### Elapsed dates

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
#>  "1992m04" "1992m01" "1992m03"
# behaves as an integer for numerical operations:
datem + 1
#>  "1992m05" "1992m02" "1992m04"
# behaves as a date for period extractions:
year(datem)
#>  1992 1992 1992
``````

`tlag`/`tlead` a vector with respect to a number of periods, not with respect to the number of rows

``````year <- c(1989, 1991, 1992)
value <- c(4.1, 4.5, 3.3)
tlag(value, 1, time = year)
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)
tlag(value, time = datem)
``````

In constrast to comparable functions in `zoo` and `xts`, these functions can be applied to any vector and be used within a `dplyr` chain:

``````df <- tibble(
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)
)
df %>% group_by(id) %>% mutate(value_l = tlag(value, time = year))
``````

### is.panel

`is.panel` checks whether a dataset is a panel i.e. the time variable is never missing and the combinations (id, time) are unique.

``````df <- tibble(
id1    = c(1, 1, 1, 2, 2),
id2   = 1:5,
year  = c(1991, 1993, NA, 1992, 1992),
value = c(4.1, 4.5, 3.3, 3.2, 5.2)
)
df %>% group_by(id1) %>% is.panel(year)
df1 <- df %>% filter(!is.na(year))
df1 %>% is.panel(year)
df1 %>% group_by(id1) %>% is.panel(year)
df1 %>% group_by(id1, id2) %>% is.panel(year)
``````

### fill_gap

fill_gap transforms a unbalanced panel into a balanced panel. It corresponds to the stata command `tsfill`. Missing observations are added as rows with missing values.

``````df <- tibble(
id    = c(1, 1, 1, 2),
datem  = as.monthly(mdy(c("04/03/1992", "01/04/1992", "03/15/1992", "05/11/1992"))),
value = c(4.1, 4.5, 3.3, 3.2)
)
df %>% group_by(id) %>% fill_gap(datem)
df %>% group_by(id) %>% fill_gap(datem, full = TRUE)
df %>% group_by(id) %>% fill_gap(datem, roll = "nearest")
``````