within quotes : segmentName: '9000000000'. can specify many segments want personalize segment’s names.","code":"create_config() default: url: https://telraam-api.net/v1 segments: segment-01: '9000000000' segment-02: '9000000000' default: url: https://telraam-api.net/v1 segments: MyFirstSegment: '9000000000' My-second-segment: '9000000000' My_3rd_segment: '9000000000'"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-details.html","id":"get-data-for-your-road-segments","dir":"Articles","previous_headings":"Introduction > Retrieve data from a specific road segment or set of segments","what":"4. Get data for your road segments","title":"Gathering data","text":"specified names IDs road segments, can use retrieve_sensor() function gather data segment Telraam API. function takes, arguments, name segment, first last day period interest. must use names specified configuration files dates must date type. want store data, can directly use write_update_data(), performs task also writes updates sensor data data/ directory .Rdata format. arguments previous function: retrieve data already stored, can use import_sensor(). function imports data associated given list sensor names .RData files contained data/ directory. , also use names specified configuration files. available periods imported.","code":"retrieve_sensor(segment_name = \"segment-01\", start_date = as.Date('2023-09-01'), end_date = as.Date('2023-09-30')) write_update_data(segment_name = \"segment-01\", start_date = as.Date('2023-09-01'), end_date = as.Date('2023-09-30')) write_update_data(\"segment-02\", as.Date('2023-08-01'), as.Date('2023-09-30')) import_sensor(list_sensor = c(\"segment-01\",\"segment-02\"))"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-details.html","id":"data-details","dir":"Articles","previous_headings":"Introduction","what":"Data details","title":"Gathering data","text":"importing data road segments (following previous steps) using included dataset, obtain Telraam data columns. Among columns, important ones : segment_id : Telraam ID road segment ; date : date UTC time reporting interval (beginning interval) ; interval : can “hourly” “daily” hourly daily aggregate data ; uptime : 0 1, represents portion reporting interval actively spent counting traffic. information uptime parameter, can refer Telraam article ; heavy, heavy_lft, heavy_rgt : number heavy vehicles, total directions. also columns transportation mode (car, bike, pedestrian) ; car_speed_hist_0to70plus, car_speed_hist_0to120plus : estimated car speed distribution 10 km/h bins 0 70+ km/h 120+ km/h (percentage) ; v85 : estimated car speed limit km/h 85% cars respect. Complete data description can found data documentation package, can refer Telraam API website details.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-visualization.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Visualize and analyse traffic","text":"vignette illustrates potential visualizations (ensuing analysis) traffic data Telraam sensors provided package. go , ’ll attach packages use. also load data: , use dataset attached package, can certainly replicate visualizations analyses using data sensors. information data retrieval, can refer dedicated vignette (vignette(\"data-details\")). Included dataset lazily loaded telraamStats package. already called library(telraamStats), dataset already attached can call directly :","code":"library(telraamStats) head(traffic)"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-visualization.html","id":"period-availability-for-your-segments","dir":"Articles","previous_headings":"","what":"Period availability for your segments","title":"Visualize and analyse traffic","text":"Road segment-specific sensors operate continuously; limited daylight duration may also face external issues (unplugged, falling, software problems, etc.). data collected period may continuous interruptions ranging hour several days. visualize traffic, important aware operating periods sensors analyzed, well quality data. data reliability provided uptime indicator. Uptime, ranging 0 1, corresponds percentage time camera actively counting passages. Following Telraam’s recommendations, uptime lower 0.5 considered associated poor-quality data. graphical representation , obtained using gg_availability() function, provides overview data availability quality different sensors entire period. indicator calculated daytime hours (5 .m. 8 p.m.) Telraam sensor operational. represents average uptime hours. illustrates daily average uptime evolution: higher value, better data quality sensor respective day. purpose assist selecting study periods. graph, example, can observe black time slots camera segment ‘ParisArcEnCiel-05’ report data end June 2022 end year 2022. can also notice average uptime higher summer period. illustrates fact duration daylight, therefore possible filming period, longer summer. graphs generated functions presented based data filtered according uptime parameter: hours uptime > 0.5 retained. filter editable moment ensures data considered sufficient level reliability.","code":"plot( gg_availability(traffic) )"},{"path":[]},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-visualization.html","id":"traffic-evolution","dir":"Articles","previous_headings":"Traffic","what":"Traffic evolution","title":"Visualize and analyse traffic","text":"identified specific period want analyze, may want observe evolution traffic trends time. gg_traffic_evolution() enables representation traffic particular period, aggregated segments, specific direction, aggregated , aggregated transportation modes selected ones. Without specifying options, can view traffic evolution cars heavy vehicles segments entire period. default, smoothing curve (based GAM model cubic spline) used represent trend. can specify date range, one segments, different transportation modes directions choose whether want smoothing curve . default, aggregation done day, can obtain precise information setting agg_day parameter FALSE aggregate hour. example possible filters parameters :","code":"plot( gg_traffic_evolution(traffic) ) plot( gg_traffic_evolution(traffic, date_range = c('2022-01-01','2022-03-01'), segment = 'RteVitre-06', mode = c('car','pedestrian'), direction = 'lft', smoothed = FALSE, agg_day = FALSE) )"},{"path":[]},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-visualization.html","id":"average-traffic-per-weekday","dir":"Articles","previous_headings":"Traffic > Average traffic","what":"Average traffic per weekday","title":"Visualize and analyse traffic","text":"traffic varies according days week, can serve analytical window understand territorial mobility. gg_traffic_avg() function allows examine average behavior per day week. can used globally specifically (specific period, segment set segments, specific direction, particular transportation mode). default, function provides average traffic per day week : , instance, one can observe several different traffic dynamics, weekdays exhibiting similar patterns weekends displaying different ones (shifted morning peak smoother traffic throughout day). , ’s possible select specific period, specific segment, mode transportation, direction, day week according needs study. representation, instance, allows us see traffic one direction segment much heavier evening morning, even Fridays compared Mondays.","code":"plot( gg_traffic_avg(traffic) ) plot( gg_traffic_avg(traffic, date_range = c('2022-07-01','2022-09-01'), segment = 'RteVitre-06', mode = 'car', direction = 'rgt', weekday = c('monday','friday') ) )"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-visualization.html","id":"average-traffic-per-segment","dir":"Articles","previous_headings":"Traffic > Average traffic","what":"Average traffic per segment","title":"Visualize and analyse traffic","text":"function gg_traffic_avg() can used calculate average traffic per segment compare segments . , simply use parameter 'aggregated_by' replace default value ('weekday') 'segment_name', shown example . function plot average traffic according time day, time road segment. representation allows us realize differences traffic, terms volumes dynamics. Similarly , possible specify period, direction travel, mode transportation, day week.","code":"plot( gg_traffic_avg(traffic, aggregated_by = \"segment_name\") ) plot( gg_traffic_avg(traffic, aggregated_by = 'segment_name', weekday = 'sunday', mode = 'car', direction = 'rgt') )"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-visualization.html","id":"average-traffic-per-direction","dir":"Articles","previous_headings":"Traffic > Average traffic","what":"Average traffic per direction","title":"Visualize and analyse traffic","text":"function gg_traffic_avg() can also used compare directions travel, using parameter aggregated_by = \"direction\" shown example . can particularly useful within road segment observe commuter traffic. , still possible filter modes transportation, days week, segments. example typical route significant commuter traffic, heavy traffic one direction morning direction evening.","code":"plot( gg_traffic_avg(traffic, aggregated_by = 'direction', weekday = 'monday', mode = 'car', segments = 'RteVitre-06' ) )"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-visualization.html","id":"average-traffic-per-mode","dir":"Articles","previous_headings":"Traffic > Average traffic","what":"Average traffic per mode","title":"Visualize and analyse traffic","text":"Finally, function gg_traffic_avg() allows comparing modes transportation , particularly observe differences volumes dynamics, using parameter aggregated_by = \"direction\" shown example . , ’s possible filter directions, days week, segments, hours, course, select modes transportation","code":"plot( gg_traffic_avg(traffic, aggregated_by = 'mode', mode = c('heavy','car'), segment = 'RteVitre-06') )"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-visualization.html","id":"car-speed","dir":"Articles","previous_headings":"","what":"Car speed","title":"Visualize and analyse traffic","text":"Telraam sensors offer speed estimation cars two levels : estimated car speed distribution (ranging 0 120+ km/h), 10 5 km/h bins ; v85, estimated car speed limit km/h 85% cars respect. 15% drivers drive faster v85 indicator. Telraam specifies speed-related measurements biases: accuracy likely better +/- 10%. Moreover, measurements pertain exclusively cars include modes transportation.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-visualization.html","id":"car-speed-distribution","dir":"Articles","previous_headings":"Car speed","what":"Car speed distribution","title":"Visualize and analyse traffic","text":"car’s speed distribution can plotted globally filters (specific period, weekdays segments) function gg_car_speed_histogram(). Refer documentation information filters. function gg_car_speed_histogram() enables compare speed histograms different segments weekdays. example distinction road segment. example, can observe difference two segments: first segment (ParisArcEnCiel-05), speed distribution spread characterized peak low speeds. second segment (RteVitre-06) peak distribution 35-40 km/h class, values concentrated around range. period, 25% cars driving 35 40 km/h segment. example weekday available filters. shift less significant distinguishing sensors, one can still notice difference speed weekdays, likely attributable traffic dynamics differences observed earlier.","code":"plot( gg_car_speed_histogram(traffic) ) plot( gg_car_speed_histogram(traffic, aggregated_by = 'segment_name') ) plot( gg_car_speed_histogram(traffic, weekday = c('monday','sunday'), segments = 'RteVitre-06', hours = 17:20, aggregated_by = \"weekday\") )"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data-visualization.html","id":"v85","dir":"Articles","previous_headings":"Car speed","what":"v85","title":"Visualize and analyse traffic","text":"v85 estimated car speed limit km/h 85% cars respect. 15% drivers drive faster v85. function gg_car_speed_v85() allows plotting evolution average indicator according time day. can particularly interesting link traffic, observe possible congestion phenomena , possible view indicator segment (aggregated_by = 'segment_name') day week (aggregated_by = 'weekday'). ’s example segment. Finally, still possible filter specific period particular segment.","code":"plot( gg_car_speed_v85(traffic) ) plot( gg_car_speed_v85(traffic, aggregated_by = 'segment_name') ) plot( gg_car_speed_v85(traffic, aggregated_by = 'weekday', date_range = c('2022-01-01','2022-03-01'), segments = 'RteVitre-06') )"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data_visualization.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Visualize and analyse traffic","text":"vignette illustrates potential visualizations (ensuing analysis) traffic data Telraam sensors provided package. go , ’ll attach packages use. also load data: , use dataset attached package, can certainly replicate visualizations analyses using data sensors. information data retrieval, can refer dedicated vignette (vignette(\"data-details\")). Included dataset lazily loaded telraamStats package. already called library(telraamStats), dataset already attached can call directly :","code":"library(telraamStats) head(traffic)"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data_visualization.html","id":"period-availability-for-your-segments","dir":"Articles","previous_headings":"","what":"Period availability for your segments","title":"Visualize and analyse traffic","text":"Road segment-specific sensors operate continuously; limited daylight duration may also face external issues (unplugged, falling, software problems, etc.). data collected period may continuous interruptions ranging hour several days. visualize traffic, important aware operating periods sensors analyzed, well quality data. data reliability provided uptime indicator. Uptime, ranging 0 1, corresponds percentage time camera actively counting passages. Following Telraam’s recommendations, uptime lower 0.5 considered associated poor-quality data. graphical representation , obtained using gg_availability() function, provides overview data availability quality different sensors entire period. indicator calculated daytime hours (5 .m. 8 p.m.) Telraam sensor operational. represents average uptime hours. illustrates daily average uptime evolution: higher value, better data quality sensor respective day. purpose assist selecting study periods. graph, example, can observe black time slots camera segment ‘ParisArcEnCiel-05’ report data end June 2022 end year 2022. can also notice average uptime higher summer period. illustrates fact duration daylight, therefore possible filming period, longer summer. graphs generated functions presented based data filtered according uptime parameter: hours uptime > 0.5 retained. filter editable moment ensures data considered sufficient level reliability.","code":"plot( gg_availability(traffic) )"},{"path":[]},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data_visualization.html","id":"traffic-evolution","dir":"Articles","previous_headings":"Traffic","what":"Traffic evolution","title":"Visualize and analyse traffic","text":"identified specific period want analyze, may want observe evolution traffic trends time. gg_traffic_evolution() enables representation traffic particular period, aggregated segments, specific direction, aggregated , aggregated transportation modes selected ones. Without specifying options, can view traffic evolution cars heavy vehicles segments entire period. default, smoothing curve (based GAM model cubic spline) used represent trend. can specify date range, one segments, different transportation modes directions choose whether want smoothing curve . default, aggregation done day, can obtain precise information setting agg_day parameter FALSE aggregate hour. example possible filters parameters :","code":"plot( gg_traffic_evolution(traffic) ) plot( gg_traffic_evolution(traffic, date_range = c('2022-01-01','2022-03-01'), segment = 'RteVitre-06', mode = c('car','pedestrian'), direction = 'lft', smoothed = FALSE, agg_day = FALSE) )"},{"path":[]},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data_visualization.html","id":"average-traffic-per-weekday","dir":"Articles","previous_headings":"Traffic > Average traffic","what":"Average traffic per weekday","title":"Visualize and analyse traffic","text":"traffic varies according days week, can serve analytical window understand territorial mobility. gg_traffic_avg() function allows examine average behavior per day week. can used globally specifically (specific period, segment set segments, specific direction, particular transportation mode). default, function provides average traffic per day week : , instance, one can observe several different traffic dynamics, weekdays exhibiting similar patterns weekends displaying different ones (shifted morning peak smoother traffic throughout day). , ’s possible select specific period, specific segment, mode transportation, direction, day week according needs study. representation, instance, allows us see traffic one direction segment much heavier evening morning, even Fridays compared Mondays.","code":"plot( gg_traffic_avg(traffic) ) plot( gg_traffic_avg(traffic, date_range = c('2022-07-01','2022-09-01'), segment = 'RteVitre-06', mode = 'car', direction = 'rgt', weekday = c('monday','friday') ) )"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data_visualization.html","id":"average-traffic-per-segment","dir":"Articles","previous_headings":"Traffic > Average traffic","what":"Average traffic per segment","title":"Visualize and analyse traffic","text":"function gg_traffic_avg() can used calculate average traffic per segment compare segments . , simply use parameter 'aggregated_by' replace default value ('weekday') 'segment_name', shown example . function plot average traffic according time day, time road segment. representation allows us realize differences traffic, terms volumes dynamics. Similarly , possible specify period, direction travel, mode transportation, day week.","code":"plot( gg_traffic_avg(traffic, aggregated_by = \"segment_name\") ) plot( gg_traffic_avg(traffic, aggregated_by = 'segment_name', weekday = 'sunday', mode = 'car', direction = 'rgt') )"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data_visualization.html","id":"average-traffic-per-direction","dir":"Articles","previous_headings":"Traffic > Average traffic","what":"Average traffic per direction","title":"Visualize and analyse traffic","text":"function gg_traffic_avg() can also used compare directions travel, using parameter aggregated_by = \"direction\" shown example . can particularly useful within road segment observe commuter traffic. , still possible filter modes transportation, days week, segments. example typical route significant commuter traffic, heavy traffic one direction morning direction evening.","code":"plot( gg_traffic_avg(traffic, aggregated_by = 'direction', weekday = 'monday', mode = 'car', segments = 'RteVitre-06' ) )"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data_visualization.html","id":"average-traffic-per-mode","dir":"Articles","previous_headings":"Traffic > Average traffic","what":"Average traffic per mode","title":"Visualize and analyse traffic","text":"Finally, function gg_traffic_avg() allows comparing modes transportation , particularly observe differences volumes dynamics, using parameter aggregated_by = \"direction\" shown example . , ’s possible filter directions, days week, segments, hours, course, select modes transportation","code":"plot( gg_traffic_avg(traffic, aggregated_by = 'mode', mode = c('heavy','car'), segment = 'RteVitre-06') )"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data_visualization.html","id":"car-speed","dir":"Articles","previous_headings":"","what":"Car speed","title":"Visualize and analyse traffic","text":"Telraam sensors offer speed estimation cars two levels : estimated car speed distribution (ranging 0 120+ km/h), 10 5 km/h bins ; v85, estimated car speed limit km/h 85% cars respect. 15% drivers drive faster v85 indicator. Telraam specifies speed-related measurements biases: accuracy likely better +/- 10%. Moreover, measurements pertain exclusively cars include modes transportation.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data_visualization.html","id":"car-speed-distribution","dir":"Articles","previous_headings":"Car speed","what":"Car speed distribution","title":"Visualize and analyse traffic","text":"car’s speed distribution can plotted globally filters (specific period, weekdays segments) function gg_car_speed_histogram(). Refer documentation information filters. function gg_car_speed_histogram() enables compare speed histograms different segments weekdays. example distinction road segment. example, can observe difference two segments: first segment (ParisArcEnCiel-05), speed distribution spread characterized peak low speeds. second segment (RteVitre-06) peak distribution 35-40 km/h class, values concentrated around range. period, 25% cars driving 35 40 km/h segment. example weekday available filters. shift less significant distinguishing sensors, one can still notice difference speed weekdays, likely attributable traffic dynamics differences observed earlier.","code":"plot( gg_car_speed_histogram(traffic) ) plot( gg_car_speed_histogram(traffic, aggregated_by = 'segment_name') ) plot( gg_car_speed_histogram(traffic, weekday = c('monday','sunday'), segments = 'RteVitre-06', hours = 17:20, aggregated_by = \"weekday\") )"},{"path":"https://ketsiaguichard.github.io/telraamStats/articles/data_visualization.html","id":"v85","dir":"Articles","previous_headings":"Car speed","what":"v85","title":"Visualize and analyse traffic","text":"v85 estimated car speed limit km/h 85% cars respect. 15% drivers drive faster v85. function gg_car_speed_v85() allows plotting evolution average indicator according time day. can particularly interesting link traffic, observe possible congestion phenomena , possible view indicator segment (aggregated_by = 'segment_name') day week (aggregated_by = 'weekday'). ’s example segment. Finally, still possible filter specific period particular segment.","code":"plot( gg_car_speed_v85(traffic) ) plot( gg_car_speed_v85(traffic, aggregated_by = 'segment_name') ) plot( gg_car_speed_v85(traffic, aggregated_by = 'weekday', date_range = c('2022-01-01','2022-03-01'), segments = 'RteVitre-06') )"},{"path":"https://ketsiaguichard.github.io/telraamStats/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Lilou Augeray. Author. Ulysse Caromel. Author. Ketsia Guichard. Maintainer. Pascal Irz. Author. Mijin Park. Author. Tanguy Richard. Author. Agis-ta-terre. Copyright holder, funder.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Augeray L, Caromel U, Irz P, Park M, Richard T (2024). telraamStats: Retrieval Visualization Mobility Data 'Telraam' Sensors. R package version 1.1.0, https://annuaire.agistaterre.org/telraamStats/.","code":"@Manual{, title = {telraamStats: Retrieval and Visualization of Mobility Data from 'Telraam' Sensors}, author = {Lilou Augeray and Ulysse Caromel and Pascal Irz and Mijin Park and Tanguy Richard}, year = {2024}, note = {R package version 1.1.0}, url = {https://annuaire.agistaterre.org/telraamStats/}, }"},{"path":"https://ketsiaguichard.github.io/telraamStats/index.html","id":"telraamstats","dir":"","previous_headings":"","what":"Retrieval and Visualization of Mobility Data from Telraam Sensors","title":"Retrieval and Visualization of Mobility Data from Telraam Sensors","text":"aim package grant user tools data visualisation data analysis mobility data Telraam sensors. example using package can seen application, although also utilizes advanced representations. Link Github repository","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/index.html","id":"telraam-sensors","dir":"","previous_headings":"","what":"Telraam Sensors","title":"Retrieval and Visualization of Mobility Data from Telraam Sensors","text":"Telraam Belgian company offers citizen-powered solution gathering diverse traffic data using affordable device. sensor continuously monitors street activity citizen’s window, capturing data various transportation modes like cars, heavy vehicles, cyclists, pedestrians. data crucial informing traffic planning engaging local communities dialogue authorities. sensors employs advanced AI proprietary algorithms detect, classify count road users, providing anonymous, aggregate data 15-minute resolution. device operates autonomously installed upper-floor window unobstructed view street. Data collected Telraam devices can shared Open Data. information view map currently available sensors, can visit Telraam website.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/index.html","id":"specific-terms","dir":"","previous_headings":"","what":"Specific Terms","title":"Retrieval and Visualization of Mobility Data from Telraam Sensors","text":"Telraam data includes specific terms essential use package : segment abbreviated term road segment. road segment part road, defined set geographical coordinate pairs Telraam id. segments defined typically two corners street, longer street likely consist multiple road segments ; sensor device measures traffic specific segment. Multiple sensors can associated road segment, unique sensor linked multiple segments. Sensors also defined configuration, primarily version (either V1 S2, S2 advanced version Telraam devices) ; uptime: Telraam V1 sensors don’t count trafic 100% time. portion time used calculations preprocessing. uptime percentage time counter actively counting. Data provided Telraam API already corrected uptime Telraam recommends keeping eye uptime values. high uptime, typically 0.7-0.8, indicates good data. first last daylight hour day consistently lower uptimes due aforementioned reasons, uptimes day 0.5, usually indicates potential issue instance. Uptime Telraam S2 units almost always 1.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/index.html","id":"licence","dir":"","previous_headings":"","what":"Licence","title":"Retrieval and Visualization of Mobility Data from Telraam Sensors","text":"work licensed Creative Commons Attribution-ShareAlike 4.0 International License.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/index.html","id":"install-package","dir":"","previous_headings":"","what":"Install Package","title":"Retrieval and Visualization of Mobility Data from Telraam Sensors","text":"","code":"install.packages('telraamStats') library(telraamStats)"},{"path":"https://ketsiaguichard.github.io/telraamStats/index.html","id":"vignettes","dir":"","previous_headings":"","what":"Vignettes","title":"Retrieval and Visualization of Mobility Data from Telraam Sensors","text":"Two vignettes currently available: - one explaining retrieval Telraam data describing data included package (vignette(\"data-details\")) - second detailing available graphical representations (vignette(\"data-visualization\")).","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/index.html","id":"future-developments","dir":"","previous_headings":"","what":"Future Developments","title":"Retrieval and Visualization of Mobility Data from Telraam Sensors","text":"Future developments focus data quality topics: descriptive statistics visualizations data quality, well imputation methods data low update frequencies (indicating low quality).","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/api_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Check API State — api_state","title":"Check API State — api_state","text":"Return state Telraam API. Determine updates can made.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/api_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check API State — api_state","text":"","code":"api_state(key = get_telraam_token())"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/api_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check API State — api_state","text":"key api key (set set_telraam_token function)","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/api_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check API State — api_state","text":"TRUE API responds well, FALSE otherwise","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/car_speed_preprocessing.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocessing mandatory for car speed graphs functions — car_speed_preprocessing","title":"Preprocessing mandatory for car speed graphs functions — car_speed_preprocessing","text":"Preprocessing mandatory car speed graphs functions","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/car_speed_preprocessing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocessing mandatory for car speed graphs functions — car_speed_preprocessing","text":"","code":"car_speed_preprocessing( enriched_data, date_range, segments, weekday, hours, aggregated_by )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/car_speed_preprocessing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocessing mandatory for car speed graphs functions — car_speed_preprocessing","text":"enriched_data enriched data.frame containing data sensors date_range Date vector. example: c('2021-01-01','2022-01-01'). Full period NULL (default). segments Character vector. Selected road segment, NULL (default). weekday Character vector. Weekday choosen. Default week. hours Integer vector. Hours choosen, default day. aggregated_by Character. Enables comparison segments weekdays. Options : 'segment_name', 'weekday', NULL (comparison, default).","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/car_speed_preprocessing.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocessing mandatory for car speed graphs functions — car_speed_preprocessing","text":"list parameters data preprocessed.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/check_options_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if options are available in the options list, replace by a default otherwise. — check_options_graph","title":"Check if options are available in the options list, replace by a default otherwise. — check_options_graph","text":"Check options available options list, replace default otherwise.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/check_options_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if options are available in the options list, replace by a default otherwise. — check_options_graph","text":"","code":"check_options_graph(options_selected, options_available, default)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/check_options_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if options are available in the options list, replace by a default otherwise. — check_options_graph","text":"options_selected List characters. Selected options. options_available List characters. Valid options. default List characters. Default options.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/check_options_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if options are available in the options list, replace by a default otherwise. — check_options_graph","text":"Options consistent possibilities","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/check_options_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if options are available in the options list, replace by a default otherwise. — check_options_graph","text":"","code":"check_options_graph(c('car','pedestrian'), c('car','pedestrian','bike','heavy'),c('car','heavy')) #> [1] \"car\" \"pedestrian\" check_options_graph(c('coucou','salut'), c('car','pedestrian','bike','heavy'),c('car','heavy')) #> [1] \"car\" \"heavy\" check_options_graph(NULL, c('car','pedestrian','bike','heavy'),c('car','heavy')) #> [1] \"car\" \"heavy\""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/convert_string_to_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a character string into a numeric vector — convert_string_to_list","title":"Convert a character string into a numeric vector — convert_string_to_list","text":"Convert character string numeric vector","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/convert_string_to_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a character string into a numeric vector — convert_string_to_list","text":"","code":"convert_string_to_list(vector)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/convert_string_to_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a character string into a numeric vector — convert_string_to_list","text":"vector Something shape \"10,20,30\"","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/convert_string_to_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a character string into a numeric vector — convert_string_to_list","text":"Numeric vector. Something shape c(10,20,30)","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/convert_string_to_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a character string into a numeric vector — convert_string_to_list","text":"","code":"convert_string_to_list(\"10,20,30\") #> [[1]] #> [1] 10 20 30 #>"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/create_config.html","id":null,"dir":"Reference","previous_headings":"","what":"Create config file if needed. — create_config","title":"Create config file if needed. — create_config","text":"user needs specify IDs sensors, function create local configuration template file \\inst directory, edit specific information.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/create_config.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create config file if needed. — create_config","text":"","code":"create_config(overwrite = FALSE)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/create_config.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create config file if needed. — create_config","text":"overwrite file exist, overwriten?","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/create_config.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create config file if needed. — create_config","text":"Boolean: TRUE file created, FALSE overwise (config already exists example).","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/create_config.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create config file if needed. — create_config","text":"","code":"if (FALSE) #run if you want to create a inst/ directory containing config file create_config()"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/custom_colors_palette.html","id":null,"dir":"Reference","previous_headings":"","what":"Colors palettes for each option — custom_colors_palette","title":"Colors palettes for each option — custom_colors_palette","text":"Colors palettes option","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/custom_colors_palette.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Colors palettes for each option — custom_colors_palette","text":"","code":"custom_colors_palette(segments)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/custom_colors_palette.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Colors palettes for each option — custom_colors_palette","text":"segments List characters. Segments name dataframe.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/custom_colors_palette.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Colors palettes for each option — custom_colors_palette","text":"list color palettes (named vector) option","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/decompose_2_fct.html","id":null,"dir":"Reference","previous_headings":"","what":"Decompose data from two sensors — decompose_2_fct","title":"Decompose data from two sensors — decompose_2_fct","text":"Decompose data two sensors","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/decompose_2_fct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decompose data from two sensors — decompose_2_fct","text":"","code":"decompose_2_fct( data, sensor1, sensor2, hour_x, direction1, direction2, mobility, norm, vacations = NULL, public_holidays = NULL )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/decompose_2_fct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decompose data from two sensors — decompose_2_fct","text":"data Data frame containing data sensor1 segment ID sensor 1 sensor2 segment ID sensor 2 hour_x hour filter direction1 direction sensor 1 direction2 direction sensor 2 mobility character vector indicating mobility types include filter norm character indicating whether normalize results vacations vacations period filtering function public_holidays public holidays period filtering function","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/decompose_2_fct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decompose data from two sensors — decompose_2_fct","text":"list containing decomposed data, seasonal data, peaks, correlation","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_dates.html","id":null,"dir":"Reference","previous_headings":"","what":"Enrich traffic data with date informations. — enrich_dates","title":"Enrich traffic data with date informations. — enrich_dates","text":"Enrich traffic data date informations.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_dates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enrich traffic data with date informations. — enrich_dates","text":"","code":"enrich_dates(data)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_dates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enrich traffic data with date informations. — enrich_dates","text":"data Data frame containing date character column containing date + hour + timezone.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_dates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enrich traffic data with date informations. — enrich_dates","text":"dataframe 3 additionnal columns : day, weekday hour.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_dates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enrich traffic data with date informations. — enrich_dates","text":"","code":"df <- data.frame('date' = c(\"2022-02-18 08:00:00 CET\",\"2022-02-18 09:00:00 CET\"), stringsAsFactors = FALSE) enrich_dates(df) #> date day hour weekday #> 1 2022-02-18 08:00:00 CET 2022-02-18 8 friday #> 2 2022-02-18 09:00:00 CET 2022-02-18 9 friday"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Enrich traffic data with segment name — enrich_name","title":"Enrich traffic data with segment name — enrich_name","text":"segment_fullname also added : combination segment's id name. segment_id configuration file.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enrich traffic data with segment name — enrich_name","text":"","code":"enrich_name(data)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enrich traffic data with segment name — enrich_name","text":"data Data frame containing segment_id column","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enrich traffic data with segment name — enrich_name","text":"dataframe two additionnal columns : segment_name segment_fullname","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_name.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enrich traffic data with segment name — enrich_name","text":"","code":"df <- data.frame('segment_id' = c(9000002156, 9000001906)) enrich_name(df) #> This ID is unknown. Please update configuration file. #> This ID is unknown. Please update configuration file. #> segment_fullname segment_id segment_name #> 1 9000002156 - NULL 9000002156 NULL #> 2 9000001906 - NULL 9000001906 NULL"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_special_days.html","id":null,"dir":"Reference","previous_headings":"","what":"Enrich traffic data with french vacation and public holidays — enrich_special_days","title":"Enrich traffic data with french vacation and public holidays — enrich_special_days","text":"Enrich traffic data french vacation public holidays","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_special_days.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enrich traffic data with french vacation and public holidays — enrich_special_days","text":"","code":"enrich_special_days(data, vacations = NULL, public_holidays = NULL)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_special_days.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enrich traffic data with french vacation and public holidays — enrich_special_days","text":"data Data frame containing day date(day + hour + timezone) column vacations Data frame containing vacation dates public_holidays Data frame containing public holidays dates","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_special_days.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enrich traffic data with french vacation and public holidays — enrich_special_days","text":"dataframe two additionnal columns : holiday, boolean: TRUE day corresponds public holiday, FALSE otherwise vacation, indicating french vacation, \"vacation\" otherwise.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_special_days.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enrich traffic data with french vacation and public holidays — enrich_special_days","text":"","code":"df <- data.frame('day' = as.Date(c(\"2022-02-18\",\"2022-01-01\")), 'date' = c('2022-02-18 12:00:00 CET','2022-01-01 12:00:00 CET')) enrich_special_days(df) #> day date holiday vacation #> 1 2022-02-18 2022-02-18 12:00:00 CET FALSE Vacances d'Hiver #> 2 2022-01-01 2022-01-01 12:00:00 CET TRUE Vacances de Noël"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_traffic.html","id":null,"dir":"Reference","previous_headings":"","what":"Enrich traffic data with date features, names and uptime filters. — enrich_traffic","title":"Enrich traffic data with date features, names and uptime filters. — enrich_traffic","text":"function add day, weekday, hour, segment_name full_name uptime quality boolean. function already used data retrievement process.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_traffic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enrich traffic data with date features, names and uptime filters. — enrich_traffic","text":"","code":"enrich_traffic(data)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_traffic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enrich traffic data with date features, names and uptime filters. — enrich_traffic","text":"data Raw data frame Telraam API, imported package.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_traffic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enrich traffic data with date features, names and uptime filters. — enrich_traffic","text":"dataframe additionnal informations : day, hour, weekday, holiday, vacation, segment_name, uptime_quality","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_traffic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enrich traffic data with date features, names and uptime filters. — enrich_traffic","text":"","code":"enriched_traffic <- enrich_traffic(traffic[0:10,]) #> This ID is unknown. Please update configuration file. #> This ID is unknown. Please update configuration file. #> This ID is unknown. Please update configuration file. #> This ID is unknown. Please update configuration file. #> This ID is unknown. Please update configuration file. #> This ID is unknown. Please update configuration file. #> This ID is unknown. Please update configuration file. #> This ID is unknown. Please update configuration file. #> This ID is unknown. Please update configuration file. #> This ID is unknown. Please update configuration file. setdiff(colnames(enriched_traffic[0:10,]), colnames(traffic[0:10,])) #> character(0)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_uptime.html","id":null,"dir":"Reference","previous_headings":"","what":"Enrich traffic data with uptime quality indication — enrich_uptime","title":"Enrich traffic data with uptime quality indication — enrich_uptime","text":"uptime lower 0.5, uptime_quality FALSE, else TRUE","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_uptime.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enrich traffic data with uptime quality indication — enrich_uptime","text":"","code":"enrich_uptime(data)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_uptime.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enrich traffic data with uptime quality indication — enrich_uptime","text":"data Data frame containing uptime column","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_uptime.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enrich traffic data with uptime quality indication — enrich_uptime","text":"dataframe additionnal column indicating uptime greater (TRUE) lower (FALSE) 0.5.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/enrich_uptime.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enrich traffic data with uptime quality indication — enrich_uptime","text":"","code":"df <- data.frame('uptime' = c(0.05, 0.95)) enrich_uptime(df) #> uptime uptime_quality #> 1 0.05 FALSE #> 2 0.95 TRUE"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_agg.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter by selected criteria and aggregating traffics. — filter_agg","title":"Filter by selected criteria and aggregating traffics. — filter_agg","text":"criteria need filled . Unfilled criteria set default filtering performed.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_agg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter by selected criteria and aggregating traffics. — filter_agg","text":"","code":"filter_agg( data, date_range = NULL, segments = NULL, direction = NULL, modes = NULL, weekdays = NULL, hours = NULL, uptime_quality = TRUE )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_agg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter by selected criteria and aggregating traffics. — filter_agg","text":"data Traffic Data Frame date_range Date vector, c(\"aaaa-mm-jj\",\"aaaa-mm-jj\") segments Vector character. Ids desired segments. direction Vector character. Direction street (lft, right, ). modes Vector character. Type(s) mobility: c(\"car\",\"heavy\",\"pedestrian\",\"bike\") weekdays Vector character. Weekday(s) choosen. hours Integer vector. Hours choosen, default day.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_agg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter by selected criteria and aggregating traffics. — filter_agg","text":"filtered data, molten mode direction, new columns : mode, mode, one row per different transportation mode + date/hour + direction direction, direction, one row per different transportation mode + date/hour + direction traffic_sum, traffic mode/direction specific date/hour","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_agg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter by selected criteria and aggregating traffics. — filter_agg","text":"","code":"date_range = as.Date(c('2022-01-01','2022-01-08')) filter_agg(traffic, date_range = date_range, segments = 'RteVitre-06', direction = 'lft', modes = 'pedestrian', weekdays = 'saturday', hours = 12:14, uptime_quality = TRUE ) #> $data #> date day hour weekday holiday vacation #> 1 2022-01-01 12:00:00 2022-01-01 12 saturday TRUE Vacances de Noël #> 2 2022-01-01 13:00:00 2022-01-01 13 saturday TRUE Vacances de Noël #> 3 2022-01-08 12:00:00 2022-01-08 12 saturday FALSE No vacation #> 4 2022-01-08 13:00:00 2022-01-08 13 saturday FALSE No vacation #> 5 2022-01-08 14:00:00 2022-01-08 14 saturday FALSE No vacation #> segment_name v85 #> 1 RteVitre-06 6.5 #> 2 RteVitre-06 40.0 #> 3 RteVitre-06 43.0 #> 4 RteVitre-06 44.5 #> 5 RteVitre-06 44.0 #> car_speed_hist_0to70plus #> 1 100, 0, 0, 0, 0, 0, 0, 0 #> 2 0.000000, 2.222222, 11.111111, 63.333333, 22.222222, 0.000000, 0.000000, 1.111111 #> 3 6.3333333, 3.6666667, 3.6666667, 41.3333333, 38.6666667, 6.0000000, 0.0000000, 0.3333333 #> 4 1.7667845, 3.1802120, 3.5335689, 41.6961131, 45.2296820, 4.2402827, 0.3533569, 0.0000000 #> 5 0.9740260, 0.3246753, 2.9220779, 48.7012987, 40.9090909, 5.8441558, 0.0000000, 0.3246753 #> car_speed_hist_0to120plus #> 1 0, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 2 0.000000, 0.000000, 0.000000, 2.222222, 3.333333, 7.777778, 22.222222, 41.111111, 16.666667, 5.555556, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.111111, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000 #> 3 3.6666667, 2.6666667, 1.6666667, 2.0000000, 1.0000000, 2.6666667, 14.0000000, 27.3333333, 29.0000000, 9.6666667, 5.3333333, 0.6666667, 0.0000000, 0.0000000, 0.3333333, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000 #> 4 1.0600707, 0.7067138, 1.4134276, 1.7667845, 0.7067138, 2.8268551, 13.7809187, 27.9151943, 28.9752650, 16.2544170, 3.5335689, 0.7067138, 0.0000000, 0.3533569, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000 #> 5 0.9740260, 0.0000000, 0.3246753, 0.0000000, 0.9740260, 1.9480519, 14.2857143, 34.4155844, 28.8961039, 12.0129870, 4.8701299, 0.9740260, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.3246753, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000 #> uptime uptime_quality mode direction traffic_sum #> 1 0.7875000 TRUE pedestrian lft 16.507937 #> 2 0.7277778 TRUE pedestrian lft 10.992366 #> 3 0.7808333 TRUE pedestrian lft 0.000000 #> 4 0.7922222 TRUE pedestrian lft 1.262272 #> 5 0.7922222 TRUE pedestrian lft 1.262272 #> #> $segment #> [1] \"RteVitre-06\" #> #> $mode #> [1] \"pedestrian\" #> #> $direction #> [1] \"lft\" #> #> $weekday #> [1] \"saturday\" #> #> $hour #> [1] 12 13 14 #>"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_date.html","id":null,"dir":"Reference","previous_headings":"","what":"Select data within a specified date range — filter_date","title":"Select data within a specified date range — filter_date","text":"Select data within specified date range","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_date.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select data within a specified date range — filter_date","text":"","code":"filter_date(data, date_range)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_date.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select data within a specified date range — filter_date","text":"data Data frame containing date column date_range vector two dates specifying range","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_date.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select data within a specified date range — filter_date","text":"Subset input data frame within specified date range","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_public_holidays.html","id":null,"dir":"Reference","previous_headings":"","what":"Select data based on public holiday criteria — filter_public_holidays","title":"Select data based on public holiday criteria — filter_public_holidays","text":"Select data based public holiday criteria","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_public_holidays.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select data based on public holiday criteria — filter_public_holidays","text":"","code":"filter_public_holidays(data, JF)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_public_holidays.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select data based on public holiday criteria — filter_public_holidays","text":"data Data frame containing date column JF character indicating public holiday selection criteria","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_public_holidays.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select data based on public holiday criteria — filter_public_holidays","text":"Subset input data frame based public holiday criteria","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_vacation.html","id":null,"dir":"Reference","previous_headings":"","what":"Select data based on vacation criteria — filter_vacation","title":"Select data based on vacation criteria — filter_vacation","text":"Select data based vacation criteria","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_vacation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select data based on vacation criteria — filter_vacation","text":"","code":"filter_vacation(data, vacation)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_vacation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select data based on vacation criteria — filter_vacation","text":"data Data frame containing date column vacation character indicating vacation selection criteria","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filter_vacation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select data based on vacation criteria — filter_vacation","text":"Subset input data frame based vacation criteria","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filtering.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter by selected criteria. — filtering","title":"Filter by selected criteria. — filtering","text":"criteria need filled . Unfilled criteria set default filtering performed.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filtering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter by selected criteria. — filtering","text":"","code":"filtering( data = NULL, sensor = NULL, direction = \" \", mobility = c(\"car\", \"heavy\", \"pedestrian\", \"bike\"), date_range = NULL, vac = NULL, p_h = NULL, wkd = NULL, vacations = NULL, public_holidays = NULL )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filtering.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter by selected criteria. — filtering","text":"data dat.frame. See \"importation\" function '2-import.R' file sensor character. Id desired sensor quotations direction character. Direction street: \" \" \"_lft\" \"_rgt\" mobility character. Type mobility: c(\"car\",\"heavy\",\"pedestrian\",\"bike\") date_range Date vector, c(\"aaaa-mm-jj\",\"aaaa-mm-jj\") vac character. , without, vacation: \"YES\" \"\" \"\" p_h character. , without, public holiday: \"YES\" \"\" \"\" wkd character vector. Selected days week: c(\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\") starting one Monday. Make sure use options(lubridate.week.start = 1) function perform correctly. vacations data.frame, containing 3 columns : desctiption, start_date end_date description column contains character vectors name every vacations studying starting_date ending_date Date-Time vectors(POSIXct) format \"year-month-day hour:minute:second\", obtainable example using ymd_hms lubridate package public_holidays date vector, containing public holidays dates \"Year-month-day\" format.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filtering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter by selected criteria. — filtering","text":"filtered data","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filtering_agg.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter by selected criteria and aggregating traffics. — filtering_agg","title":"Filter by selected criteria and aggregating traffics. — filtering_agg","text":"criteria need filled . Unfilled criteria set default filtering performed.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filtering_agg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter by selected criteria and aggregating traffics. — filtering_agg","text":"","code":"filtering_agg( data, date_range = NULL, segments = NULL, direction = NULL, modes = NULL, weekdays = NULL, hours = NULL, uptime_quality = TRUE )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filtering_agg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter by selected criteria and aggregating traffics. — filtering_agg","text":"data Traffic Data Frame date_range Date vector, c(\"aaaa-mm-jj\",\"aaaa-mm-jj\") direction Vector character. Direction street (lft, right, ). modes Vector character. Type(s) mobility: c(\"car\",\"heavy\",\"pedestrian\",\"bike\") hours Integer vector. Hours choosen, default day. segment Vector character. Ids desired segments. weekday Vector character. Weekday(s) choosen.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/filtering_agg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter by selected criteria and aggregating traffics. — filtering_agg","text":"filtered data new column \"traffic\" aggregated data specified direction/modes","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_api_state.html","id":null,"dir":"Reference","previous_headings":"","what":"Check API state — get_api_state","title":"Check API state — get_api_state","text":"Return state 'Telraam' API. Determine updates can made.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_api_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check API state — get_api_state","text":"","code":"get_api_state(key = get_telraam_token())"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_api_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check API state — get_api_state","text":"key API key (set set_telraam_token function - default -, directly filled).","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_api_state.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check API state — get_api_state","text":"Boolean: TRUE API responds well, FALSE otherwise.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_api_state.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check API state — get_api_state","text":"","code":"if (FALSE) { my_token <- 'ThisIsNotAValidToken' get_api_state(my_token) }"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_custom_palette.html","id":null,"dir":"Reference","previous_headings":"","what":"Colors palettes for each option (mode, direction, segment_name, weekday, hour) — get_custom_palette","title":"Colors palettes for each option (mode, direction, segment_name, weekday, hour) — get_custom_palette","text":"Colors palettes option (mode, direction, segment_name, weekday, hour)","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_custom_palette.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Colors palettes for each option (mode, direction, segment_name, weekday, hour) — get_custom_palette","text":"","code":"get_custom_palette(segments)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_custom_palette.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Colors palettes for each option (mode, direction, segment_name, weekday, hour) — get_custom_palette","text":"segments Character vectors. Segments name dataframe.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_custom_palette.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Colors palettes for each option (mode, direction, segment_name, weekday, hour) — get_custom_palette","text":"list color palettes (named vector) option (mode, direction, segment_name, weekday, hour)","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_custom_palette.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Colors palettes for each option (mode, direction, segment_name, weekday, hour) — get_custom_palette","text":"","code":"segments <- c('Route1', 'Route2') get_custom_palette(segments) #> $mode #> car pedestrian bike heavy #> \"#F95335\" \"#FCAF38\" \"#50A3A4\" \"#674A40\" #> #> $direction #> both lft rgt #> \"#6d676e\" \"#ff595e\" \"#8ac926\" #> #> $segment_name #> #> #7E1900FF #1A3399FF #> #> $weekday #> #> #009392FF #39B185FF #9CCB86FF #E9E29CFF #EEB479FF #E88471FF #CF597EFF #> #> $hour #> 1 2 3 4 5 6 #> \"#FEFBE9FF\" \"#FCF7D5FF\" \"#F5F3C1FF\" \"#EAF0B5FF\" \"#DDECBFFF\" \"#D0E7CAFF\" #> 7 8 9 10 11 12 #> \"#C2E3D2FF\" \"#B5DDD8FF\" \"#A8D8DCFF\" \"#9BD2E1FF\" \"#8DCBE4FF\" \"#81C4E7FF\" #> 13 14 15 16 17 18 #> \"#7BBCE7FF\" \"#7EB2E4FF\" \"#88A5DDFF\" \"#9398D2FF\" \"#9B8AC4FF\" \"#9D7DB2FF\" #> 19 20 21 22 23 #> \"#9A709EFF\" \"#906388FF\" \"#805770FF\" \"#684957FF\" \"#46353AFF\" \"grey\" #>"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_graph_subtitles.html","id":null,"dir":"Reference","previous_headings":"","what":"Create subtitles for graphics. — get_graph_subtitles","title":"Create subtitles for graphics. — get_graph_subtitles","text":"Create subtitles graphics.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_graph_subtitles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create subtitles for graphics. — get_graph_subtitles","text":"","code":"get_graph_subtitles( segments = NULL, modes = NULL, directions = NULL, weekdays = NULL, hours = NULL )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_graph_subtitles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create subtitles for graphics. — get_graph_subtitles","text":"segments Character vector. Selected road segment text, precision NULL (default). modes Character vector. Different modes transportation selected (heavy, car, bike, pedestrian). Default: NULL directions Character vector. Directions traffic (lft, rgt, ) choosen. Default NULL. weekdays Character vector. Weekdays choosen. Default NULL. hours Numeric vector. Hours choosen. Default NULL.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_graph_subtitles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create subtitles for graphics. — get_graph_subtitles","text":"Character, description parameters filled, usable subtitle graphs.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_graph_subtitles.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create subtitles for graphics. — get_graph_subtitles","text":"","code":"get_graph_subtitles(segments = c(\"Route1\"), mode = \"car\", direction = \"lft\", weekdays = \"monday\", hours = 12:14) #> [1] \"Modes: car\\nDirections: lft\\nWeekdays: monday\\nSegments: Route1\\nHours: 12, 13, 14\""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_segment_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the name of a segment giving its id — get_segment_name","title":"Get the name of a segment giving its id — get_segment_name","text":"Get name segment giving id","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_segment_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the name of a segment giving its id — get_segment_name","text":"","code":"get_segment_name(segment_id)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_segment_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the name of a segment giving its id — get_segment_name","text":"segment_id ID segment, present inst/config.yml","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_segment_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the name of a segment giving its id — get_segment_name","text":"Name segment, specified configuration file, NULL otherwise.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_segment_name.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the name of a segment giving its id — get_segment_name","text":"","code":"if (FALSE) #run if you want to create a inst/ directory containing config file create_config() get_segment_name(9000000000) #> This ID is unknown. Please update configuration file. #> NULL"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_segments.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Telraam segments into a named vector — get_segments","title":"Get Telraam segments into a named vector — get_segments","text":"Get Telraam segments info yml file transform named vector","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_segments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Telraam segments into a named vector — get_segments","text":"","code":"get_segments()"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_segments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Telraam segments into a named vector — get_segments","text":"Named vector names segment IDs, NULL configuration file","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_segments.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Telraam segments into a named vector — get_segments","text":"","code":"if (FALSE) #run if you want to create a inst/ directory containing config file create_config() get_segments() #> $`segment-01` #> [1] \"9000000000\" #> #> $`segment-02` #> [1] \"9000000000\" #>"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_telraam_token.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the current authentication token for the 'Telraam' API — get_telraam_token","title":"Get the current authentication token for the 'Telraam' API — get_telraam_token","text":"Get current authentication token 'Telraam' API","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_telraam_token.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the current authentication token for the 'Telraam' API — get_telraam_token","text":"","code":"get_telraam_token()"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_telraam_token.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the current authentication token for the 'Telraam' API — get_telraam_token","text":"Token currently used, set set_telraam_token()","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/get_telraam_token.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the current authentication token for the 'Telraam' API — get_telraam_token","text":"","code":"my_token <- \"MyTelraamToken\" set_telraam_token(my_token) get_telraam_token() #> [1] \"MyTelraamToken\""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Availability and quality of sensors during a period through a heatmap. — gg_availability","title":"Availability and quality of sensors during a period through a heatmap. — gg_availability","text":"Higher uptime average, higher quality data. null uptime means sensor available period.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Availability and quality of sensors during a period through a heatmap. — gg_availability","text":"","code":"gg_availability(enriched_data, date_range = NULL)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Availability and quality of sensors during a period through a heatmap. — gg_availability","text":"enriched_data enriched data.frame containing data sensors date_range Date vector. Example: c('2021-01-01','2022-01-01'). Full period NULL.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Availability and quality of sensors during a period through a heatmap. — gg_availability","text":"Graph showing availability quality sensors selected date range.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Availability and quality of sensors during a period through a heatmap. — gg_availability","text":"","code":"gg_availability(traffic)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_car_speed_histogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of car speed over a period, for a segment or a subset of segment. — gg_car_speed_histogram","title":"Histogram of car speed over a period, for a segment or a subset of segment. — gg_car_speed_histogram","text":"Histogram car speed period, segment subset segment.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_car_speed_histogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of car speed over a period, for a segment or a subset of segment. — gg_car_speed_histogram","text":"","code":"gg_car_speed_histogram( enriched_data, date_range = NULL, segments = NULL, weekday = NULL, hours = NULL, aggregated_by = NULL )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_car_speed_histogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of car speed over a period, for a segment or a subset of segment. — gg_car_speed_histogram","text":"enriched_data enriched data.frame containing data sensors date_range Date vector. Example: c('2021-01-01','2022-01-01'). Full period NULL (default). segments Character vector. Selected road segment, NULL (default). weekday Character vector. Weekday choosen. Default week. hours Integer vector. Hours choosen, default day. aggregated_by Character. Enables comparison segments weekdays. Options : 'segment_name', 'weekday', NULL (comparison, default).","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_car_speed_histogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of car speed over a period, for a segment or a subset of segment. — gg_car_speed_histogram","text":"Graph showing histogram car speed period.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_car_speed_histogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of car speed over a period, for a segment or a subset of segment. — gg_car_speed_histogram","text":"","code":"library(dplyr) #> #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union subset_traffic <- traffic %>% filter(day < '2022-02-01', hour > 9) gg_car_speed_histogram(subset_traffic) gg_car_speed_histogram(subset_traffic, aggregated_by = 'segment_name') gg_car_speed_histogram(subset_traffic, weekday = c('monday','sunday'), segments = 'RteVitre-06', hours = 17:20, aggregated_by = \"weekday\")"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_car_speed_v85.html","id":null,"dir":"Reference","previous_headings":"","what":"Average of v85 car speed per hour over a period, for a segment or a subset of segment. — gg_car_speed_v85","title":"Average of v85 car speed per hour over a period, for a segment or a subset of segment. — gg_car_speed_v85","text":"v85 estimated car speed limit km/h 85% cars respect. 15% drivers drive faster v85 indicator.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_car_speed_v85.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average of v85 car speed per hour over a period, for a segment or a subset of segment. — gg_car_speed_v85","text":"","code":"gg_car_speed_v85( enriched_data, date_range = NULL, segments = NULL, weekday = NULL, hours = NULL, aggregated_by = NULL )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_car_speed_v85.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average of v85 car speed per hour over a period, for a segment or a subset of segment. — gg_car_speed_v85","text":"enriched_data enriched data.frame containing data sensors date_range Date vector. Example: c('2021-01-01','2022-01-01'). Full period NULL (default). segments Character vector. Selected road segment, NULL (default). weekday Character vector. Weekday choosen. Default week. hours Integer vector. Hours choosen, default day. aggregated_by Character. Enables comparison segments weekdays. Options : 'segment_name', 'weekday', NULL (comparison, default).","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_car_speed_v85.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average of v85 car speed per hour over a period, for a segment or a subset of segment. — gg_car_speed_v85","text":"Graph showing average v85 speed per hour.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_car_speed_v85.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average of v85 car speed per hour over a period, for a segment or a subset of segment. — gg_car_speed_v85","text":"","code":"library(dplyr) subset_traffic <- traffic %>% filter(day < '2022-02-01', hour > 9) gg_car_speed_histogram(subset_traffic[0:100,]) gg_car_speed_histogram(subset_traffic, aggregated_by = 'segment_name') gg_car_speed_histogram(subset_traffic, weekday = c('monday','sunday'), segments = 'RteVitre-06', hours = 17:20, aggregated_by = \"weekday\")"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_na_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"gg_na_dist\nThe function gives a graphical representation of NA's distribution (monthly) during a selected period for one sensor. — gg_na_dist","title":"gg_na_dist\nThe function gives a graphical representation of NA's distribution (monthly) during a selected period for one sensor. — gg_na_dist","text":"gg_na_dist function gives graphical representation NA's distribution (monthly) selected period one sensor.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_na_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"gg_na_dist\nThe function gives a graphical representation of NA's distribution (monthly) during a selected period for one sensor. — gg_na_dist","text":"","code":"gg_na_dist( data, sensor, start = NULL, end = NULL, hours = \"ALL\", list_weekday = \"ALL\", pub_holidays = \"YES\", holidays = \"YES\" )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_na_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"gg_na_dist\nThe function gives a graphical representation of NA's distribution (monthly) during a selected period for one sensor. — gg_na_dist","text":"data Data.Frame. data frame containing road traffic data. sensor Numeric. sensor ID want see representation. start Character. start period format \"yyyy-mm-dd\". end Character. end period format \"yyyy-mm-dd\". hours Vector. vector containing hours want see representation, e.g., c(\"05h\", \"18h\"). list_weekday Vector. vector containing weekdays French, e.g., c(\"Monday\", \"Sunday\"). pub_holidays Character. \"YES\" want see results public holidays included; \"\" want exclude representation; \"\" want see result. holidays Character. \"YES\" want see results including holidays; \"\" want exclude representation; \"\" want see result.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_na_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"gg_na_dist\nThe function gives a graphical representation of NA's distribution (monthly) during a selected period for one sensor. — gg_na_dist","text":"ggplot graph.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_na_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"gg_na_heatmap\nThis function generates a heatmap representation of the proportion of missing values (NA) for different sensors over monthly periods during a selected time frame. — gg_na_heatmap","title":"gg_na_heatmap\nThis function generates a heatmap representation of the proportion of missing values (NA) for different sensors over monthly periods during a selected time frame. — gg_na_heatmap","text":"gg_na_heatmap function generates heatmap representation proportion missing values (NA) different sensors monthly periods selected time frame.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_na_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"gg_na_heatmap\nThis function generates a heatmap representation of the proportion of missing values (NA) for different sensors over monthly periods during a selected time frame. — gg_na_heatmap","text":"","code":"gg_na_heatmap( data, start = NULL, end = NULL, hours = \"ALL\", list_weekday = \"ALL\", pub_holidays = \"YES\", holidays = \"YES\" )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_na_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"gg_na_heatmap\nThis function generates a heatmap representation of the proportion of missing values (NA) for different sensors over monthly periods during a selected time frame. — gg_na_heatmap","text":"data Data frame. data frame containing road traffic data. start Character. start date format \"yyyy-mm-dd\". end Character. end date format \"yyyy-mm-dd\". hours Vector. vector containing hours want see representation, e.g., c(\"05h\", \"18h\"). list_weekday Vector. vector containing weekdays French, e.g., c(\"Monday\", \"Sunday\"). pub_holidays Character. \"YES\" want see results public holidays included; \"\" want exclude representation; \"\" want see result. holidays Character. \"YES\" want see results including holidays; \"\" want exclude representation; \"\" want see result.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_na_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"gg_na_heatmap\nThis function generates a heatmap representation of the proportion of missing values (NA) for different sensors over monthly periods during a selected time frame. — gg_na_heatmap","text":"ggplot graph representing heatmap NA proportions different sensors monthly periods.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_traffic_avg.html","id":null,"dir":"Reference","previous_headings":"","what":"Average of traffic during a week. — gg_traffic_avg","title":"Average of traffic during a week. — gg_traffic_avg","text":"short description... Average traffic week, period segment subset segment, transportation mode , direction .","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_traffic_avg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Average of traffic during a week. — gg_traffic_avg","text":"","code":"gg_traffic_avg( enriched_data, date_range = NULL, segments = NULL, modes = c(\"heavy\", \"car\"), direction = \"both\", weekday = NULL, aggregated_by = \"weekday\" )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_traffic_avg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Average of traffic during a week. — gg_traffic_avg","text":"enriched_data enriched data.frame containing data sensors date_range Date vector. example: c('2021-01-01','2022-01-01'). Full period NULL (default). segments Character vector. Selected road segment, NULL (default). modes Character vector. Different modes transportation aggregated (heavy, car, bike, pedestrian) . Default: heavy & car direction Character. Direction traffic (lft, rgt, ). Default . weekday Character vector. Weekday choosen. Default week. aggregated_by Character. Options : 'segment_name', 'weekday', 'direction', 'mode'. Default: 'weekday'.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_traffic_avg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Average of traffic during a week. — gg_traffic_avg","text":"Graph showing weekly average evolution traffic (specified parameters) specified period.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_traffic_avg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Average of traffic during a week. — gg_traffic_avg","text":"","code":"gg_traffic_avg(traffic) gg_traffic_avg(traffic, date_range = c('2022-07-01','2022-09-01'), segment = 'RteVitre-06', mode = 'car', direction = 'rgt', weekday = c('monday','friday') )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_traffic_evolution.html","id":null,"dir":"Reference","previous_headings":"","what":"Evolution of traffic and smoothed traffic. — gg_traffic_evolution","title":"Evolution of traffic and smoothed traffic. — gg_traffic_evolution","text":"Evolution traffic (global, per mode ou per direction), smoothed traffic period.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_traffic_evolution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evolution of traffic and smoothed traffic. — gg_traffic_evolution","text":"","code":"gg_traffic_evolution( enriched_data, date_range = NULL, segments = NULL, modes = c(\"heavy\", \"car\"), direction = \"both\", smoothed = TRUE, agg_day = TRUE )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_traffic_evolution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evolution of traffic and smoothed traffic. — gg_traffic_evolution","text":"enriched_data enriched data.frame containing data sensors date_range Date vector. Example: c('2021-01-01','2022-01-01'). Full period NULL (default). segments Character vector. Selected road segment name, NULL (default). modes Character vector. Different modes transportation aggregated (heavy, car, bike, pedestrian) . Default: heavy & car direction Character. Direction traffic (lft, rgt, ). Default . smoothed Boolean. smoothed traffic plotted ? Default: True agg_day Boolean. data aggregated per day ? Default : True","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_traffic_evolution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evolution of traffic and smoothed traffic. — gg_traffic_evolution","text":"Graph showing evolution traffic (specified parameters) specified period.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_traffic_evolution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evolution of traffic and smoothed traffic. — gg_traffic_evolution","text":"","code":"gg_traffic_evolution(traffic) gg_traffic_evolution(traffic, date_range = c('2022-01-01','2022-03-01'), segment = 'RteVitre-06', mode = c('car','pedestrian'), direction = 'lft', smoothed = FALSE, agg_day = FALSE)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_working_sensor.html","id":null,"dir":"Reference","previous_headings":"","what":"gg_working_sensor\nthe function gives a graphical representation of the sensors operation during a selected period,\nthe representation can be grouped or individual — gg_working_sensor","title":"gg_working_sensor\nthe function gives a graphical representation of the sensors operation during a selected period,\nthe representation can be grouped or individual — gg_working_sensor","text":"gg_working_sensor function gives graphical representation sensors operation selected period, representation can grouped individual","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_working_sensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"gg_working_sensor\nthe function gives a graphical representation of the sensors operation during a selected period,\nthe representation can be grouped or individual — gg_working_sensor","text":"","code":"gg_working_sensor( list_sensor = \"ALL\", data, start, end, hours = \"ALL\", aggregate = TRUE, list_weekday = \"ALL\", pub_holyday = \"YES\", holydays = \"YES\" )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_working_sensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"gg_working_sensor\nthe function gives a graphical representation of the sensors operation during a selected period,\nthe representation can be grouped or individual — gg_working_sensor","text":"list_sensor vector containing sensor IDs, ex : c(\"9000001906\", \"9000003090) data data frame containing road traffic data start start period format \"yyyy-mm-dd\" end start period format \"yyyy-mm-dd\" hours vector containing hours want see representation, ex : c(\"05h\", \"18h\") aggregate aggregate = TRUE, see information sensors together. aggregate = FALSe, information show sensor sensor list_weekday vector containing weekdays french, ex : c(\"lundi\", \"jeudi\") pub_holyday \"YES\" : want see results public holydays inclue; \"\" want exclude representation; \"\" want see result holydays \"YES\" : want see results including holydays; \"\" want exclude representation; \"\" want see result","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/gg_working_sensor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"gg_working_sensor\nthe function gives a graphical representation of the sensors operation during a selected period,\nthe representation can be grouped or individual — gg_working_sensor","text":"ggplot graph","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/graph_subtitles.html","id":null,"dir":"Reference","previous_headings":"","what":"Create subtitles for graphics. — graph_subtitles","title":"Create subtitles for graphics. — graph_subtitles","text":"Create subtitles graphics.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/graph_subtitles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create subtitles for graphics. — graph_subtitles","text":"","code":"graph_subtitles( segments = NULL, modes = NULL, directions = NULL, weekdays = NULL, hours = NULL )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/graph_subtitles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create subtitles for graphics. — graph_subtitles","text":"segments Character vector. Selected road segment, NULL (default). modes Character vector. Different modes transportation aggregated (heavy, car, bike, pedestrian) . Default: heavy & car directions Character vector. Direction traffic (lft, rgt, ). Default . weekdays Character vector. Weekday choosen. Default week.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/graph_subtitles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create subtitles for graphics. — graph_subtitles","text":"DataFrame one row per hour/segment/transportation mode/direction","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/imp_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Impute Missing Data using Linear Interpolation — imp_na","title":"Impute Missing Data using Linear Interpolation — imp_na","text":"function uses linear interpolation method fill missing data road traffic data. interpolation done day 4 hours data row missing, days (grouped day week) 4 consecutive weeks missing.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/imp_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Impute Missing Data using Linear Interpolation — imp_na","text":"","code":"imp_na(data, period = \"open days\")"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/imp_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Impute Missing Data using Linear Interpolation — imp_na","text":"data Data frame. data frame containing road traffic data. period Character. period impute data : \"open days\" open days, \"public holidays\" public holidays, \"holidays\" holidays.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/imp_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Impute Missing Data using Linear Interpolation — imp_na","text":"data frame imputed data new column named \"imputation\" indicating whether data point original (0), imputed (1), NA (2).","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/import_sensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Imports data associated with a list of sensors — import_sensor","title":"Imports data associated with a list of sensors — import_sensor","text":"Imports data associated given list sensor names .RData files contained data directory. main purpose function load data saved write update data.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/import_sensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Imports data associated with a list of sensors — import_sensor","text":"","code":"import_sensor(list_sensor)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/import_sensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Imports data associated with a list of sensors — import_sensor","text":"list_sensor character vector specifying names sensors import data .","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/import_sensor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Imports data associated with a list of sensors — import_sensor","text":"dataframe containing imported data.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/import_sensor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Imports data associated with a list of sensors — import_sensor","text":"","code":"if (FALSE) { period <- as.Date(c('2022-01-01', '2022-12-31')) write_update_data('RteVitre-06', period[1], period[2]) write_update_data('ParisArcEnCiel-05', period[1], period[2]) import_sensor(c('RteVitre-06', 'ParisArcEnCiel-05')) }"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/import_sensor_comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Import Sensor Data and Combine — import_sensor_comp","title":"Import Sensor Data and Combine — import_sensor_comp","text":"function imports sensor data CSV files given sensor IDs combines . function also processes data converting certain columns lists.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/import_sensor_comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import Sensor Data and Combine — import_sensor_comp","text":"","code":"import_sensor_comp(sensor_ids, sensor_comp_names)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/import_sensor_comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import Sensor Data and Combine — import_sensor_comp","text":"sensor_ids character vector containing sensor IDs import. sensor_comp_names character vector containing names CSV files import.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/import_sensor_comp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import Sensor Data and Combine — import_sensor_comp","text":"data frame containing imported combined sensor data.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/is_vacation.html","id":null,"dir":"Reference","previous_headings":"","what":"Indicates if a date is in vacation period and if true, which vacation. — is_vacation","title":"Indicates if a date is in vacation period and if true, which vacation. — is_vacation","text":"date vacation period, \"vacation\" returned.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/is_vacation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indicates if a date is in vacation period and if true, which vacation. — is_vacation","text":"","code":"is_vacation(date, vacation)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/is_vacation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indicates if a date is in vacation period and if true, which vacation. — is_vacation","text":"date Date (character format) vacation Dataframe vacations, format set_globals_vars output.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/is_vacation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indicates if a date is in vacation period and if true, which vacation. — is_vacation","text":"Vacation description day two dates, \"vacation\" otherwise.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/is_vacation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Indicates if a date is in vacation period and if true, which vacation. — is_vacation","text":"","code":"vacation <- data.frame('description' = c('Vacances de Noël'), start_date = as.POSIXct('2021-12-17 23:00:00'), end_date = as.POSIXct('2022-01-02 23:00:00')) is_vacation(as.Date('2022-01-01'), vacation) #> [1] \"Vacances de Noël\""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/melt_direction_mode.html","id":null,"dir":"Reference","previous_headings":"","what":"Melt dataframe to obtain one row per hour/segment/transportation mode/direction\nThis format makes graphs with ggplot and filtering easier. — melt_direction_mode","title":"Melt dataframe to obtain one row per hour/segment/transportation mode/direction\nThis format makes graphs with ggplot and filtering easier. — melt_direction_mode","text":"Melt dataframe obtain one row per hour/segment/transportation mode/direction format makes graphs ggplot filtering easier.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/melt_direction_mode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Melt dataframe to obtain one row per hour/segment/transportation mode/direction\nThis format makes graphs with ggplot and filtering easier. — melt_direction_mode","text":"","code":"melt_direction_mode(data)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/melt_direction_mode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Melt dataframe to obtain one row per hour/segment/transportation mode/direction\nThis format makes graphs with ggplot and filtering easier. — melt_direction_mode","text":"data Traffic Data Frame","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/melt_direction_mode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Melt dataframe to obtain one row per hour/segment/transportation mode/direction\nThis format makes graphs with ggplot and filtering easier. — melt_direction_mode","text":"DataFrame one row per hour/segment/transportation mode/direction","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/melt_direction_mode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Melt dataframe to obtain one row per hour/segment/transportation mode/direction\nThis format makes graphs with ggplot and filtering easier. — melt_direction_mode","text":"","code":"melt_direction_mode(traffic[0:2,]) #> date day hour weekday holiday vacation #> 1 2022-01-01 01:00:00 CET 2022-01-01 1 saturday TRUE Vacances de Noël #> 2 2022-01-01 02:00:00 CET 2022-01-01 2 saturday TRUE Vacances de Noël #> 3 2022-01-01 01:00:00 CET 2022-01-01 1 saturday TRUE Vacances de Noël #> 4 2022-01-01 02:00:00 CET 2022-01-01 2 saturday TRUE Vacances de Noël #> 5 2022-01-01 01:00:00 CET 2022-01-01 1 saturday TRUE Vacances de Noël #> 6 2022-01-01 02:00:00 CET 2022-01-01 2 saturday TRUE Vacances de Noël #> 7 2022-01-01 01:00:00 CET 2022-01-01 1 saturday TRUE Vacances de Noël #> 8 2022-01-01 02:00:00 CET 2022-01-01 2 saturday TRUE Vacances de Noël #> 9 2022-01-01 01:00:00 CET 2022-01-01 1 saturday TRUE Vacances de Noël #> 10 2022-01-01 02:00:00 CET 2022-01-01 2 saturday TRUE Vacances de Noël #> 11 2022-01-01 01:00:00 CET 2022-01-01 1 saturday TRUE Vacances de Noël #> 12 2022-01-01 02:00:00 CET 2022-01-01 2 saturday TRUE Vacances de Noël #> 13 2022-01-01 01:00:00 CET 2022-01-01 1 saturday TRUE Vacances de Noël #> 14 2022-01-01 02:00:00 CET 2022-01-01 2 saturday TRUE Vacances de Noël #> 15 2022-01-01 01:00:00 CET 2022-01-01 1 saturday TRUE Vacances de Noël #> 16 2022-01-01 02:00:00 CET 2022-01-01 2 saturday TRUE Vacances de Noël #> 17 2022-01-01 01:00:00 CET 2022-01-01 1 saturday TRUE Vacances de Noël #> 18 2022-01-01 02:00:00 CET 2022-01-01 2 saturday TRUE Vacances de Noël #> 19 2022-01-01 01:00:00 CET 2022-01-01 1 saturday TRUE Vacances de Noël #> 20 2022-01-01 02:00:00 CET 2022-01-01 2 saturday TRUE Vacances de Noël #> 21 2022-01-01 01:00:00 CET 2022-01-01 1 saturday TRUE Vacances de Noël #> 22 2022-01-01 02:00:00 CET 2022-01-01 2 saturday TRUE Vacances de Noël #> 23 2022-01-01 01:00:00 CET 2022-01-01 1 saturday TRUE Vacances de Noël #> 24 2022-01-01 02:00:00 CET 2022-01-01 2 saturday TRUE Vacances de Noël #> segment_name v85 car_speed_hist_0to70plus #> 1 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 2 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 3 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 4 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 5 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 6 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 7 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 8 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 9 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 10 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 11 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 12 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 13 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 14 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 15 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 16 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 17 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 18 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 19 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 20 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 21 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 22 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 23 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> 24 RteVitre-06 NA 0, 0, 0, 0, 0, 0, 0, 0 #> car_speed_hist_0to120plus #> 1 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 2 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 3 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 4 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 5 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 6 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 7 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 8 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 9 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 10 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 11 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 12 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 13 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 14 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 15 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 16 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 17 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 18 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 19 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 20 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 21 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 22 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 23 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> 24 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 #> uptime uptime_quality mode direction traffic_sum #> 1 0 FALSE pedestrian both 0 #> 2 0 FALSE pedestrian both 0 #> 3 0 FALSE bike both 0 #> 4 0 FALSE bike both 0 #> 5 0 FALSE heavy both 0 #> 6 0 FALSE heavy both 0 #> 7 0 FALSE car both 0 #> 8 0 FALSE car both 0 #> 9 0 FALSE pedestrian lft 0 #> 10 0 FALSE pedestrian lft 0 #> 11 0 FALSE bike lft 0 #> 12 0 FALSE bike lft 0 #> 13 0 FALSE heavy lft 0 #> 14 0 FALSE heavy lft 0 #> 15 0 FALSE car lft 0 #> 16 0 FALSE car lft 0 #> 17 0 FALSE pedestrian rgt 0 #> 18 0 FALSE pedestrian rgt 0 #> 19 0 FALSE bike rgt 0 #> 20 0 FALSE bike rgt 0 #> 21 0 FALSE heavy rgt 0 #> 22 0 FALSE heavy rgt 0 #> 23 0 FALSE car rgt 0 #> 24 0 FALSE car rgt 0"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/na_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualisation NA stats — na_prop","title":"Visualisation NA stats — na_prop","text":"function calculates returns proportion missing values (NA) imputed values given sensor within specified date range.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/na_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualisation NA stats — na_prop","text":"","code":"na_prop(data, start, end, sensor)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/na_prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualisation NA stats — na_prop","text":"data Data frame. Containing road traffic data. start Character. start date format \"yyyy-mm-dd\". end Character. end date format \"yyyy-mm-dd\". sensor Character. sensor ID want work.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/na_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualisation NA stats — na_prop","text":"character vector containing two phrases: proportion missing values data treatment. proportion missing values replaced imputed data remaining proportion missing values.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_comparaison.html","id":null,"dir":"Reference","previous_headings":"","what":"Comparison time period — plot_comparaison","title":"Comparison time period — plot_comparaison","text":"Comparative visualization number users hour day data two different time periods.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_comparaison.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Comparison time period — plot_comparaison","text":"","code":"plot_comparaison(param_ref, param_1, color_graph)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_comparaison.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Comparison time period — plot_comparaison","text":"param_ref List data parameters want filter compare, filtering : list(data=,sensor=,direction=,mobility=,date_range=,vac=,wkd=,p_h=) : - data: dataframe containing data sensor: character, id sensor want study quotes direction: character, \" \" , \"_rgt\" right (B ), \"_lft\" left (B) mobility: character vector, c(\"car\", \"heavy\", \"pedestrian\", \"bike\") types can choose date_range: date vector, c(\"starting_date\", \"ending_date\") vac: character, \"YES\" keep, \"\" filter , \"\" take vacation database wkd: character vector, c(\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\"), \"1\" stands Monday \"7\" Sunday. Use options(lubridate.week.start = 1) function perform correctly. p_h: character, \"YES\" keep, \"\" filter , \"\" take public holidays database param_1 List, param_ref color_graph Color palette plot","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_comparaison.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Comparison time period — plot_comparaison","text":"ggplot object representing comparison plot","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_deseas.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot decomposed data — plot_deseas","title":"Plot decomposed data — plot_deseas","text":"Plot decomposed data","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_deseas.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot decomposed data — plot_deseas","text":"","code":"plot_deseas( data, sensor1, sensor2, hour_x, direction1, direction2, mobility, norm, vacations = NULL, public_holidays = NULL )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_deseas.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot decomposed data — plot_deseas","text":"data Data frame containing data sensor1 segment ID sensor 1 sensor2 segment ID sensor 2 hour_x hour filter direction1 direction sensor 1 direction2 direction sensor 2 mobility character vector indicating mobility types include filter norm character indicating whether normalize results vacations vacations period filtering function public_holidays public holidays period filtering function","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_deseas.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot decomposed data — plot_deseas","text":"list plots including trend, seasonal, random, peaks, correlation","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_hour_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots the number of vehicles in each direction for a selected sensor, aswell as the average speed and traffic volume\nby hour of the day. — plot_hour_threshold","title":"Plots the number of vehicles in each direction for a selected sensor, aswell as the average speed and traffic volume\nby hour of the day. — plot_hour_threshold","text":"Plots number vehicles direction selected sensor, aswell average speed traffic volume hour day.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_hour_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots the number of vehicles in each direction for a selected sensor, aswell as the average speed and traffic volume\nby hour of the day. — plot_hour_threshold","text":"","code":"plot_hour_threshold(...)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_hour_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots the number of vehicles in each direction for a selected sensor, aswell as the average speed and traffic volume\nby hour of the day. — plot_hour_threshold","text":"... data parameters filter , see filtering","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_hour_threshold.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots the number of vehicles in each direction for a selected sensor, aswell as the average speed and traffic volume\nby hour of the day. — plot_hour_threshold","text":"list containing plotly figure aggregated traffic data hour.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_speed.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize speed proportion evolution with trafic — plot_speed","title":"Visualize speed proportion evolution with trafic — plot_speed","text":"Graphical representation (raw smoothed curves) proportion vehicles 10, 20, 30 40 km/h per vehicles/hour","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_speed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize speed proportion evolution with trafic — plot_speed","text":"","code":"plot_speed(...)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_speed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize speed proportion evolution with trafic — plot_speed","text":"... data parameters filter , see filtering","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_speed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize speed proportion evolution with trafic — plot_speed","text":"list containing curves, result Darling-Erdös test performed smoothed curves, processed data used curves","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Threshold on Speed Chart — plot_threshold","title":"Plot Threshold on Speed Chart — plot_threshold","text":"Plot Threshold Speed Chart","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Threshold on Speed Chart — plot_threshold","text":"","code":"plot_threshold(plot_speed, selected_speed, state_threshold, threshold = NULL)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Threshold on Speed Chart — plot_threshold","text":"plot_speed List, plot_speed function returns. selected_speed Character indicating selected speed threshold (c(\"\",40KM/H\", \"30KM/H\", \"20KM/H\", \"10KM/H\")). state_threshold Character indicating state threshold (\"auto\" \"manual\"). threshold Numeric value representing manual threshold state_threshold \"manual\".","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/plot_threshold.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Threshold on Speed Chart — plot_threshold","text":"ggplot2 chart speed plot threshold line.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/prep.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare data for plotting — prep","title":"Prepare data for plotting — prep","text":"Prepare data plotting","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/prep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare data for plotting — prep","text":"","code":"prep(data, count = \"count\", mean = \"mean\", var = \"var\", total = .data$total)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/prep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare data for plotting — prep","text":"data Data table processed count Column name representing count mean Column name representing mean var Column name representing variance total Column total table","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/prep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare data for plotting — prep","text":"processed data table plotting","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/prep_comp_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepares and completes road traffic data by replacing missing hour values with NA — prep_comp_data","title":"Prepares and completes road traffic data by replacing missing hour values with NA — prep_comp_data","text":"function prepares input road traffic data converting date character format, filtering rows missing dates, performing additional data cleaning imputation. also completes data adding missing hours sensor converting necessary variables numeric format.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/prep_comp_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepares and completes road traffic data by replacing missing hour values with NA — prep_comp_data","text":"","code":"prep_comp_data(data)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/prep_comp_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepares and completes road traffic data by replacing missing hour values with NA — prep_comp_data","text":"data Data frame containing road traffic data.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/prep_comp_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepares and completes road traffic data by replacing missing hour values with NA — prep_comp_data","text":"complete data frame missing hour values replaced NA.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/prep_view_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare and filter data for viewing — prep_view_data","title":"Prepare and filter data for viewing — prep_view_data","text":"function prepares filters input data based specified parameters","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/prep_view_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare and filter data for viewing — prep_view_data","text":"","code":"prep_view_data( data, start = str_sub(min(data$date), 1, 10), end = str_sub(max(data$date), 1, 10), list_weekday = \"ALL\", pub_holidays = \"YES\", holidays = \"YES\" )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/prep_view_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare and filter data for viewing — prep_view_data","text":"data input data frame containing sensor data. start start date \"yyyy-mm-dd\" format (default minimum date data). end end date \"yyyy-mm-dd\" format (default maximum date data). list_weekday character vector weekdays include analysis (default \"\"). pub_holidays character indicating filter public holidays. Possible values \"YES\", \"\", \"\" (default \"YES\"). holidays character indicating filter holidays. Possible values \"YES\", \"\", \"\" (default \"YES\").","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/prep_view_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare and filter data for viewing — prep_view_data","text":"data frame containing summarized information operating sensors. data frame columns sensor_id, days, hourly uptime data (06h 18h).","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/preprocess_car_speed.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocessing mandatory for car speed graphs functions — preprocess_car_speed","title":"Preprocessing mandatory for car speed graphs functions — preprocess_car_speed","text":"Preprocessing mandatory car speed graphs functions","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/preprocess_car_speed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocessing mandatory for car speed graphs functions — preprocess_car_speed","text":"","code":"preprocess_car_speed( enriched_data, aggregated_by, date_range, segments, weekday, hours )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/preprocess_car_speed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocessing mandatory for car speed graphs functions — preprocess_car_speed","text":"enriched_data enriched data.frame containing data sensors aggregated_by Character. Enables comparison segments weekdays. Options : 'segment_name', 'weekday'. date_range Date vector. Example: c('2021-01-01','2022-01-01'). segments Character vector. Selected road segment. weekday Character vector. Weekday choosen. hours Integer vector. Hours choosen.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/preprocess_car_speed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocessing mandatory for car speed graphs functions — preprocess_car_speed","text":"list parameters data preprocessed.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/preprocess_car_speed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preprocessing mandatory for car speed graphs functions — preprocess_car_speed","text":"","code":"if (FALSE) { preprocess_car_speed(traffic, date_range = c('2022-01-01','2022-03-01'), segments = 'RteVitre-06', weekday = c('monday','sunday'), hours = 17:20, aggregated_by = \"weekday\") }"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/retrieve_sensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieves data associated with a sensor from the Telraam API — retrieve_sensor","title":"Retrieves data associated with a sensor from the Telraam API — retrieve_sensor","text":"Retrieves data associated sensor Telraam API. data retrieved specified time period start_date end_date (inclusive).","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/retrieve_sensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieves data associated with a sensor from the Telraam API — retrieve_sensor","text":"","code":"retrieve_sensor(segment_name, start_date, end_date, key = get_telraam_token())"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/retrieve_sensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieves data associated with a sensor from the Telraam API — retrieve_sensor","text":"segment_name Character. Name segment, specified config. start_date Date. Start date \"aaaa-mm-jj\", must date type. end_date Date. End date \"aaaa-mm-jj\", must date type. key API key (set set_telraam_token() function)","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/retrieve_sensor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieves data associated with a sensor from the Telraam API — retrieve_sensor","text":"Dataframe Telraam API, enriched enrich_traffic() function.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/retrieve_sensor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieves data associated with a sensor from the Telraam API — retrieve_sensor","text":"","code":"if (FALSE) { period <- as.Date(c('2022-01-01', '2022-12-31')) retrieve_sensor('RteVitre-06', period[1], period[2]) }"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/seq_by_3_month.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate sequence of intervals with three-month periods — seq_by_3_month","title":"Generate sequence of intervals with three-month periods — seq_by_3_month","text":"function used internally retrieve_sensor function generate sequence intervals three-month periods. takes start date (start_date) end date (end_date), returns data frame two columns representing start end dates interval.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/seq_by_3_month.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate sequence of intervals with three-month periods — seq_by_3_month","text":"","code":"seq_by_3_month(start_date, end_date)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/seq_by_3_month.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate sequence of intervals with three-month periods — seq_by_3_month","text":"start_date Date. Start date \"yyyy-mm-dd\" format. end_date Date. End date \"yyyy-mm-dd\" format.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/seq_by_3_month.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate sequence of intervals with three-month periods — seq_by_3_month","text":"Dataframe start end columns, row represents 3 months period.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/seq_by_3_month.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate sequence of intervals with three-month periods — seq_by_3_month","text":"","code":"seq_by_3_month(as.Date('2023-11-01'),as.Date('2024-11-01')) #> start end #> 1 2023-11-01 2024-02-01 #> 2 2024-02-01 2024-05-01 #> 3 2024-05-01 2024-08-01 #> 4 2024-08-01 2024-11-01"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/set_global_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to set up the global variables for public holidays and vacations, with the default\nbeing the french dates from a governmental API. — set_global_vars","title":"Function to set up the global variables for public holidays and vacations, with the default\nbeing the french dates from a governmental API. — set_global_vars","text":"Function set global variables public holidays vacations, default french dates governmental API.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/set_global_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to set up the global variables for public holidays and vacations, with the default\nbeing the french dates from a governmental API. — set_global_vars","text":"","code":"set_global_vars(vacations = NULL, public_holidays = NULL)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/set_global_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to set up the global variables for public holidays and vacations, with the default\nbeing the french dates from a governmental API. — set_global_vars","text":"vacations data frame containing vacation dates public_holidays data frame containing public holidays dates","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/set_global_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to set up the global variables for public holidays and vacations, with the default\nbeing the french dates from a governmental API. — set_global_vars","text":"return anything, set global variables public holidays vacations.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/set_global_vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to set up the global variables for public holidays and vacations, with the default\nbeing the french dates from a governmental API. — set_global_vars","text":"","code":"pkg.globals <- new.env(parent = emptyenv()) set_global_vars() print(pkg.globals$vacations) #> NULL"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/set_telraam_token.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves an authentication token for the 'Telraam' API. — set_telraam_token","title":"Saves an authentication token for the 'Telraam' API. — set_telraam_token","text":"want get token instruction, please use get_telraam_token().","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/set_telraam_token.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves an authentication token for the 'Telraam' API. — set_telraam_token","text":"","code":"set_telraam_token(token)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/set_telraam_token.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves an authentication token for the 'Telraam' API. — set_telraam_token","text":"token string token","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/set_telraam_token.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Saves an authentication token for the 'Telraam' API. — set_telraam_token","text":"Boolean: TRUE token correctly set","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/set_telraam_token.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Saves an authentication token for the 'Telraam' API. — set_telraam_token","text":"","code":"my_token <- \"MyTelraamToken\" set_telraam_token(my_token) get_telraam_token() #> [1] \"MyTelraamToken\""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/simple_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of cars and Heavies as a function of time — simple_plot","title":"Number of cars and Heavies as a function of time — simple_plot","text":"Number cars Heavies function time","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/simple_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of cars and Heavies as a function of time — simple_plot","text":"","code":"simple_plot( data, sensor, date_range, line_color = \"black\", smooth_color = \"#B1D62E\" )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/simple_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of cars and Heavies as a function of time — simple_plot","text":"data data.frame containing data sensors sensor numeric, Id chosen sensor date_range Date vector. example: c('2021-01-01','2022-01-01') line_color character. color main line. Default black smooth_color character. color smoothing line. Default light green","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/simple_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of cars and Heavies as a function of time — simple_plot","text":"graph showing evolution number cars heavies selected date range chosen sensor.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/traffic.html","id":null,"dir":"Reference","previous_headings":"","what":"Traffic data of Telraam sensors in Châteaubourg (FR) — traffic","title":"Traffic data of Telraam sensors in Châteaubourg (FR) — traffic","text":"Telraam sensors continously monitor street form citizen window. count heavy vehicules, cars, twok-wheelers pedestrians, every hour. Châteaubourg one city France highest density sensors dataframe subset sensors data Châteaubourg 2022. Additional properties present natively Telraam API added package.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/traffic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Traffic data of Telraam sensors in Châteaubourg (FR) — traffic","text":"","code":"traffic"},{"path":[]},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/traffic.html","id":"traffic","dir":"Reference","previous_headings":"","what":"traffic","title":"Traffic data of Telraam sensors in Châteaubourg (FR) — traffic","text":"data frame 16,729 rows 22 columns: instance_id Sensor number. Equals -1 API request made road segment camera. segment_id Road segment Telraam ID. Equals -1 API request made camera road segment. segment_name Additional property - Segment name specified configuration file. segment_fullname Additional property - Concatenation segment_id segment name specified configuration. date date UTC time reporting interval (beginning interval). day Additional property - Day reporting interval. hour Additional property - Hour reporting interval. weekday Additional property - Weekday reporting interval. holiday Additional property - boolean, indicates whether entry French public holiday. vacation Indicates whether entry French vacation period, true, vacation period name. interval can \"hourly\" \"daily\" hourly daily aggregate data. uptime 0 1, represents portion reporting interval actively spent counting traffic uptime_quality Additional property - boolean, indicates whether entry uptime greather equal 0. heavy, heavy_lft, heavy_rgt number heavy vehicles, total directions. car, car_lft, car_rgt number cars, total directions. bike, bike_lft, bike_rgt number two-wheelers, total directions. pedestrian, pedestrian_lft, pedestrian_rgt number pedestrians, total directions. direction 1, internal consistency value Telraam. car_speed_hist_0to70plus, car_speed_hist_0to120plus estimated car speed distribution 10 km/h bins 0 70+ km/h 120+ km/h (percentage total 100%). timezone name Time zone segment can found. v85 estimated car speed limit km/h 85% cars respect","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/traffic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Traffic data of Telraam sensors in Châteaubourg (FR) — traffic","text":"https://telraam-api.net/","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/which_vacations.html","id":null,"dir":"Reference","previous_headings":"","what":"Indicates if a date is in vacation period and if true, which vacation.\nIf the date is not in a vacation period, ","title":"Indicates if a date is in vacation period and if true, which vacation.\nIf the date is not in a vacation period, ","text":"Indicates date vacation period true, vacation. date vacation period, \"vacation\" returned.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/which_vacations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indicates if a date is in vacation period and if true, which vacation.\nIf the date is not in a vacation period, ","text":"","code":"which_vacations(date, vacation)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/which_vacations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indicates if a date is in vacation period and if true, which vacation.\nIf the date is not in a vacation period, ","text":"date Date (character format) vacation Dataframe vacations, format set_globals_vars output.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/which_vacations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indicates if a date is in vacation period and if true, which vacation.\nIf the date is not in a vacation period, ","text":"Vacation description day two dates, \"vacation\" otherwise.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/write_update_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Write or update the sensor data in the data folder — write_update_data","title":"Write or update the sensor data in the data folder — write_update_data","text":"Writes updates sensor data data folder. retrieves data specified sensor start_date end_date (inclusive) using retrieve_sensor function, converts certain columns character strings writing data RData file data folder.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/write_update_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write or update the sensor data in the data folder — write_update_data","text":"","code":"write_update_data(segment_name, start_date, end_date)"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/write_update_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write or update the sensor data in the data folder — write_update_data","text":"segment_name Character. Name segment, specified config. start_date Date. Start date \"aaaa-mm-jj\" end_date Date. End date \"aaaa-mm-jj\"","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/write_update_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write or update the sensor data in the data folder — write_update_data","text":"Boolean: TRUE data well saved/written, FALSE otherwise (data example)","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/write_update_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Write or update the sensor data in the data folder — write_update_data","text":"","code":"if (FALSE) { period <- as.Date(c('2022-01-01', '2022-12-31')) write_update_data('RteVitre-06', period[1], period[2]) }"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/write_update_data_comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Write or update the sensor data in the data folder — write_update_data_comp","title":"Write or update the sensor data in the data folder — write_update_data_comp","text":"function writes updates sensor data data folder. retrieves data specified sensor start_date end_date (inclusive) using retrieve_sensor function, converts certain columns character strings writing data CSV file data folder.","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/write_update_data_comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write or update the sensor data in the data folder — write_update_data_comp","text":"","code":"write_update_data_comp( segment_name, start_date, end_date, vacations = NULL, public_holidays = NULL )"},{"path":"https://ketsiaguichard.github.io/telraamStats/reference/write_update_data_comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write or update the sensor data in the data folder — write_update_data_comp","text":"segment_name Character. Name segment, specified config. start_date Date. Start date \"aaaa-mm-jj\" end_date Date. End date \"aaaa-mm-jj\" vacations vacation periods, set default french ones public_holidays public holidays list, set default french ones","code":""},{"path":"https://ketsiaguichard.github.io/telraamStats/news/index.html","id":"telraamstats-110","dir":"Changelog","previous_headings":"","what":"telraamStats 1.1.0","title":"telraamStats 1.1.0","text":"Initial CRAN submission.","code":""}] diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 6da4032..32d6526 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -9,6 +9,9 @@ https://ketsiaguichard.github.io/telraamStats/articles/data-details.html + + https://ketsiaguichard.github.io/telraamStats/articles/data-visualization.html + https://ketsiaguichard.github.io/telraamStats/articles/data_visualization.html diff --git a/vignettes/data_visualization.Rmd b/vignettes/data-visualization.Rmd similarity index 100% rename from vignettes/data_visualization.Rmd rename to vignettes/data-visualization.Rmd