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I'm trying to add missing lines for "day" and extrapolate the data for "value". In my data each subject ("id") has 2 periods (period 1 and period 2) and values for consecutive days.
An example of my data looks like this:
df <- data.frame(
id = c(1,1,1,1, 1,1,1,1, 2,2,2,2, 2,2,2,2, 3,3,3,3, 3,3,3,3),
period = c(1,1,1,1, 2,2,2,2, 1,1,1,1, 2,2,2,2, 1,1,1,1, 2,2,2,2),
day= c(1,2,4,5, 1,3,4,5, 2,3,4,5, 1,2,3,5, 2,3,4,5, 1,2,3,4),
value =c(10,12,15,16, 11,14,15,17, 13,14,15,16, 15,16,18,20, 16,17,19,29, 14,16,18,20))
For each id and period I am missing data for days 3,2,1,4,1,5, respectively. I want to expand the data to let's say 10 days and extrapolate the data on value column (e.g. with linear regression).
My final df should be something like that:
df2 <- data.frame(
id = c(1,1,1,1,1,1,1, 1,1,1,1,1,1,1, 2,2,2,2,2,2,2, 2,2,2,2,2,2,2, 3,3,3,3,3,3,3, 3,3,3,3,3,3,3),
period = c(1,1,1,1,1,1,1, 2,2,2,2,2,2,2, 1,1,1,1,1,1,1, 2,2,2,2,2,2,2, 1,1,1,1,1,1,1, 2,2,2,2,2,2,2),
day= c(1,2,3,4,5,6,7, 1,2,3,4,5,6,7, 1,2,3,4,5,6,7, 1,2,3,4,5,6,7, 1,2,3,4,5,6,7, 1,2,3,4,5,6,7),
value =c(10,12,13,15,16,17,18, 11,12,14,15,17,18,19, 12,13,14,15,16,18,22, 15,16,18,19,20,22,23, 15,16,17,19,29,39,49, 14,16,18,20,22,24,26))
The most similar example I found doesn't extrapolate by two variables (ID and period in my case), it extrapolates only by year. I tried to adapt the code but no success :(
Another example extrapolates the data by multiple id but doesn't add rows for missing data.
I couldn't combine both codes with my limited experience in R. Any suggestions?
Thanks in advance...
We can use complete
library(dplyr)
library(tidyr)
library(forecast)
df %>%
group_by(id, period) %>%
complete(day =1:7)%>%
mutate(value = as.numeric(na.interp(value)))
#akrun's answer is good, as long as you don't mind using linear interpolation. However, if you do want to use a linear model, you could try this data.table approach.
library(data.table)
model <- lm(value ~ day + period + id,data=df)
dt <- as.data.table(df)[,.SD[,.(day = 1:7,value = value[match(1:7,day)])],by=.(id,period)]
dt[is.na(value), value := predict(model,.SD),]
dt
id period day value
1: 1 1 1 10.00000
2: 1 1 2 12.00000
3: 1 1 3 12.86714
4: 1 1 4 15.00000
5: 1 1 5 16.00000
6: 1 1 6 18.13725
7: 1 1 7 19.89396
8: 1 2 1 11.00000
9: 1 2 2 12.15545
10: 1 2 3 14.00000
11: 1 2 4 15.00000
12: 1 2 5 17.00000
13: 1 2 6 19.18227
14: 1 2 7 20.93898
15: 2 1 1 11.90102
16: 2 1 2 13.00000
17: 2 1 3 14.00000
18: 2 1 4 15.00000
19: 2 1 5 16.00000
20: 2 1 6 20.68455
21: 2 1 7 22.44125
22: 2 2 1 15.00000
23: 2 2 2 16.00000
24: 2 2 3 18.00000
25: 2 2 4 18.21616
26: 2 2 5 20.00000
27: 2 2 6 21.72957
28: 2 2 7 23.48627
29: 3 1 1 14.44831
30: 3 1 2 16.00000
31: 3 1 3 17.00000
32: 3 1 4 19.00000
33: 3 1 5 29.00000
34: 3 1 6 23.23184
35: 3 1 7 24.98855
36: 3 2 1 14.00000
37: 3 2 2 16.00000
38: 3 2 3 18.00000
39: 3 2 4 20.00000
40: 3 2 5 22.52016
41: 3 2 6 24.27686
42: 3 2 7 26.03357
id period day value
By using the data below, I want to create a new unique customer id by considering their contact date.
Rule: After every two days, I want each customer to get a new unique customer id and preserve it on the following record if the following contact date for the same customer is within the following two days if not assign a new id to this same customer.
I couldn't go any further than calculating date differences.
The original dataset I work is bigger; therefore, I prefer a data.table solution if possible.
library(data.table)
treshold <- 2
dt <- structure(list(customer_id = c('10','20','20','20','20','20','30','30','30','30','30','40','50','50'),
contact_date = as.Date(c("2019-01-05","2019-01-01","2019-01-01","2019-01-02",
"2019-01-08","2019-01-09","2019-02-02","2019-02-05",
"2019-02-05","2019-02-09","2019-02-12","2019-02-01",
"2019-02-01","2019-02-05")),
desired_output = c(1,2,2,2,3,3,4,5,5,6,7,8,9,10)),
class = "data.frame",
row.names = 1:14)
setDT(dt)
setorder(dt, customer_id, contact_date)
dt[, date_diff_in_days:=contact_date - shift(contact_date, type = c("lag")), by=customer_id]
dt[, date_diff_in_days:=as.numeric(date_diff_in_days)]
dt
customer_id contact_date desired_output date_diff_in_days
1: 10 2019-01-05 1 NA
2: 20 2019-01-01 2 NA
3: 20 2019-01-01 2 0
4: 20 2019-01-02 2 1
5: 20 2019-01-08 3 6
6: 20 2019-01-09 3 1
7: 30 2019-02-02 4 NA
8: 30 2019-02-05 5 3
9: 30 2019-02-05 5 0
10: 30 2019-02-09 6 4
11: 30 2019-02-12 7 3
12: 40 2019-02-01 8 NA
13: 50 2019-02-01 9 NA
14: 50 2019-02-05 10 4
Rule: After every two days, I want each customer to get a new unique customer id and preserve it on the following record if the following contact date for the same customer is within the following two days if not assign a new id to this same customer.
When creating a new ID, if you set up the by= vectors correctly to capture the rule, the auto-counter .GRP can be used:
thresh <- 2
dt[, g := .GRP, by=.(
customer_id,
cumsum(contact_date - shift(contact_date, fill=first(contact_date)) > thresh)
)]
dt[, any(g != desired_output)]
# [1] FALSE
I think the code above is correct since it works on the example, but you might want to check on your actual data (comparing against results from, eg, Gregor's approach) to be sure.
We use cumsum to increment whenever date_diff_in_days is NA or when the threshold is exceeded.
dt[, result := cumsum(is.na(date_diff_in_days) | date_diff_in_days > treshold)]
# customer_id contact_date desired_output date_diff_in_days result
# 1: 10 2019-01-05 1 NA 1
# 2: 20 2019-01-01 2 NA 2
# 3: 20 2019-01-01 2 0 2
# 4: 20 2019-01-02 2 1 2
# 5: 20 2019-01-08 3 6 3
# 6: 20 2019-01-09 3 1 3
# 7: 30 2019-02-02 4 NA 4
# 8: 30 2019-02-05 5 3 5
# 9: 30 2019-02-05 5 0 5
# 10: 30 2019-02-09 6 4 6
# 11: 30 2019-02-12 7 3 7
# 12: 40 2019-02-01 8 NA 8
# 13: 50 2019-02-01 9 NA 9
# 14: 50 2019-02-05 10 4 10
I am looking for a function in R similar to lag1, lag2 and retain functions in SAS which I can use with data.tables.
I know there are functions like embed and lag in R but they don't return a single value or the previous value . They return a complete set of vectors.
Is there anything in R which I can use with data.table?
More info on the SAS functions :
Retain
Lag
You have to be aware that R works very different from the data step in SAS. The lag function in SAS is used in the data step, and is used within the implicit loop structure of that data step. The same goes for the retain function, which simply keeps the value constant when going through the data looping.
R on the other hand works completely vectorized. This means that you have to rethink what you want to do, and adapt accordingly.
retain is simply useless in R, as R recycles arguments by default. If you want to do this explicitly, you might look at eg rep() to construct a vector with constant values and a certain length.
lag is a matter of using indices, and just shifting position of all values in a vector. In order to keep a vector of the same length, you need to add some NA and remove some extra values.
A simple example: This SAS code lags a variable x and adds a variable year that has a constant value:
data one;
retain year 2013;
input x ##;
y=lag1(x);
z=lag2(x);
datalines;
1 2 3 4 5 6
;
In R, you could write your own lag function like this:
mylag <- function(x,k) c(rep(NA,k),head(x,-k))
This single line adds k times NA at the beginning of the vector, and drops the last k values from the vector. The result is a lagged vector as given by lag1 etc. in SAS.
this allows something like :
nrs <- 1:6 # equivalent to datalines
one <- data.frame(
x = nrs,
y = mylag(nrs,1),
z = mylag(nrs,2),
year = 2013 # R automatically loops, so no extra command needed
)
The result is :
> one
x y z year
1 1 NA NA 2013
2 2 1 NA 2013
3 3 2 1 2013
4 4 3 2 2013
5 5 4 3 2013
6 6 5 4 2013
Exactly the same would work with a data.table object. The important note here is to rethink your strategy: Instead of thinking loopwise as you do with the DATA step in SAS, you have to start thinking in terms of vectors and indices when using R.
I would say the closet equivalent to retain, lag1, and lag2 would be the Lag function in the quantmod package.
It's very easy to use with data.tables. E.g.:
library(data.table)
library(quantmod)
d <- data.table(v1=c(rep('a', 10), rep('b', 10)), v2=1:20)
setkeyv(d, 'v1')
d[,new_var := Lag(v2, 1), by='v1']
d[,new_var2 := v2-Lag(v2, 3), by='v1']
d[,new_var3 := Next(v2, 2), by='v1']
This yields the following:
print(d)
v1 v2 new_var new_var2 new_var3
1: a 1 NA NA 3
2: a 2 1 NA 4
3: a 3 2 NA 5
4: a 4 3 3 6
5: a 5 4 3 7
6: a 6 5 3 8
7: a 7 6 3 9
8: a 8 7 3 10
9: a 9 8 3 NA
10: a 10 9 3 NA
11: b 11 NA NA 13
12: b 12 11 NA 14
13: b 13 12 NA 15
14: b 14 13 3 16
15: b 15 14 3 17
16: b 16 15 3 18
17: b 17 16 3 19
18: b 18 17 3 20
19: b 19 18 3 NA
20: b 20 19 3 NA
As you can see, Lag lets you look back and Next lets you look forward. Both functions are nice because they pad the result with NAs such that it has the same length as the input.
If you want to get even fancier, and higher-performance, you can look into rolling joins with data.table objects. This is a little bit different thab what you are asking for, but is conceptually related, and so powerful and awesome I have to share.
Start with a data.table:
library(data.table)
library(quantmod)
set.seed(42)
d1 <- data.table(
id=c(rep('a', 10), rep('b', 10)),
time=rep(1:10,2),
value=runif(20))
setkeyv(d1, c('id', 'time'))
print(d1)
id time value
1: a 1 0.9148060
2: a 2 0.9370754
3: a 3 0.2861395
4: a 4 0.8304476
5: a 5 0.6417455
6: a 6 0.5190959
7: a 7 0.7365883
8: a 8 0.1346666
9: a 9 0.6569923
10: a 10 0.7050648
11: b 1 0.4577418
12: b 2 0.7191123
13: b 3 0.9346722
14: b 4 0.2554288
15: b 5 0.4622928
16: b 6 0.9400145
17: b 7 0.9782264
18: b 8 0.1174874
19: b 9 0.4749971
20: b 10 0.5603327
You have another data.table you want to join, but not all time indexes are present in the second table:
d2 <- data.table(
id=sample(c('a', 'b'), 5, replace=TRUE),
time=sample(1:10, 5),
value2=runif(5))
setkeyv(d2, c('id', 'time'))
print(d2)
id time value2
1: a 4 0.811055141
2: a 10 0.003948339
3: b 6 0.737595618
4: b 8 0.388108283
5: b 9 0.685169729
A regular merge yields lots of missing values:
d2[d1,,roll=FALSE]
id time value2 value
1: a 1 NA 0.9148060
2: a 2 NA 0.9370754
3: a 3 NA 0.2861395
4: a 4 0.811055141 0.8304476
5: a 5 NA 0.6417455
6: a 6 NA 0.5190959
7: a 7 NA 0.7365883
8: a 8 NA 0.1346666
9: a 9 NA 0.6569923
10: a 10 0.003948339 0.7050648
11: b 1 NA 0.4577418
12: b 2 NA 0.7191123
13: b 3 NA 0.9346722
14: b 4 NA 0.2554288
15: b 5 NA 0.4622928
16: b 6 0.737595618 0.9400145
17: b 7 NA 0.9782264
18: b 8 0.388108283 0.1174874
19: b 9 0.685169729 0.4749971
20: b 10 NA 0.5603327
However, data.table allows you to roll the secondary index forward, WITHIN THE PRIMARY INDEX!
d2[d1,,roll=TRUE]
id time value2 value
1: a 1 NA 0.9148060
2: a 2 NA 0.9370754
3: a 3 NA 0.2861395
4: a 4 0.811055141 0.8304476
5: a 5 0.811055141 0.6417455
6: a 6 0.811055141 0.5190959
7: a 7 0.811055141 0.7365883
8: a 8 0.811055141 0.1346666
9: a 9 0.811055141 0.6569923
10: a 10 0.003948339 0.7050648
11: b 1 NA 0.4577418
12: b 2 NA 0.7191123
13: b 3 NA 0.9346722
14: b 4 NA 0.2554288
15: b 5 NA 0.4622928
16: b 6 0.737595618 0.9400145
17: b 7 0.737595618 0.9782264
18: b 8 0.388108283 0.1174874
19: b 9 0.685169729 0.4749971
20: b 10 0.685169729 0.5603327
This is pretty damn cool: Old observations are rolled forward in time, until they are replaced by new ones. If you want to replace the NA values at the beggining of the series, you can do so by rolling the first observation backwards:
d2[d1,,roll=TRUE, rollends=c(TRUE, TRUE)]
id time value2 value
1: a 1 0.811055141 0.9148060
2: a 2 0.811055141 0.9370754
3: a 3 0.811055141 0.2861395
4: a 4 0.811055141 0.8304476
5: a 5 0.811055141 0.6417455
6: a 6 0.811055141 0.5190959
7: a 7 0.811055141 0.7365883
8: a 8 0.811055141 0.1346666
9: a 9 0.811055141 0.6569923
10: a 10 0.003948339 0.7050648
11: b 1 0.737595618 0.4577418
12: b 2 0.737595618 0.7191123
13: b 3 0.737595618 0.9346722
14: b 4 0.737595618 0.2554288
15: b 5 0.737595618 0.4622928
16: b 6 0.737595618 0.9400145
17: b 7 0.737595618 0.9782264
18: b 8 0.388108283 0.1174874
19: b 9 0.685169729 0.4749971
20: b 10 0.685169729 0.5603327
These rolling joins are absolutely incredible, and I've never seen them implemented in any other open source package (see ?data.table for more info). It will take a little while to turn off your "SAS brain" and turn on your "R brain", but once you get over that initial hump you'll find that the language is much more expressive.
For retain, try this :
retain<-function(x,event,outside=NA)
{
indices <- c(1,which(event==TRUE), nrow(df)+1)
values <- c(outside,x[event==TRUE])
y<- rep(values, diff(indices))
}
With data : I want to retain down the value when w==b
df <- data.frame(w = c("a","b","c","a","b","c"), x = 1:6, y = c(1,1,2,2,2,3), stringsAsFactors = FALSE)
df$z<-retain(df$x-df$y,df$w=="b")
df
And here's the contrary obtain, that does not exist in SAS:
obtain<-function(x,event,outside=NA)
{
indices <- c(0,which(event==TRUE), nrow(df))
values <- c(x[event==TRUE],outside)
y<- rep(values, diff(indices))
}
Here's an example. I want to obtain the value in advance where w==b
df$z2<-obtain(df$x-df$y,df$w=="b")
df
Thanks to Julien for helping.
here's an example: cumulate value with sqldf:
> w_cum <-
sqldf("select t1.id, t1.SomeNumt, SUM(t2.SomeNumt) as cum_sum
from w_cum t1
inner join w_cum t2 on t1.id >= t2.id
group by t1.id, t1.SomeNumt
order by t1.id
")
id SomeNumt cum_sum
1 11 11
2 12 23
3 13 36
4 14 50
5 15 65
6 16 81
7 17 98
8 18 116
9 19 135
10 20 155
I want to add a new column to my data.table containing the cumulative product of Data1 based on the Date. The cumulative product should be calculated for each category (Cat) and should start with the latest available Date.
Sample data:
DF = data.frame(Cat=rep(c("A","B"),each=4), Date=rep(c("01-08-2013","01-07-2013","01-04-2013","01-03-2013"),2), Data1=c(1:8))
DF$Date = as.Date(DF$Date , "%m-%d-%Y")
DT = data.table(DF)
DT[ , Data1_cum:=NA_real_]
DT
Cat Date Data1 Data1_cum
1: A 2013-01-08 1 NA
2: A 2013-01-07 2 NA
3: A 2013-01-04 3 NA
4: A 2013-01-03 4 NA
5: B 2013-01-08 5 NA
6: B 2013-01-07 6 NA
7: B 2013-01-04 7 NA
8: B 2013-01-03 8 NA
The result should look like this:
Cat Date Data1 Data1_cum
1: A 2013-01-08 1 1
2: A 2013-01-07 2 2
3: A 2013-01-04 3 6
4: A 2013-01-03 4 24
5: B 2013-01-08 5 5
6: B 2013-01-07 6 30
7: B 2013-01-04 7 210
8: B 2013-01-03 8 1680
I figured out that I could do something similar using cumprod(), but I do not know how to handle the categories. NAs in Data1 should be ignored / treated as 1.
The real dataset has about 8 million rows and 1000 categories.
If the only looksissue is the ordering...
DT[order(Date, decreasing=TRUE), Data1_cum := cumprod(Data1), by=Cat]
DT
Cat Date Data1 Data1_cum
1: A 2013-01-08 1 1
2: A 2013-01-07 2 2
3: A 2013-01-04 3 6
4: A 2013-01-03 4 24
5: B 2013-01-08 5 5
6: B 2013-01-07 6 30
7: B 2013-01-04 7 210
8: B 2013-01-03 8 1680
However, if you have NA's to deal with, then there is a few extra steps:
Note: If you shuffle the order of the rows, your results can vary. Careful with how you implement the order(.) command
## Let's add some NA values
DT <- rbind(DT, DT)
DT[c(2, 6, 11, 15), Data1 := NA]
# shuffle the rows, to make sure this is right
set.seed(1)
DT <- DT[sample(nrow(DT))]
Assigning the cumulative product:
Leaving NA's
## If you want to leave the NA's as NA's in the cum prod, use:
DT[ , Data1_cum := NA_real_ ]
DT[ intersect(order(Date, decreasing=TRUE), which(!is.na(Data1)))
, Data1_cum := cumprod(Data1)
, by=Cat]
# View the data, orderly
DT[order(Date, decreasing=TRUE)][order(Cat)]
Cat Date Data1 Data1_cum
1: A 2013-01-08 1 1
2: A 2013-01-08 1 1
3: A 2013-01-07 2 2
4: A 2013-01-07 NA NA <~~~~~~~ Note that the NA rows have the value of the prev row
5: A 2013-01-04 3 6
6: A 2013-01-04 NA NA <~~~~~~~ Note that the NA rows have the value of the prev row
7: A 2013-01-03 4 24
8: A 2013-01-03 4 96
9: B 2013-01-08 5 5
10: B 2013-01-08 5 25
11: B 2013-01-07 6 150
12: B 2013-01-07 NA NA <~~~~~~~ Note that the NA rows have the value of the prev row
13: B 2013-01-04 7 1050
14: B 2013-01-04 NA NA <~~~~~~~ Note that the NA rows have the value of the prev row
15: B 2013-01-03 8 8400
16: B 2013-01-03 8 67200
Replacing NA's with value of previous Row
## If instead you want to treat the NA's as 1, use:
DT[order(Date, decreasing=TRUE), Data1_cum := {Data1[is.na(Data1)] <- 1; cumprod(Data1 [order(Date, decreasing=TRUE)] )}, by=Cat]
# View the data, orderly
DT[order(Date, decreasing=TRUE)][order(Cat)]
Cat Date Data1 Data1_cum
1: A 2013-01-08 1 1
2: A 2013-01-08 1 1
3: A 2013-01-07 2 2
4: A 2013-01-07 NA 2 <~~~~~~~ Rows with NA took on values of the previous Row
5: A 2013-01-04 3 6
6: A 2013-01-04 NA 6 <~~~~~~~ Rows with NA took on values of the previous Row
7: A 2013-01-03 4 24
8: A 2013-01-03 4 96
9: B 2013-01-08 5 5
10: B 2013-01-08 5 25
11: B 2013-01-07 6 150
12: B 2013-01-07 NA 150 <~~~~~~~ Rows with NA took on values of the previous Row
13: B 2013-01-04 7 1050
14: B 2013-01-04 NA 1050 <~~~~~~~ Rows with NA took on values of the previous Row
15: B 2013-01-03 8 8400
16: B 2013-01-03 8 67200
Alternatively, If you already have the cumulative product and simply want to remove the NA's you can do so as follows:
# fix the NA's with the previous value
DT[order(Date, decreasing=TRUE),
Data1_cum := {tmp <- c(0, head(Data1_cum, -1));
Data1_cum[is.na(Data1_cum)] <- tmp[is.na(Data1_cum)];
Data1_cum }
, by=Cat ]
data.table is awesome, because I can do rolling joins, and even do rolling joins within groups!
library(data.table)
set.seed(42)
metrics <- data.frame(
ID=c(rep(1, 10), rep(2,5), rep(3,5)),
Time=c(1:10, 4:8, 8:12),
val1=runif(20),
val2=runif(20),
val3=runif(20),
val4=runif(20)
)
metrics <- data.table(metrics[sample(1:nrow(metrics), 15),], key=c('ID', 'Time'))
calendar <- data.table(expand.grid(ID=1:3, Time=1:12), key=c('ID', 'Time'))
metrics[calendar,roll=TRUE]
However, this isn't awesome enough for me. This data.table still has NAs:
> metrics[calendar,roll=TRUE]
ID Time val1 val2 val3 val4
1: 1 1 0.9148060 0.9040314 0.3795592 0.675607275
2: 1 2 0.9370754 0.1387102 0.4357716 0.982817198
3: 1 3 0.9370754 0.1387102 0.4357716 0.982817198
4: 1 4 0.8304476 0.9466682 0.9735399 0.566488424
5: 1 5 0.8304476 0.9466682 0.9735399 0.566488424
6: 1 6 0.5190959 0.5142118 0.9575766 0.189473935
7: 1 7 0.7365883 0.3902035 0.8877549 0.271286615
8: 1 8 0.7365883 0.3902035 0.8877549 0.271286615
9: 1 9 0.6569923 0.4469696 0.9709666 0.693204820
10: 1 10 0.7050648 0.8360043 0.6188382 0.240544740
11: 1 11 0.7050648 0.8360043 0.6188382 0.240544740
12: 1 12 0.7050648 0.8360043 0.6188382 0.240544740
13: 2 1 NA NA NA NA
14: 2 2 NA NA NA NA
15: 2 3 NA NA NA NA
16: 2 4 0.4577418 0.7375956 0.3334272 0.042988796
17: 2 5 0.7191123 0.8110551 0.3467482 0.140479094
18: 2 6 0.9346722 0.3881083 0.3984854 0.216385415
19: 2 7 0.2554288 0.6851697 0.7846928 0.479398564
20: 2 8 0.2554288 0.6851697 0.7846928 0.479398564
21: 2 9 0.2554288 0.6851697 0.7846928 0.479398564
22: 2 10 0.2554288 0.6851697 0.7846928 0.479398564
23: 2 11 0.2554288 0.6851697 0.7846928 0.479398564
24: 2 12 0.2554288 0.6851697 0.7846928 0.479398564
25: 3 1 NA NA NA NA
26: 3 2 NA NA NA NA
27: 3 3 NA NA NA NA
28: 3 4 NA NA NA NA
29: 3 5 NA NA NA NA
30: 3 6 NA NA NA NA
31: 3 7 NA NA NA NA
32: 3 8 0.9400145 0.8329161 0.7487954 0.719355838
33: 3 9 0.9400145 0.8329161 0.7487954 0.719355838
34: 3 10 0.1174874 0.2076590 0.1712643 0.375489965
35: 3 11 0.4749971 0.9066014 0.2610880 0.514407708
36: 3 12 0.5603327 0.6117786 0.5144129 0.001570554
ID Time val1 val2 val3 val4
I could fill these NA's using zoo:::na.locf, fromLast=TRUE, but that's not very fun. Can anyone think of an elegant way I can roll NA's backward, (after rolling them forward), during the data.table join?
This is possible in data.table version 1.8.8 released March 2013:
metrics[calendar, roll=TRUE, rollends=c(TRUE, TRUE)]
From the data.table NEWS file:
In addition to TRUE/FALSE, 'roll' may now be a positive number (roll forwards/LOCF) or
negative number (roll backwards/NOCB). A finite number limits the distance a value is
rolled (limited staleness). roll=TRUE and roll=+Inf are equivalent.
'rollends' is a new parameter holding two logicals. The first observation is rolled
backwards if the first value of rollends is TRUE. The last observation is rolled forwards if the second value of rollends
is TRUE. If roll is a finite number, the same limit applies to the ends.
New value roll='nearest' joins to the nearest value (either backwards or forwards) when
the value falls in a gap, and to the end value according to 'rollends'.
'rolltolast' has been deprecated. For backwards compatibility it is converted to
{roll=TRUE;rollends=c(FALSE,FALSE)}.
As always, to download the most up-to-date version of data.table, see Installation.
metrics[calendar, roll = TRUE, rollends = c(TRUE, TRUE)]