And the plyr version of this would be (using DF as the data frame name)
## transform method, mapping length(runtime) to all observations
## similar to David's results:
library('plyr')
ddply(DF, .(time, partitioning_mode), transform, n = length(runtime))
# or equivalently, the newer and somewhat faster
ddply(DF, .(time, partitioning_mode), mutate, n = length(runtime))
# If you just want the counts, then use
ddply(DF, .(time, partitioning_mode), summarise, n = length(runtime))
##---------
# Just for fun, here's the equivalent SQL call using sqldf():
library('sqldf')
sqldf('select time partitioning_mode count(*) from DF group by time
partitioning_mode')
# which you can distribute over multiple lines for readability, e.g.
sqldf('select time, partitioning_mode, count(*) as n
from DF
group by time, partitioning_mode')
# Result:
time partitioning_mode n
1 1 replication 4
2 1 sharding 11
##---------
# To do the same type of summary in data.table (to follow up on Jim
Holtman's post), here's one way:
library(data.table)
dt <- data.table(DF, key = 'time, partitioning_mode')
dt[, list(n = length(runtime)), by = key(dt)]
time partitioning_mode n
[1,] 1 replication 4
[2,] 1 sharding 11
###------
HTH,
Dennis
On Sun, Oct 23, 2011 at 10:29 AM, Giovanni Azua <brave...@gmail.com> wrote:
> Hello,
>
> This is one problem at the time :)
>
> I have a data frame df that looks like this:
>
> time partitioning_mode workload runtime
> 1 1 sharding query 607
> 2 1 sharding query 85
> 3 1 sharding query 52
> 4 1 sharding query 79
> 5 1 sharding query 77
> 6 1 sharding query 67
> 7 1 sharding query 98
> 8 1 sharding refresh 2932
> 9 1 sharding refresh 2870
> 10 1 sharding refresh 2877
> 11 1 sharding refresh 2868
> 12 1 replication query 2891
> 13 1 replication query 2907
> 14 1 replication query 2922
> 15 1 replication query 2937
>
> and if I could use SQL ... omg! I really wish I could! I would do exactly
> this:
>
> insert into throughput
> select time, partitioning_mode, count(*)
> from data.frame
> group by time, partitioning_mode
>
> My attempted R versions are wrong and produce very cryptic error message:
>
>> throughput <- aggregate(x=df[,c("time", "partitioning_mode")],
>> by=list(df$time,df$partitioning_mode), count)
> Error in `[.default`(df2, u_id, , drop = FALSE) :
> incorrect number of dimensions
>
>> throughput <- aggregate(x=df, by=list(df$time,df$partitioning_mode), count)
> Error in `[.default`(df2, u_id, , drop = FALSE) :
> incorrect number of dimensions
>
>>throughput <- tapply(X=df$time, INDEX=list(df$time,df$partitioning),
>>FUN=count)
> I cant comprehend what comes out from this one ... :(
>
> and I thought C++ template errors were the most cryptic ;P
>
> Many many thanks in advance,
> Best regards,
> Giovanni
> ______________________________________________
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>
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