Clt+A to select all the rows, go to Data>Text to Columns (Alt+A+E) and select comma separator and press Enter. Step 4: Now that you have the data, we can format our excel file. Keep appending the data below (don’t paste header every time). Step 3: Once you edit the link, paste it in your browser and open it. So you need to adjust the start date, end date will always be same for all iterations. Here: FACT is the station id, 2013 is start year, 3 is start month and 1 is start day and then dayend, monthend and yearend are your end date. FACT//CustomHistory.html?dayend= 31&monthend= 12&yearend= 2016&req_city=NA&req_state=NA&req_statename=NA&format=1 I think in one go it allows to download 370 rows, say 1 year. You have to manually adjust the dates and download 3-4 times depending on the length of timeframe. Please note when you try to download the data for a couple of years, it won’t come in one go. I make them bold what you need to replace. Now go to “Histroy” tab and then you can see the code (circled below), it is “FACT”. Visit Weather Underground website and search for the city. Each city has one station installed by WU and it has a unique identity. Step 1: We will first learn how to find the station code. I am using this source from a couple of years. And we will choose “Weather Underground” as our source. So today I am going to write an R code and some manual trick to download the temperature data for different cities.įirst, I will write how to download the data manually as the R code is not very efficient in extracting data for all variables. To understand the relation between temperature and demand we need historical data. Forecasting of FMCG products is also very important for manufacturer and temperature play again an important role to understand the demand. So dairy products manufacturers have to adjust their storage of raw material and production of final products in such a way which can minimize the operational cost. Milk which is the major component for ice cream is heavily produced during winter as cows produce more in this season but consumed more in summer. It is a very interesting case study to read upon. Weather is an important factor for fast moving consumer goods product.

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