Perform standard panel-data calculations — panel

These functions perform the standard between and within transformations on panel data.

within_i(
  .var,
  .df = get(".", envir = parent.frame()),
  .fcn = function(x) mean(x, na.rm = TRUE),
  .i = NULL,
  .t = NULL,
  .uniqcheck = FALSE
)

between_i(
  .var,
  .df = get(".", envir = parent.frame()),
  .fcn = function(x) mean(x, na.rm = TRUE),
  .i = NULL,
  .t = NULL,
  .uniqcheck = FALSE
)

Arguments

.var	Vector to be transformed
.df	Data frame, pibble, or tibble (usually the data frame or tibble that contains `.var`) which contains the panel structure variables either listed in `.i` and `.t`, or earlier declared with `as_pibble()`. If `tlag` is called inside of a `dplyr` verb, this can be omitted and the data will be picked up automatically.
.fcn	The function to be passed to `dplyr::summarize()`. `x - .fcn(x)` within `.i` is the within tranformation. `.fcn(x)` within `.i` minus `.fcn` overall is the between transformation. This will almost always be the default `.fcn = function(x) mean(x,na.rm=TRUE)`.
.i	Quoted or unquoted variable(s) that identify the individual cases. Note that setting any one of `.i`, `.t`, or `.d` will override all three already applied to the data, and will return data that is `as_pibble()`d with all three, unless `.setpanel=FALSE`.
.t	Quoted or unquoted variable with the single variable name indicating the time. `pmdplyr` accepts two kinds of time variables: numeric variables where a fixed distance `.d` will take you from one observation to the next, or, if `.d=0`, any standard variable type with an order. Consider using the `time_variable()` function to create the necessary variable if your data uses a `Date` variable for time.
.uniqcheck	Logical parameter. Set to TRUE to always check whether `.i` and `.t` uniquely identify observations in the data. By default this is set to FALSE and the check is only performed once per session, and only if at least one of `.i`, `.t`, or `.d` is set.

Details

These functions do not take a .d argument because it is irrelevant here.

Examples


data(SPrail)
# Calculate within- and between-route variation in price and add it to the data
SPrail <- SPrail %>%
  dplyr::mutate(
    within_route = within_i(price, .i = c(origin, destination)),
    between_route = between_i(price, .i = c(origin, destination))
  )