R connector to the Meteomatics API
===================================================================================
Meteomatics provides a REST-style API to retrieve historic, current, and forecast data globally. This includes model data and observational data in time series and areal formats. Areal formats are also offered through a WMS/WFS-compatible interface. Geographic and time series data can be combined in certain file formats, such as NetCDF.
You can install the development version of MeteomaticsRConnector from GitHub directly within your R session with:
# install.packages("devtools")
devtools::install_github("meteomatics/R-connector-api")Alternatively, you can clone the git repository to a local directory of your choice, open the installed R project by clicking on MeteomaticsRConnector.Rproj and install the package by clicking on “Build” and then “Install Package”.
The following external R functions are required and need to be
installed:
httr, data.table, lubridate, stringr, jsonlite, sets, tidyr, utils, rlist.
This is a basic example on how to use the
MeteomaticsRConnector::query_api() function:
library(MeteomaticsRConnector)
## Insert the Meteomatics API username and password
username <- 'r-community'
password <- 'Utotugode673'
## Specify the date and time
datetime <- strftime(ISOdatetime(year = as.integer(strftime(lubridate::today(), '%Y')),
month = as.integer(strftime(lubridate::today(), '%m')),
day = as.integer(strftime(lubridate::today(), '%d')),
hour = 00, min = 00, sec = 00, tz = 'UTC'),
format = '%Y-%m-%dT%H:%M:%OSZ', tz = 'UTC')
## Create your final URL to query the 2m temperature in Berlin from the Meteomatics API
url <- paste0(sprintf('https://%s:%s@api.meteomatics.com/', username, password),
datetime, '/t_2m:C/52.520551,13.461804/csv')
## Call the MeteomaticsRConnector::query_api() function
query_api(url, username, password)## Calling URL:
## https://api.meteomatics.com/2023-01-25T00:00:00Z/t_2m:C/52.520551,13.461804/csv
## Response [https://r-community:Utotugode673@api.meteomatics.com/2023-01-25T00:00:00Z/t_2m:C/52.520551,13.461804/csv]
## Date: 2023-01-25 15:35
## Status: 200
## Content-Type: text/csv; charset=utf-8
## Size: 43 B
## validdate;t_2m:C
## 2023-01-25T00:00:00Z;-0.2
This is a basic example on how to use the
MeteomaticsRConnector::query_time_series() function:
library(MeteomaticsRConnector)
## Insert the Meteomatics API username and password
username <- 'r-community'
password <- 'Utotugode673'
## Specify the coordinates of interest
coordinates <- list(c(47.3,9.3), c(47.43,9.4))
## Specify the start and end date
startdate <- ISOdatetime(year = as.integer(strftime(lubridate::today(), '%Y')),
month = as.integer(strftime(lubridate::today(), '%m')),
day = as.integer(strftime(lubridate::today(), '%d')),
hour = 00, min = 00, sec = 00, tz = 'UTC')
enddate <- ISOdatetime(year = as.integer(strftime(lubridate::today(), '%Y')),
month = as.integer(strftime(lubridate::today(), '%m')),
day = as.integer(strftime(lubridate::today(), '%d')) + 1,
hour = 00, min = 00, sec = 00, tz = 'UTC')
## Specify a time interval
interval <- "PT1H"
## Specify the parameter(s) of interest
parameters <- list("t_2m:C", "precip_1h:mm")
## Call the MeteomaticsRConnector::query_time_series() function
query_time_series(coordinates, startdate, enddate, interval, parameters,
username, password)## Calling URL:
## https://api.meteomatics.com/2023-01-25T00:00:00Z--2023-01-26T00:00:00Z:PT1H/t_2m:C,precip_1h:mm/47.3,9.3+47.43,9.4/csv?
## lat lon validdate t_2m:C precip_1h:mm
## 1 47.30 9.3 2023-01-25 00:00:00 -4.2 0
## 2 47.30 9.3 2023-01-25 01:00:00 -4.5 0
## 3 47.30 9.3 2023-01-25 02:00:00 -4.5 0
## 4 47.30 9.3 2023-01-25 03:00:00 -4.2 0
## 5 47.30 9.3 2023-01-25 04:00:00 -4.7 0
## 6 47.30 9.3 2023-01-25 05:00:00 -4.6 0
## 7 47.30 9.3 2023-01-25 06:00:00 -3.6 0
## 8 47.30 9.3 2023-01-25 07:00:00 -4.1 0
## 9 47.30 9.3 2023-01-25 08:00:00 -3.5 0
## 10 47.30 9.3 2023-01-25 09:00:00 -3.4 0
## 11 47.30 9.3 2023-01-25 10:00:00 -2.0 0
## 12 47.30 9.3 2023-01-25 11:00:00 -2.8 0
## 13 47.30 9.3 2023-01-25 12:00:00 -2.4 0
## 14 47.30 9.3 2023-01-25 13:00:00 -2.1 0
## 15 47.30 9.3 2023-01-25 14:00:00 -1.8 0
## 16 47.30 9.3 2023-01-25 15:00:00 -2.1 0
## 17 47.30 9.3 2023-01-25 16:00:00 -2.6 0
## 18 47.30 9.3 2023-01-25 17:00:00 -3.5 0
## 19 47.30 9.3 2023-01-25 18:00:00 -3.9 0
## 20 47.30 9.3 2023-01-25 19:00:00 -4.1 0
## 21 47.30 9.3 2023-01-25 20:00:00 -4.2 0
## 22 47.30 9.3 2023-01-25 21:00:00 -4.4 0
## 23 47.30 9.3 2023-01-25 22:00:00 -5.0 0
## 24 47.30 9.3 2023-01-25 23:00:00 -5.2 0
## 25 47.30 9.3 2023-01-26 00:00:00 -5.5 0
## 26 47.43 9.4 2023-01-25 00:00:00 -2.3 0
## 27 47.43 9.4 2023-01-25 01:00:00 -2.8 0
## 28 47.43 9.4 2023-01-25 02:00:00 -2.7 0
## 29 47.43 9.4 2023-01-25 03:00:00 -2.2 0
## 30 47.43 9.4 2023-01-25 04:00:00 -2.0 0
## 31 47.43 9.4 2023-01-25 05:00:00 -2.1 0
## 32 47.43 9.4 2023-01-25 06:00:00 -2.4 0
## 33 47.43 9.4 2023-01-25 07:00:00 -2.6 0
## 34 47.43 9.4 2023-01-25 08:00:00 -3.6 0
## 35 47.43 9.4 2023-01-25 09:00:00 -1.8 0
## 36 47.43 9.4 2023-01-25 10:00:00 -0.8 0
## 37 47.43 9.4 2023-01-25 11:00:00 -0.2 0
## 38 47.43 9.4 2023-01-25 12:00:00 0.1 0
## 39 47.43 9.4 2023-01-25 13:00:00 0.7 0
## 40 47.43 9.4 2023-01-25 14:00:00 1.0 0
## 41 47.43 9.4 2023-01-25 15:00:00 0.9 0
## 42 47.43 9.4 2023-01-25 16:00:00 0.4 0
## 43 47.43 9.4 2023-01-25 17:00:00 0.3 0
## 44 47.43 9.4 2023-01-25 18:00:00 0.2 0
## 45 47.43 9.4 2023-01-25 19:00:00 0.1 0
## 46 47.43 9.4 2023-01-25 20:00:00 -0.2 0
## 47 47.43 9.4 2023-01-25 21:00:00 -0.5 0
## 48 47.43 9.4 2023-01-25 22:00:00 -0.8 0
## 49 47.43 9.4 2023-01-25 23:00:00 -1.1 0
## 50 47.43 9.4 2023-01-26 00:00:00 -1.5 0
For a start, we recommend to read the documentation of each function,
where additional examples on how to use the R connector are provided. To
read the documentation e.g. for the
MeteomaticsRConnector::query_time_series() function write:
library(MeteomaticsRConnector)
?MeteomaticsRConnector::query_time_series