[R] making dataframes

[Bill.Venables at csiro.au]

Firstly, the way you have constructed your data frame in the example will convert everything to factors.  What you need to do is actually a bit simpler:

#######
dum <- data.frame(date, col1, col2)
#######

One way to turn this into the kind of data frame you want is to convert the main part of it to a table first, and then coerce into a data frame:

#######
tab <- as.table(as.matrix(dum[, -1]))
row.names(tab) <- date
names(dimnames(tab)) <- c("date", "category")
Dum <- as.data.frame(tab, responseName = "rainfall")
Dum$date <- factor(Dum$date, levels = date)
#######

Here is a checK:

> head(Dum)

  date category rainfall
1  jan     col1      8.2
2  feb     col1      5.4
3  mar     col1      4.3
4  apr     col1      4.1
5  may     col1      3.1
6 june     col1      2.5

> with(Dum, tapply(rainfall, date, mean))

 jan  feb  mar  apr  may june july  aug  sep  oct  nov  dec 
5.65 3.85 4.50 5.50 5.30 1.80 2.35 6.50 5.35 2.20 5.95 4.40 


Bill Venables.

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of pelt
Sent: Thursday, 17 March 2011 12:28 AM
To: r-help at r-project.org
Subject: [R] making dataframes

Dear all,

I have a dataframe which looks like this (dummy):

date<-c("jan", "feb", "mar", "apr", "may", "june", "july", 
"aug","sep","oct","nov","dec")
col1<-c(8.2,5.4,4.3,4.1,3.1,2.5,1.1,4.5,3.2,1.9,7.8,6.5)
col2<-c(3.1,2.3,4.7,6.9,7.5,1.1,3.6,8.5,7.5,2.5,4.1,2.3)
dum<-data.frame(cbind(date,col1,col2))
dum
   date col1 col2
1   jan  8.2  3.1
2   feb  5.4  2.3
3   mar  4.3  4.7
4   apr  4.1  6.9
5   may  3.1  7.5
6  june  2.5  1.1
7  july  1.1  3.6
8   aug  4.5  8.5
9   sep  3.2  7.5
10  oct  1.9  2.5
11  nov  7.8  4.1
12  dec  6.5  2.3

I would like to convert this data.frame into something that looks like this:
   date rainfall category
1   jan  8.2  col1
2   feb  5.4  col1
3   mar  4.3  col1
4   apr  4.1  col1
5   may  3.1  col1
6  june  2.5  col1
7  july  1.1  col1
8   aug  4.5  col1
9   sep  3.2  col1
10  oct  1.9  col1
11  nov  7.8  col1
12  dec  6.5  col1
1   jan   3.1 col2
2   feb  2.3 col2
3   mar  4.7 col2
4   apr   6.9 col2
5   may   7.5 col2
6  june   1.1 col2
7  july    3.6 col2
8   aug   8.5 col2
9   sep   7.5 col2
10  oct  2.5 col2
11  nov  4.1 col2
12  dec  2.3 col2

So the column-names become categories.  The dataset is rather large with 
many columns and a lengthy date-string. Is there an easy way to do this?

Thank you for your help,

Kind regards,

Saskia van Pelt

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