Author: Matthieu Gomez
Date: 2025-07-23

# statar

#### Matthieu Gomez

#### 2025-07-23

### 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
     #> [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

`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")