Download time series from multiple stations/variables

Stijn Van Hoey

2024-12-28

Introduction

In many studies, the interest of the user is to download a batch of time series following on a selection criterion. Examples are:

• downloading air pressure data for the last day for all available measurement stations.
• downloading all measured variables at a frequency of 15 minutes for a given measurement station.

In this vignette, this type of batch downloads is explained, using the available functions of the wateRinfo package in combination with already existing tidyverse functionalities.

library(dplyr)

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##   object 'type_sum.accel' not found

library(ggplot2)

Download all stations for a given variable

Consider the scenario: “downloading air pressure data for the last day for all available measurement stations”. We can achieve this by downloading all the stations information providing air_pressure data (get_stations()) and for each of the ts_id values in the resulting data.frame, applying the get_timeseries_tsid() function:

# extract the available stations for a predefined variable
variable_of_interest <- "air_pressure"
stations <- get_stations(variable_of_interest)

# Download the data for a given period for each of the stations
air_pressure <- stations %>%
    group_by(ts_id) %>%
    do(get_timeseries_tsid(.$ts_id, period = "P1D", to = "2017-01-02")) %>%
    ungroup() %>%
    left_join(stations, by = "ts_id")

As this results in a tidy data set, we can use the power of ggplot to plot the data of the individual measurement stations:

# create a plot of the individual datasets
air_pressure %>% 
    ggplot(aes(x = Timestamp, y = Value)) + 
    geom_point() + xlab("1 Jan 2017") + 
    facet_wrap(c("station_name", "stationparameter_name")) + 
    scale_x_datetime(date_labels = "%H:%M",
                     date_breaks = "6 hours")

Download set of variables from a station

Consider the scenario: “downloading all soil_moisture (in dutch: ‘bodemvocht’) variables at a frequency of 15 minutes for the measurement station Liedekerke”. We can achieve this by downloading all the variables information of the Liedekerke station(get_variables()) using the station code of the waterinfo.be interface (ME07_006), filtering on the P.15 time series and for each of the ts_id values, applying the get_timeseries_tsid() function:

liedekerke_stat <- "ME07_006"
variables <- get_variables(liedekerke_stat)

variables_to_download <- variables %>% 
    filter(parametertype_name == "Bodemvocht") %>%
    filter(ts_name == "P.15")

liedekerke <- variables_to_download %>%
    group_by(ts_id) %>%
    do(get_timeseries_tsid(.$ts_id, period = "P1M", from = "2017-01-01")) %>%
    ungroup() %>%
    left_join(variables, by = "ts_id")

As this results in a tidy data set, we can use the power of ggplot to plot the data of the individual measurement stations:

liedekerke %>% 
    ggplot(aes(x = Timestamp, y = Value)) + 
    geom_line() + xlab("") + ylab("bodemvocht") + 
    facet_wrap(c("ts_name", "stationparameter_name"), scales = "free") +
    scale_x_datetime(date_labels = "%d-%m\n%Y",
                     date_breaks = "10 days")