The classes “monthly” and “quarterly” print as dates and are compatible with usual time extraction (ie `month`

, `year`

, etc). Yet, they are stored as integers representing the number of elapsed periods since 1970/01/0 (resp in week, months, quarters). This is particularly handy for simple algebra:

```
# elapsed dates
library(lubridate)
date <- mdy(c("04/03/1992", "01/04/1992", "03/15/1992"))
datem <- as.monthly(date)
# displays as a period
datem
#> [1] "1992m04" "1992m01" "1992m03"
# behaves as an integer for numerical operations:
datem + 1
#> [1] "1992m05" "1992m02" "1992m04"
# behaves as a date for period extractions:
year(datem)
#> [1] 1992 1992 1992
```

`lag`

/`lead`

a vector along a time variable

```
year <- c(1989, 1991, 1992)
value <- c(4.1, 4.5, 3.3)
lag(value, 1, order_by = year) # lag based on previous row
lag(value, 1, along_with = year) # lag based on previous year - 1
library(lubridate)
date <- mdy(c("01/04/1992", "03/15/1992", "04/03/1992"))
datem <- as.monthly(date)
value <- c(4.1, 4.5, 3.3)
lag(value, along_with = datem)
```

`roll_lag`

/`roll_lead`

apply a function on a vector over a window defined by a time variable

```
year <- c(1989, 1991, 1992)
value <- c(1, 1, 1)
roll_lag(value, sum, n = 2, order_by = year) # rolling sum based on the two previous rows
roll_lag(value, sum, n = 2, along_with = year) # rolling sum based on dates in [year-2, year]
roll_lag(value, sum, n = 2, along_with = year, closed= c(TRUE, FALSE)) # rolling sum based on dates in [year-2, year[
```

Since these functions can be applied to any vector (in constrast to `zoo`

and `xts`

), they can be used *within groups*. For instance, using `data.table`

:

```
DT <- data.table(
id = c(1, 1, 1, 2, 2),
year = c(1989, 1991, 1992, 1991, 1992),
value = c(4.1, 4.5, 3.3, 3.2, 5.2)
)
DT[, value_l := lag(value, along_with = year), by = id]
DT[, value_ma := roll_lag(value, mean, n = 3, along_with = year), by = id]
```

`fill_gap`

fills in gaps in a data.table along a time variable (corresponds to Stata `tsfill`

)

```
DT <- data.table(
id = c(1, 1, 1, 2, 2),
year = c(1992, 1989, 1991, 1992, 1991),
value = c(4.1, 4.5, 3.3, 3.2, 5.2)
)
fill_gap(DT, value, along_with = year, by = id)
library(lubridate)
DT[, date:= mdy(c("03/01/1992", "04/03/1992", "07/15/1992", "08/21/1992", "10/03/1992"))]
DT[, datem := as.monthly(date)]
fill_gap(DT, value, along_with = datem, by = id)
```

`setna`

fills in missing values along a time variable. `setna`

inherits from the data.table options `roll`

and `rollends`

```
DT <- data.table(
id = c(1, 1, 1, 1, 2, 2),
date = c(1992, 1989, 1991, 1993, 1992, 1991),
value = c(NA, NA, 3, NA, 3.2, 5.2)
)
DT1 <- copy(DT)
setkey(DT1, id, date)
DT2 <- copy(DT1)
DT3 <- copy(DT1)
setna(DT, value, along_with = date, by = id)
setna(DT1)
setna(DT2, value, rollends = TRUE)
setna(DT3, value, roll = "nearest")
```