Our interactive map (WunderMap) displays animated radar and satellite data to track storms, as well as providing specific map presets for tracking hurricanes and wildfires.

With free push notifications for weather alerts, Weather Underground is your essential app during severe weather events. Powered by our unique community of weather enthusiasts reporting live data from weather stations in their own backyards, this crowd-sourced data generates forecasts targeted to your precise location. Myfile <- paste('NewportWeather',i,'.csv')ĭownload.file(url = myurl, file.The new Weather Underground Android app provides the world's most accurate hyper-local weather forecasts in addition to interactive radar, satellite maps and severe weather alerts. The code finished as this: start_date <- as.POSIXct('',tz='gmt') I also had to chance length(my_dates) to nrow(my_dates) in the for loop. myfile <- paste('NewportWeather',i,'.csv') You will need to change the folder the file is downloaded into to run the code.Īs pointed out, i needed to incorporate i into the filename which I found can be done like this. Is anyone able to help me download all data for the entire date range in either separate files, or all appended onto the same file. When running this, only data for one day is downloaded in the correct file location, not all of them in the date range.

Myfile <- paste('NewportWeather',rownames(my_dates),'.csv')ĭownload.file(url = myurl, file.path(folder, myfile, fsep = '\' )) My_dates<-as.integer(unlist(strsplit(as.character(dates),'-')))

Where i is a date between the start and end.Įnd_date <- as.POSIXct(Sys.Date(),tz='gmt')ĭates <- seq.POSIXt(start_date,end_date,by='day') I am running a for loop for the url: myurl <- paste('',station,'&day=',my_dates$day,'&month=',my_dates$month,'&year=',my_dates$year,'&graphspan=day&format=1', sep = '') In order to speed up the run time I am running the code from the 1st of September 2019 to today's date (I will actually need to run for a period of a few years).

I am trying to download historical data from weather underground into csv format by running a for loop with iterations for each date.

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