This function retrieves the time-lagged values of a variable, using the time variable defined in
.t in the function or by
tlag() is highly unusual among time-lag functions in that it is usable even if observations are not uniquely identified by
.i, if defined).
tlag( .var, .df = get(".", envir = parent.frame()), .n = 1, .default = NA, .quick = FALSE, .resolve = "error", .group_i = TRUE, .i = NULL, .t = NULL, .d = NA, .uniqcheck = FALSE )
Unquoted variable from
Data frame, pibble, or tibble (usually the object that contains
Number of periods to lag by. 1 by default. Note that this is automatically scaled by
Fill-in value used when lagged observation is not present. Defaults to NA.
If there is more than one observation per individal/period, and the value of
By default, if
Quoted or unquotes variable(s) that identify the individual cases. Note that setting any one of
Quoted or unquoted variable indicating the time.
Number indicating the gap in
Logical parameter. Set to TRUE to always check whether
data(Scorecard) # The Scorecard data is uniquely identified by unitid and year. # However, there are sometimes gaps between years. # In cases like this, using dplyr::lag() will still use the row before, # whereas tlag() will respect the gap and give a NA, much like plm::lag() # (although tlag is slower than either, sorry) Scorecard <- Scorecard %>% dplyr::mutate(pmdplyr_tlag = tlag(earnings_med, .i = unitid, .t = year )) Scorecard <- Scorecard %>% dplyr::arrange(year) %>% dplyr::group_by(unitid) %>% dplyr::mutate(dplyr_lag = dplyr::lag(earnings_med)) %>% dplyr::ungroup() # more NAs in the pmdplyr version - observations with a gap and thus no real lag present in data sum(is.na(Scorecard$pmdplyr_tlag))#>  26987#>  16950# If we want to ignore gaps, or have .d = 0, and .i and .t uniquely identify observations, # we can use the .quick option to match dplyr::lag() Scorecard <- Scorecard %>% dplyr::mutate(pmdplyr_quick_tlag = tlag(earnings_med, .i = unitid, .t = year, .d = 0, .quick = TRUE )) sum(Scorecard$dplyr_lag != Scorecard$pmdplyr_quick_tlag, na.rm = TRUE)#>  0# Where tlag shines is when you have multiple observations per .i/.t # If the value of .var is constant within .i/.t, it will work just as you expect. # If it's not, it will throw an error, or you can set # .resolve to tell tlag how to select a single value from the many # Maybe we want to get the lagged average earnings within degree award type Scorecard <- Scorecard %>% dplyr::mutate( last_year_earnings_by_category = tlag(earnings_med, .i = pred_degree_awarded_ipeds, .t = year, .resolve = function(x) mean(x, na.rm = TRUE) ) ) # Or maybe I want the lagged earnings across all types - .i isn't necessary! Scorecard <- Scorecard %>% dplyr::mutate(last_year_earnings_all = tlag(earnings_med, .t = "year", .resolve = function(x) mean(x, na.rm = TRUE) )) # Curious why the first nonmissing obs show up in 2012? # It's because there's no 2008 or 2010 in the data, so when 2009 or 2011 look back # a year, they find nothing! # We could get around this by setting .d = 0 to ignore gap length # Note this can be a little slow. Scorecard <- Scorecard %>% dplyr::mutate(last_year_earnings_all = tlag(earnings_med, .t = year, .d = 0, .resolve = function(x) mean(x, na.rm = TRUE) ))