weatherdatagrab
Richard_Rowe
2025-03-23
Extract 3 sets of data from 2 different sources to obtain weather
and tide data for a Tableau dashboard
Licence for data sources are given at end of document.
- Extract a current forcast for a location from open-meteo.com
- Extract a 7 day forecast for a location from open-meteo.com
- Extact tidal information for a location from UK Hydrographic office
The 3 csv files can then be fed into a Tablea dashboard.
These API requests can be set to run automaticaly by creating a script file with the code chunks and then running it automatically in windows scheduler to obtain fresh data as required.
#### Step 1.
API request for given site Lat Long to obtain current weather data and save to csv file
library(httr)
library(jsonlite)
library(dplyr)
# API request setup
api_url <- "https://api.open-meteo.com/v1/forecast"
params <- list(
latitude = 50.3577,
longitude = -4.6934,
current = paste(
"temperature_2m",
"relative_humidity_2m",
"apparent_temperature",
"is_day",
"wind_speed_10m",
"wind_direction_10m",
"wind_gusts_10m",
"precipitation",
"rain",
"showers",
"snowfall",
"weather_code",
"pressure_msl",
"cloud_cover",
"surface_pressure",
sep = ","
),
forecast_days = 1
)
# Make the API call
response <- GET(api_url, query = params)
data <- content(response, as = "parsed", simplifyVector = TRUE)
# Extract current data
current_data <- data$current
current_df <- data.frame(
time = current_data$time,
temperature_2m = current_data$temperature_2m,
relative_humidity_2m = current_data$relative_humidity_2m,
apparent_temperature = current_data$apparent_temperature,
is_day = current_data$is_day,
wind_speed_10m = current_data$wind_speed_10m,
wind_direction_10m = current_data$wind_direction_10m,
wind_gusts_10m = current_data$wind_gusts_10m,
precipitation = current_data$precipitation,
rain = current_data$rain,
showers = current_data$showers,
snowfall = current_data$snowfall,
weather_code = current_data$weather_code,
pressure_msl = current_data$pressure_msl,
cloud_cover = current_data$cloud_cover,
surface_pressure = current_data$surface_pressure
)
# Save current data to CSV for Tableau
write.csv(current_df, "current_weather_data.csv", row.names = FALSE)
print("Data saved to current_weather_data.csv")## [1] "Data saved to current_weather_data.csv"# Display the first rows of the dataframe
head(current_df)## time temperature_2m relative_humidity_2m apparent_temperature
## 1 2025-03-27T15:45 11 94 10.1
## is_day wind_speed_10m wind_direction_10m wind_gusts_10m precipitation rain
## 1 1 14 179 20.9 0 0
## showers snowfall weather_code pressure_msl cloud_cover surface_pressure
## 1 0 0 3 1020 100 1015.6Step 2
API request to obtain 7 day forecast for given site Lat Long and save to csv file.
#library(httr)
#library(jsonlite)
#library(dplyr)
# API request setup
api_url <- "https://api.open-meteo.com/v1/forecast"
params <- list(
latitude = 50.3577,
longitude = -4.6934,
daily = paste(
"sunset",
"sunrise",
sep = ","
),
hourly = paste(
"temperature_2m",
"relative_humidity_2m",
"apparent_temperature",
"precipitation_probability",
"precipitation",
"rain",
"snowfall",
"weather_code",
"pressure_msl",
"surface_pressure",
"cloud_cover",
"visibility",
"wind_speed_10m",
"wind_direction_10m",
"wind_gusts_10m",
"uv_index",
"is_day",
"dew_point_2m",
"showers",
sep = ","
),
models = "best_match",
timezone = "Europe/London",
past_days = 2
)
# Make the API call and parse the JSON response
response <- GET(api_url, query = params)
data <- content(response, as = "parsed", simplifyVector = TRUE)
# Process hourly data and rename columns for Tableau
hourly <- data$hourly
hourly_df <- data.frame(
"Forecast Time" = as.POSIXct(hourly$time, format = "%Y-%m-%dT%H:%M", tz = "UTC"),
"Forecast Temperature 2M" = hourly$temperature_2m,
"Forecast Relative Humidity 2M" = hourly$relative_humidity_2m,
"Forecast Apparent Temperature" = hourly$apparent_temperature,
"Forecast Precipitation Probability" = hourly$precipitation_probability,
"Forecast Precipitation" = hourly$precipitation,
"Forecast Rain" = hourly$rain,
"Forecast Snowfall" = hourly$snowfall,
"forecast_weather_code" = hourly$weather_code,
"Forecast Pressure MSL" = hourly$pressure_msl,
"Forecast Surface Pressure" = hourly$surface_pressure,
"Forecast Cloud Cover" = hourly$cloud_cover,
"Forecast Visibility" = hourly$visibility,
"Forecast Wind Speed 10M" = hourly$wind_speed_10m,
"Forecast Wind Direction 10M" = hourly$wind_direction_10m,
"Forecast Wind Gusts 10M" = hourly$wind_gusts_10m,
"Forecast UV Index" = hourly$uv_index,
"Forecast Is Day" = hourly$is_day,
"Dew Point 2M" = hourly$dew_point_2m,
"Forecast Showers" = hourly$showers
)
# Process is day/night data
daily <- data$daily
daily_df <- data.frame(
date = as.POSIXct(daily$time, format = "%Y-%m-%d", tz = "UTC"),
sunset = daily$sunset,
sunrise = daily$sunrise
)
# Print a preview of data
print(head(hourly_df))## Forecast.Time Forecast.Temperature.2M Forecast.Relative.Humidity.2M
## 1 2025-03-25 00:00:00 5.2 95
## 2 2025-03-25 01:00:00 5.0 96
## 3 2025-03-25 02:00:00 5.7 92
## 4 2025-03-25 03:00:00 5.3 91
## 5 2025-03-25 04:00:00 5.0 91
## 6 2025-03-25 05:00:00 4.9 92
## Forecast.Apparent.Temperature Forecast.Precipitation.Probability
## 1 3.9 0
## 2 4.3 0
## 3 3.1 0
## 4 3.6 0
## 5 3.1 0
## 6 2.7 0
## Forecast.Precipitation Forecast.Rain Forecast.Snowfall forecast_weather_code
## 1 0 0 0 3
## 2 0 0 0 3
## 3 0 0 0 3
## 4 0 0 0 3
## 5 0 0 0 3
## 6 0 0 0 3
## Forecast.Pressure.MSL Forecast.Surface.Pressure Forecast.Cloud.Cover
## 1 1023.8 1019.3 100
## 2 1023.9 1019.4 100
## 3 1023.8 1019.3 99
## 4 1023.8 1019.3 100
## 5 1024.0 1019.5 100
## 6 1023.8 1019.3 100
## Forecast.Visibility Forecast.Wind.Speed.10M Forecast.Wind.Direction.10M
## 1 24140 5.4 360
## 2 24140 7.6 335
## 3 24140 8.9 328
## 4 24140 5.8 353
## 5 24140 4.7 23
## 6 24140 3.8 17
## Forecast.Wind.Gusts.10M Forecast.UV.Index Forecast.Is.Day Dew.Point.2M
## 1 11.2 0 0 4.5
## 2 10.1 0 0 4.4
## 3 11.5 0 0 4.5
## 4 10.8 0 0 4.0
## 5 5.8 0 0 3.6
## 6 10.8 0 0 3.7
## Forecast.Showers
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0print(head(daily_df))## date sunset sunrise
## 1 2025-03-25 2025-03-25T18:39 2025-03-25T06:09
## 2 2025-03-26 2025-03-26T18:41 2025-03-26T06:07
## 3 2025-03-27 2025-03-27T18:42 2025-03-27T06:05
## 4 2025-03-28 2025-03-28T18:44 2025-03-28T06:03
## 5 2025-03-29 2025-03-29T18:46 2025-03-29T06:00
## 6 2025-03-30 2025-03-30T18:47 2025-03-30T05:58# Save to CSV for Tableau
write.csv(hourly_df, "forecast_hourly_weather_data.csv", row.names = FALSE)
write.csv(daily_df, "daily_sunrise_sunset_data.csv", row.names = FALSE)
print("Data saved to hourly_weather_data.csv and daily_weather_data.csv")## [1] "Data saved to hourly_weather_data.csv and daily_weather_data.csv"
#### Step 3
API request to UK Hydro office to obtain tide informaton for local site and save to csv file
#library(httr)
#library(jsonlite)
# API URL
url <- "https://admiraltyapi.azure-api.net/uktidalapi/api/V1/Stations/0008/TidalEvents"
# Rquest headers
headers <- add_headers(
`Cache-Control` = "no-cache",
`Ocp-Apim-Subscription-Key` = "810112e61b6543129fd9739f96965786"
)
# API call
response <- GET(url, headers)
# Check the response status code
status_code <- status_code(response)
print(paste("Response Code:", status_code))## [1] "Response Code: 200"# Parse and save data if the request is successful
if (status_code == 200) {
# Parse the JSON content
tidal_data <- content(response, as = "parsed", simplifyVector = FALSE)
# Ensure `tidal_data` is treated as a list of events
tidal_df <- data.frame(
DateTime = sapply(tidal_data, function(x) x$DateTime),
EventType = sapply(tidal_data, function(x) x$EventType),
Height = sapply(tidal_data, function(x) x$Height)
)
# Save to CSV for Tableau
write.csv(tidal_df, "tidal_events_data.csv", row.names = FALSE)
print("Tidal data saved to tidal_events_data.csv")
# Preview the data
print(head(tidal_df))
} else {
# Print error message if the request fails
print("Failed to fetch data. Please check API key and parameters.")
}## [1] "Tidal data saved to tidal_events_data.csv"
## DateTime EventType Height
## 1 2025-03-27T03:42:43.333 HighWater 4.941922
## 2 2025-03-27T10:10:24.167 LowWater 0.895907
## 3 2025-03-27T16:16:15 HighWater 4.895074
## 4 2025-03-27T22:33:05 LowWater 0.858783
## 5 2025-03-28T04:31:02.5 HighWater 5.263757
## 6 2025-03-28T11:00:41.667 LowWater 0.473054Data licences
https://open-meteo.com/en/license
http://www.ukho.gov.uk/copyright/index.aspx#Noncommercialpurpose
#citation(ggplot) #citation(httr) #citation(jsonlite) #citation(dplyr)