statar
In statar: Tools Inspired by 'Stata' to Manipulate Tabular Data

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")