The API has 20 years of records of weather data. When you need some data, you have to use the API to load them.

For my first exercise, I had to give an usable file with data about the relative humidity throughout the year near to Frasnes-lez-Anvaing.

Find the nearest station

Fisrt, I checked where was Frasnes-lez-Anvaing on the platform to find the nearest station. This station was in Esplechin.

Then, I needed to find the ID of this station to load data from this station. I loaded the list of all the stations and searched for the Esplechin Station’s ID.

# Loading data from API : list of all the stations
stations.df <- prepare_agromet_API_data.fun(
  get_from_agromet_API.fun(
    user_token.chr = Sys.getenv("AGROMET_TOKEN"), # authentification to API
    table_name.chr = "station",
    sensors.chr=NULL,
    stations_ids.chr = "all",                     # load data of all the stations
    api_v.chr="v1",
    test.bool=FALSE
  )
)

Prepare data

Load the recordset

Then, I loaded the data from this station from 2010 to 2015.

# Load the recordset from the nearest station to Frasnes-lez-Anvaing : Esplechin 
records.df <- prepare_agromet_API_data.fun(
  get_from_agromet_API.fun(
    user_token.chr = Sys.getenv("AGROMET_TOKEN"),
    table_name.chr = "cleandata",
    sensors.chr= "hra",                # relative humidity measured
    stations_ids.chr = "38",           # ID of station in Esplechin
    api_v.chr="v2",
    test.bool=FALSE,
    dfrom="2010-01-01",
    dto="2015-01-01"
  )
)

Once data had been loaded, I had records for every hour from 2010 to 2015 : 43 825 records !

Aggregate the recordset

However, the file does not need a hourly, daily records are more convenient. So, I had to aggregate data. Moreover, the objective is to compare relative humidity throughout the year, every day of the year had to be grouped. For example, the January 01st relative humidity measure will be the mean of the measures from each year.

# Calculate the mean hra for each day
library(tidyverse)
records.day.df <- records.df %>%
  mutate(day = as.POSIXlt(as.Date(mtime))$yday) %>%
  group_by(sid, poste, longitude, latitude, altitude, network_name, day) %>%
  summarise(hra_mean_day = mean(hra))
head(records.day.df)
## # A tibble: 6 x 8
## # Groups:   sid, poste, longitude, latitude, altitude, network_name [1]
##   sid   poste  longitude latitude altitude network_name   day hra_mean_day
##   <chr> <chr>      <dbl>    <dbl>    <dbl> <chr>        <int>        <dbl>
## 1 38    Esple…      3.30     50.6      50. pameseb          0         95.4
## 2 38    Esple…      3.30     50.6      50. pameseb          1         93.5
## 3 38    Esple…      3.30     50.6      50. pameseb          2         93.8
## 4 38    Esple…      3.30     50.6      50. pameseb          3         92.1
## 5 38    Esple…      3.30     50.6      50. pameseb          4         92.1
## 6 38    Esple…      3.30     50.6      50. pameseb          5         93.3

Observe the results

Then, we can see the evolution of the relative humidity throughout the year.

Export the result

Finally, we can export the result as CSV file. In that way, people using this file will be able to to other calculs on it.

write.csv(records.day.df, file = "../../static/post/load_data_frasnes_hra_files/records_Esplechin_2010-2015.csv")