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
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
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 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")
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