Sorting and aggregating in R - r

Question

I used the aggregate function in R to bring down my data entries from 90k to 1800.
a=test$ID
b=test$Date
c=test$Value
d=test$Value1
sumA=aggregate(c, by=list(Date=b,Id=a), FUN=sum)
sumB=aggregate(d, by=list(Date=b,Id=a), FUN=sum)
final[1]=sumA[1],final[2]=sumA[2]
final[3]=sumA[3]/sumB[3]
Now I have data in 20 different dates in a month with close to 90 different ids each day so its around 1800 entries in the final table .
My question is that I want to aggregate further down and find the maximum value of final[3] for each date so that I am just left with 20 values .
In simple terms -
There are 20 days .
Each day has 90 values for 90 ids
I want to find maximum of these 90 values for each day .
So at last I would be left with just 20 values for 20 days .
Now aggregate function is not working here with function 'max' instead of sum
Date ID Value Value1
1 A 20 10
1 A 25 5
1 B 50 5
1 B 50 5
1 C 25 25
1 C 35 5
2 A 30 10
2 A 25 45
2 B 40 10
2 B 40 30
This is the Data
Now By using Aggregate function I got final table as
Date ID x
1 A 45/15=3
1 B 100/10=10
1 c 60/30=2
2 A 55/55=1
2 B 80/40=2
Now I want maximum value for date 1 and 2 thats it
Date max- Value
1 10
2 2

Answer 1

This is a one step process using data table. The data.table is an evolved version of data.frame, and works really well. It has the class of data.frame, so works just like data.frame.
Step0: Converting data.frame to data.table:
library(data.table)
setDT(test)
setkey(test,Date,ID)
Step1: Do the computation
test[,sum(Value)/sum(Value1),by=key(test)][,max(V1),by=Date]
Here the explanation of the step:
The first part creates what you call the final table in your question:
test[,sum(Value)/sum(Value1),by=key(test)]
# Date ID V1
# 1: 1 A 3
# 2: 1 B 10
# 3: 1 C 2
# 4: 2 A 1
# 5: 2 B 2
Now this is passed to the second item to do the max function by Date:
test[,sum(Value)/sum(Value1),by=key(test)][,max(V1),by=Date]
# Date V1
# 1: 1 10
# 2: 2 2
Hope this helps.
It's a very well documented package. You should read more about it.

Answer 2

May be this helps.
test <- structure(list(Date = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L
), ID = c("A", "A", "B", "B", "C", "C", "A", "A", "B", "B"),
Value = c(20L, 25L, 50L, 50L, 25L, 35L, 30L, 25L, 40L, 40L
), Value1 = c(10L, 5L, 5L, 5L, 25L, 5L, 10L, 45L, 10L, 30L
)), .Names = c("Date", "ID", "Value", "Value1"), class = "data.frame", row.names = c(NA,
-10L))
res1 <- aggregate(. ~ID+Date, data=test, FUN=sum)
res1 <- transform(res1, x=Value/Value1)
res1
# ID Date Value Value1 x
#1 A 1 45 15 3
#2 B 1 100 10 10
#3 C 1 60 30 2
#4 A 2 55 55 1
#5 B 2 80 40 2
aggregate(. ~Date, data=res1[,-c(1,3:4)], FUN=max)
# Date x
# 1 1 10
# 2 2 2
First I run the aggregate based on two grouping variables (ID and Date) on the two value column by using. ~`
Created a new variable x i.e. Value/Value1 with transform
Did the final run of aggregate with one grouping variable (Date) and removed the rest of the variables except x.