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Join Lagged Variable Values over a Date Range

Source: R/join_lagged_values.R
join_lagged_values.Rd

Joins lagged values of selected variables from one dataset (new_data) into another (original_data), based on date ranges defined by min_lag and max_lag. Unlike add_lagged_columns(), this function supports joining across data frames with different date grids (e.g., monthly source data into quarterly target data).

Usage

join_lagged_values(
  original_data,
  new_data,
  id_keys,
  min_lag,
  max_lag,
  ff_adjustment = FALSE,
  data_options = NULL
)

Arguments

original_data

A data frame containing the target panel data.

new_data

A data frame containing the source variables to lag and merge. All columns besides id_keys and the date column will be lagged and joined.

id_keys

A character vector specifying the identifier column(s).

min_lag

A lubridate::Period specifying the lower lag bound (inclusive).

max_lag

A lubridate::Period specifying the upper lag bound (inclusive).

ff_adjustment

Logical; if TRUE, keeps only the last observation per identifier and year before lagging (Fama-French convention). Defaults to FALSE.

data_options

A list of class tidyfinance_data_options (created via data_options()) specifying column name mappings. The date element is used to identify the date column. Uses data_options() default if NULL: "date" = "date".

Value

A data frame with all columns from original_data plus the lagged columns from new_data (keeping their original names).

See also

Other rolling and lagging functions: add_lagged_columns(), compute_rolling_value()

Examples

set.seed(42)
library(dplyr)
library(lubridate)
#> 
#> Attaching package: ‘lubridate’
#> The following objects are masked from ‘package:base’:
#> 
#>     date, intersect, setdiff, union

df1 <- tibble(
  id = rep(1:2, each = 6),
  date = rep(seq(as.Date("2020-01-01"), by = "month", length.out = 6), 2)
)

df2 <- df1 |>
  mutate(x = rnorm(n()))

join_lagged_values(
  original_data = df1,
  new_data = df2,
  id_keys = "id",
  min_lag = months(1),
  max_lag = months(3)
)
#> # A tibble: 12 × 3
#>       id date             x
#>    <int> <date>       <dbl>
#>  1     1 2020-01-01 NA     
#>  2     1 2020-02-01  1.37  
#>  3     1 2020-03-01 -0.565 
#>  4     1 2020-04-01  0.363 
#>  5     1 2020-05-01  0.633 
#>  6     1 2020-06-01  0.404 
#>  7     2 2020-01-01 NA     
#>  8     2 2020-02-01  1.51  
#>  9     2 2020-03-01 -0.0947
#> 10     2 2020-04-01  2.02  
#> 11     2 2020-05-01 -0.0627
#> 12     2 2020-06-01  1.30