Rthingsboard

The goal of ‘Rthingsboard’ is to provide interaction with the API of ‘ThingsBoard’ (https://thingsboard.io/), an open-source IoT platform for device management, data collection, processing and visualization.

Installation

You can install the released version of ‘Rthingsboard’ from CRAN with:

install.packages("Rthingsboard")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("DDorch/Rthingsboard")

Example

This is a basic example which shows you how to extract data from the following public dashboard : http://scada.g-eau.fr/dashboard/4db16100-f3e9-11e8-9dbf-cbc1e37c11e3?publicId=299cedc0-f3e9-11e8-9dbf-cbc1e37c11e3

Load library

library(Rthingsboard)

Define the configuration parameters

# Identifier of SupAgro Halle hydraulique SCADA
url = "http://scada.g-eau.fr"
publicId = "299cedc0-f3e9-11e8-9dbf-cbc1e37c11e3"
entityId = "18d56d50-f3e9-11e8-9dbf-cbc1e37c11e3"
startDate = as.POSIXct("2020-11-19 15:00:00", tz = "Europe/Paris")
endDate = as.POSIXct("2020-11-19 18:00:00", tz = "Europe/Paris")

# Set logger threshold to DEBUG to see extra messages for debug purpose
logger::log_threshold(logger::DEBUG)

Connexion to the ‘ThingsBoard’ server

First, you need to create an object of class ThingsboardApi as follow:

# Connection to the API
tb_api = ThingsboardApi(url = url, publicId = publicId)

Retrieve data from the ‘ThingsBoard’ server

You can get the available keys on the specified device defined by its entityId:

# Get list of keys
keys = tb_api$getKeys(entityId = entityId)

Knowing the name of the available keys, you can get the telemetry of this device for a given period defined by startTS and endTS.

Here below, we download the telemetry for all keys beginning by “Y”:

df <- tb_api$getTelemetry(entityId,
                       keys = keys[grep("^Y", keys)],
                       startTs = startDate,
                       endTs = endDate)

Here below the first records of the extracted telemetry:

knitr::kable(head(df))

key	ts	value
Y0	2020-11-19 17:59:55	2.9683
Y0	2020-11-19 17:59:50	2.7493
Y0	2020-11-19 17:59:45	2.6398
Y0	2020-11-19 17:59:40	2.7493
Y0	2020-11-19 17:59:35	2.7493
Y0	2020-11-19 17:59:30	2.7493

You can then record this table into a file in the current directory:

# getwd() # to get the path of the current directory
write.csv2(df, "myData.csv")

And also plot some time series:

library(ggplot2)
ggplot(df, aes(x = ts, y = value)) +
  geom_line(aes(color = key), size = 1) +
  scale_color_brewer(palette = "Set1")

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