July 24, 2019 at 12:00 am
Comments posted to this topic are about the item Going from long to wide
July 24, 2019 at 12:59 pm
Good reminder, thanks Steve
Rusty is not the word....
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Space, the final frontier? not any more...
All limits henceforth are self-imposed.
“libera tute vulgaris ex”
July 24, 2019 at 1:34 pm
A new one for me, still not sure I completely get how it works. Need to play with it a bit.
July 25, 2019 at 12:33 pm
The choice of "4 columns 3 rows" does not appear to be correct, but it is the closest, so this is why I chose it. The correct choice should probably read 5 columns 3 rows. The original dataframe named speaking has 4 columns and 9 rows. Pivoting it produces 3 rows and 5 columns: original ID and Name and also 3 columns for years (2015, 2016 and 2017). The events wrap under the years becoming cell values. Interestingly enough, the explanation includes the entire dataframe spelled out. It has 5 columns, as expected (not 4), plus the index of course. Calling str on it clearly states that the dataframe has 5 columns, not 4:
> str(widespeaking)
'data.frame': 3 obs. of 5 variables:
$ ID : int 1 2 3
$ Name: Factor w/ 3 levels "Steve","Grant",..: 1 2 3
$ 2015: num 32 24 34
$ 2016: num 18 32 32
$ 2017: num 32 31 19
Asking for column names returns a vector with 5 items:
> colnames(widespeaking)
[1] "ID" "Name" "2015" "2016" "2017"
I am not sure what, if anything, is wrong with my understanding. It really looks like the dataframe has 5 columns, not 4.
Oleg
August 5, 2019 at 3:43 pm
Ah, I didn't think the ID was counted, but as it's in every dataframe. I'll correct that.
Renaming the variable as well.
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