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My data frame comprises: 1st column is date ( consist of normal and leap year) , 2nd to 32 column is day ( day 1 , day 2... day 31) How to arrange this dataset to a single column.

marc_s
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Kirubel
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    Welcome to StackOverflow! Please read the info about [how to ask a good question](http://stackoverflow.com/help/how-to-ask) and how to give a [reproducible example](http://stackoverflow.com/questions/5963269). This will make it much easier for others to help you. – Sotos Jun 18 '19 at 08:18
  • Sounds like a reshaping issue. Have you tried [here](https://stackoverflow.com/questions/12466493/reshaping-multiple-sets-of-measurement-columns-wide-format-into-single-columns) or [here](https://stackoverflow.com/questions/25925556/gather-multiple-sets-of-columns)? – gersht Jun 18 '19 at 09:38

1 Answers1

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I'm very new to R (and also to SO, at least with answering) but I would use gather from the tidyr package for this task.

It's a bit hard to imagine your data without an example, so I'll make my own (Actually I don't see why there should be a date in the first column and then other columns for every single day):

  Date       day_1 day_2 day_3 day_4 day_5 day_6 ... day_31
  ----       ----- ----- ----- ----- ----- ----- ... ------
1 2019-05-01     1     1     1     0     0     5 ...      6
2 2019-05-02     4     0     2     1     4     5 ...      4
3 2019-05-03     3     2     5     0     5     2 ...      2

You can use gather to convert your columns to rows the following.

gather(Data, key = "Day", value = "Rainfall", 2:32)

The first parameter (Data) is your dataset. The key parameter defines, what name your 'Key' column should have and value defines the name of the value column. The 2:32 part means, that you want columns 2 to 32 included in the result. If you have no other columns than the ones you mentioned you could also use -1 instead of 2:32, which just means to ignore the first colum.

This would give you the following result: 

         Date    Day Rainfall
1  2019-05-01  day_1        1
2  2019-05-02  day_1        4
3  2019-05-03  day_1        3
4  2019-05-01  day_2        1
5  2019-05-02  day_2        0
6  2019-05-03  day_2        2
7  2019-05-01  day_3        1
8  2019-05-02  day_3        2
9  2019-05-03  day_3        5
10 2019-05-01  day_4        0
11 2019-05-02  day_4        1
...
...
...
Martin
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