What's the R way to do the following group by?

I have some dataset like this:

# date     # value 开发者_开发问答   class
1984-04-01 95.32384   A
1984-04-01 39.86818   B
1984-07-01 43.57983   A
1984-07-01 10.83754   B

Now I would like to group the data by data and subtract the value of class B from class A. I looked into ddply, summarize, melt and aggregate but cannot quite get what I want. Is there a way to do it easily? Note that I have exactly two values per date one of class A and one of class B. I mean i could re-arrange it into two dfs order it by date and class and merge it again, but I feel there is a more R way to do it.

Assuming this data frame (generated as in Prasad's post but with a set.seed for reproducibility):

set.seed(123)
DF <- data.frame( date = rep(seq(as.Date('1984-04-01'), 
                                 as.Date('1984-04-01') + 3, by=1), 
                            1, each=2),
                  class = rep(c('A','B'), 4),
                  value = sample(1:8))

then we consider seven solutions:

1) zoo can give us a one line solution (not counting the library statement):

library(zoo)
z <- with(read.zoo(DF, split = 2), A - B)

giving this zoo series:

> z
1984-04-01 1984-04-02 1984-04-03 1984-04-04 
        -3          3          3         -5 

Also note that as.data.frame(z) or data.frame(time = time(z), value = coredata(z)) gives a data frame; however, you may wish to leave it as a zoo object since it is a time series and other operations are more conveniently done on it in this form, e.g. plot(z)

2) sqldf can also give a one statement solution (aside from the library invocation):

> library(sqldf)
> sqldf("select date, sum(((class = 'A') - (class = 'B')) * value) as value
+ from DF group by date")
        date value
1 1984-04-01    -3
2 1984-04-02     3
3 1984-04-03     3
4 1984-04-04    -5

3) tapply can be used as the basis of a solution inspired by the sqldf solution:

> with(DF, tapply(((class =="A") - (class == "B")) * value, date, sum))
1984-04-01 1984-04-02 1984-04-03 1984-04-04 
        -3          3          3         -5 

4) aggregate can be used in the same way as sqldf and tapply above (although a slightly different solution also based on aggregate has already appeared):

> aggregate(((DF$class=="A") - (DF$class=="B")) * DF["value"], DF["date"], sum)
        date value
1 1984-04-01    -3
2 1984-04-02     3
3 1984-04-03     3
4 1984-04-04    -5

5) summaryBy from the doBy package can provide yet another solution although it does need a transform to help it along:

> library(doBy)
> summaryBy(value ~ date, transform(DF, value = ((class == "A") - (class == "B")) * value), FUN = sum, keep.names = TRUE)
        date value
1 1984-04-01    -3
2 1984-04-02     3
3 1984-04-03     3
4 1984-04-04    -5

6) remix from the remix package can do it too but with a transform and features particularly pretty output:

> library(remix)
> remix(value ~ date, transform(DF, value = ((class == "A") - (class == "B")) * value), sum)
value ~ date
============

+------+------------+-------+-----+
|                           | sum |
+======+============+=======+=====+
| date | 1984-04-01 | value | -3  |
+      +------------+-------+-----+
|      | 1984-04-02 | value | 3   |
+      +------------+-------+-----+
|      | 1984-04-03 | value | 3   |
+      +------------+-------+-----+
|      | 1984-04-04 | value | -5  |
+------+------------+-------+-----+

7) summary.formula in the Hmisc package also has pretty output:

> library(Hmisc)
> summary(value ~ date, data = transform(DF, value = ((class == "A") - (class == "B")) * value), fun = sum, overall = FALSE)
value    N=8

+----+----------+-+-----+
|    |          |N|value|
+----+----------+-+-----+
|date|1984-04-01|2|-3   |
|    |1984-04-02|2| 3   |
|    |1984-04-03|2| 3   |
|    |1984-04-04|2|-5   |
+----+----------+-+-----+

The easiest way I can think of is to use dcast from the reshape2 package, to create a data-frame with one date per row and columns A and B, then use transform to do A-B:

df <- data.frame( date = rep(seq(as.Date('1984-04-01'), 
                                 as.Date('1984-04-01') + 3, by=1), 
                            1, each=2),
                  class = rep(c('A','B'), 4),
                  value = sample(1:8))

require(reshape2)
df_wide <- dcast(df, date  ~ class, value_var = 'value')

> df_wide
        date A B
1 1984-04-01 8 7
2 1984-04-02 6 1
3 1984-04-03 3 4
4 1984-04-04 5 2

> transform( df_wide, A_B = A - B )

        date A B A_B
1 1984-04-01 8 7   1
2 1984-04-02 6 1   5
3 1984-04-03 3 4  -1
4 1984-04-04 5 2   3

In base R, I would approach the problem by using aggregate and sum. This works by converting each value of class B to its negative:

(Using the data provided by @PrasadChalasani)

df <- within(df, value[class=="B"] <- -value[class=="B"])
aggregate(df$value, by=list(date=df$date), sum)

        date x
1 1984-04-01 3
2 1984-04-02 2
3 1984-04-03 2
4 1984-04-04 1

For the record, I like the reshape option the best. Here's a plyr option using summarise:

library(plyr)

ddply(df, "date", summarise
    , A = value[class == "A"]
    , B = value[class == "B"]
    , A_B = value[class == "A"] - value[class == "B"]
)