I am planning to do a very specific prediction model using some statistical learning techniques in R. I am using some weather data that I obtain using Mathematica. However, I haven't been able to export it properly and every question over here seems either too advanced or too basic to apply to my problem. I have a location (lat, long), say for this example equal to (10,-10) that I need to get the maximum and minimum temperatures per day and the total precipitation as well.

MaxTemp = WeatherData[{10, -10}, "MaxTemperature", {{1990, 1, 1}, {2016, 12, 31}, "Day"}]
MinTemp = WeatherData[{10, -10}, "MinTemperature", {{1990, 1, 1}, {2016, 12, 31}, "Day"}]
TotalPrec = WeatherData[{10, -10}, "TotalPrecipitation", {{1990, 1, 1}, {2016, 12, 31}, "Day"}]

Now that I have these three variables, how can I export them in an easy to use format (.csv, .xslx) for R? Is this straightforward? Ideally I would like a dataset with the three of them, but I have no idea how to produce it.



1 Answer 1


I haven't looked at other questions/answers on this site because you stated that what you found didn't help you with your issue at hand; instead I will show you how to treat practically any issue in Mathematica: starting small and building from there

Let's look at something representative of the kind of data you try to deal with

example = WeatherData[{10, -10}, "MaxTemperature", {{2016, 1, 1}, {2016, 1, 31}, "Day"}]

The output of which is


From the Details and Options-section in the documentation of TimeSeries one can learn that the time series property "DatePath" gives a list of date-value pairs



Note the special formatting of the dates as little panels and the brownish font color of the temperatures. The formatting indicates that these are not mere numbers but build-in objects. To export those in a format appropriate for your needs we need to transform them somehow.

Typing "date" and "unit" into documentation search should eventually lead you to DateObject and Quantity and their related functions.

I wrote these two simple function definitions for converting dates and units with a little help from the documentation

format[date_DateObject] := DateValue[date, {"Day", "Month", "Year" }]
format[value_Quantity] := QuantityMagnitude[value]


and added another definition for {date, temperature}-pairs

format[{date_DateObject, value_Quantity}] := {format@date, format@value} //Flatten

Assembling the helper functions and methods

The steps above can be packaged in a function like

convertToList[timeSeries_TemporalData] := timeSeries["DatePath"] // Map@format 

that converts TimeSeries-objects into a list of lists of the form {day, month, year, value}

Exporting this is as easy as

Export["example.csv", example //convertToList]

Treatment for missing data

I noticed that the temperature values for some dates are missing; take for example the 7. of August 1982. Luckily TimeSeries has a build-in method for handling missing data called MissingDataMethod.

This can be packaged in another helper function such as

treatMissing[data_TemporalData] := TimeSeries[data, 
MissingDataMethod -> {"Interpolation", InterpolationOrder -> 1}]

The export command then becomes

Export["example.csv", example //treatMissing //convertToList]


We wrote our own little domain specific language for transforming time series into .csv-files. Especially the Postfix-Syntax makes this really shine since you can combine arbitrary complex operations into sentence-like syntax

example //treatMissing  (* //doSomeStuff *) //convertToList (* //doSomeMore *)

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