CODEWITHSUNDEEP
rdata.tableaggregateplyrdata-manipulation
EdM
EdM

Reputation: 164

Aggregate one data frame by time intervals from another data frame

I'm trying to aggregate two data frames (df1 and df2).

The first contains 3 variables: ID, Date1 and Date2.

df1

ID      Date1      Date2
 1 2016-03-01 2016-04-01
 1 2016-04-01 2016-05-01
 2 2016-03-14 2016-04-15
 2 2016-04-15 2016-05-17
 3 2016-05-01 2016-06-10
 3 2016-06-10 2016-07-15

The second also contains 3 variables: ID, Date3 and Value.

df2

ID      Date3 Value
 1 2016-03-15     5
 1 2016-04-04     7
 1 2016-04-28     7
 2 2016-03-18     3
 2 2016-03-27     5
 2 2016-04-08     9
 2 2016-04-20     2
 3 2016-05-05     6
 3 2016-05-25     8
 3 2016-06-13     3

The idea is to get, for each df1 row, the sum of df2$Value that have the same ID and for which Date3 is between Date1 and Date2:

ID      Date1      Date2 SumValue
 1 2016-03-01 2016-04-01        5
 1 2016-04-01 2016-05-01       14
 2 2016-03-14 2016-04-15       17
 2 2016-04-15 2016-05-17        2
 3 2016-05-01 2016-06-10       14
 3 2016-06-10 2016-07-15        3

I know how to make a loop on this, but the data frames are huge! Does someone has an efficient solution? Exploring data.table, plyr and dplyr but could not find a solution.

Upvotes: 6

Views: 496

Answers (3)

Arun
Arun

Reputation: 118799

With the recently implemented non-equi joins feature in the current development version of data.table, v1.9.7, this can be done as follows:

dt2[dt1, .(sum = sum(Value)), on=.(ID, Date3>=Date1, Date3<=Date2), by=.EACHI]
#    ID      Date3      Date3 sum
# 1:  1 2016-03-01 2016-04-01   5
# 2:  1 2016-04-01 2016-05-01  14
# 3:  2 2016-03-14 2016-04-15  17
# 4:  2 2016-04-15 2016-05-17   2
# 5:  3 2016-05-01 2016-06-10  14
# 6:  3 2016-06-10 2016-07-15   3

The column names needs some fixing.. will work on it later.

Upvotes: 4

SymbolixAU
SymbolixAU

Reputation: 26258

A couple of data.table solutions that should scale well (and a good stop-gap until non-equi joins are implemented):

Do the comparison in J using by=EACHI.

library(data.table)
setDT(df1)
setDT(df2)

df1[, `:=`(Date1 = as.Date(Date1), Date2 = as.Date(Date2))]
df2[, Date3 := as.Date(Date3)]

df1[  df2,
      {
        idx = Date1 <= i.Date3 & i.Date3 <= Date2
        .(Date1 = Date1[idx],
          Date2 = Date2[idx],
          Date3 = i.Date3,
          Value = i.Value)
      }, 
      on=c("ID"),
      by=.EACHI][, .(sumValue = sum(Value)), by=.(ID, Date1, Date2)]

#   ID      Date1      Date2 sumValue
# 1:  1 2016-03-01 2016-04-01        5
# 2:  1 2016-04-01 2016-05-01       14
# 3:  2 2016-03-14 2016-04-15       17
# 4:  2 2016-04-15 2016-05-17        2
# 5:  3 2016-05-01 2016-06-10       14
# 6:  3 2016-06-10 2016-07-15        3

foverlap join (as suggested in the comments)

library(data.table)
setDT(df1)
setDT(df2)

df1[, `:=`(Date1 = as.Date(Date1), Date2 = as.Date(Date2))]
df2[, Date3 := as.Date(Date3)]

df2[, Date4 := Date3]


setkey(df1, ID, Date1, Date2)


foverlaps(df2,
          df1,
          by.x=c("ID", "Date3", "Date4"),
          type="within")[, .(sumValue = sum(Value)), by=.(ID, Date1, Date2)]

#     ID      Date1      Date2 sumValue
# 1:  1 2016-03-01 2016-04-01        5
# 2:  1 2016-04-01 2016-05-01       14
# 3:  2 2016-03-14 2016-04-15       17
# 4:  2 2016-04-15 2016-05-17        2
# 5:  3 2016-05-01 2016-06-10       14
# 6:  3 2016-06-10 2016-07-15        3

Further reading

Rolling join on data.table with duplicate keys

foverlap joins in data.table

Upvotes: 5

bgoldst
bgoldst

Reputation: 35314

Here's a base R solution using sapply():

df1 <- data.frame(ID=c(1L,1L,2L,2L,3L,3L),Date1=as.Date(c('2016-03-01','2016-04-01','2016-03-14','2016-04-15','2016-05-01','2016-06-01')),Date2=as.Date(c('2016-04-01','2016-05-01','2016-04-15','2016-05-17','2016-06-15','2016-07-15')));
df2 <- data.frame(ID=c(1L,1L,1L,2L,2L,2L,2L,3L,3L,3L),Date3=as.Date(c('2016-03-15','2016-04-04','2016-04-28','2016-03-18','2016-03-27','2016-04-08','2016-04-20','2016-05-05','2016-05-25','2016-06-13')),Value=c(5L,7L,7L,3L,5L,9L,2L,6L,8L,3L));
cbind(df1,SumValue=sapply(seq_len(nrow(df1)),function(ri) sum(df2$Value[df1$ID[ri]==df2$ID & df1$Date1[ri]<=df2$Date3 & df1$Date2[ri]>df2$Date3])));
##   ID      Date1      Date2 SumValue
## 1  1 2016-03-01 2016-04-01        5
## 2  1 2016-04-01 2016-05-01       14
## 3  2 2016-03-14 2016-04-15       17
## 4  2 2016-04-15 2016-05-17        2
## 5  3 2016-05-01 2016-06-15       17
## 6  3 2016-06-01 2016-07-15        3

Note that your df1 and expected output have slightly different dates in some cases; I used the df1 dates.

Here's another approach that attempts to be more vectorized: Precompute a cartesian product of indexes into the two frames, then perform a single vectorized conditional expression using the index vectors to get matching pairs of indexes, and finally use the matching indexes to aggregate the desired result:

cbind(df1,SumValue=with(expand.grid(i1=seq_len(nrow(df1)),i2=seq_len(nrow(df2))),{
    x <- df1$ID[i1]==df2$ID[i2] & df1$Date1[i1]<=df2$Date3[i2] & df1$Date2[i1]>df2$Date3[i2];
    tapply(df2$Value[i2[x]],i1[x],sum);
}));
##   ID      Date1      Date2 SumValue
## 1  1 2016-03-01 2016-04-01        5
## 2  1 2016-04-01 2016-05-01       14
## 3  2 2016-03-14 2016-04-15       17
## 4  2 2016-04-15 2016-05-17        2
## 5  3 2016-05-01 2016-06-15       17
## 6  3 2016-06-01 2016-07-15        3

Upvotes: 1

Related Questions