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#' Fill in missing values with previous or next value
#'
#' @inheritParams arrange.tbl_lazy
#' @param ... Columns to fill.
#' @param .direction Direction in which to fill missing values. Currently
#' either "down" (the default) or "up". Note that "up" does not work when
#' `.data` is sorted by non-numeric columns. As a workaround revert the order
#' yourself beforehand; for example replace `arrange(x, desc(y))` by
#' `arrange(desc(x), y)`.
#'
#' @examplesIf rlang::is_installed("tidyr", version = "1.0.0")
#' squirrels <- tibble::tribble(
#' ~group, ~name, ~role, ~n_squirrels, ~ n_squirrels2,
#' 1, "Sam", "Observer", NA, 1,
#' 1, "Mara", "Scorekeeper", 8, NA,
#' 1, "Jesse", "Observer", NA, NA,
#' 1, "Tom", "Observer", NA, 4,
#' 2, "Mike", "Observer", NA, NA,
#' 2, "Rachael", "Observer", NA, 6,
#' 2, "Sydekea", "Scorekeeper", 14, NA,
#' 2, "Gabriela", "Observer", NA, NA,
#' 3, "Derrick", "Observer", NA, NA,
#' 3, "Kara", "Scorekeeper", 9, 10,
#' 3, "Emily", "Observer", NA, NA,
#' 3, "Danielle", "Observer", NA, NA
#' )
#' squirrels$id <- 1:12
#'
#' tbl_memdb(squirrels) %>%
#' window_order(id) %>%
#' tidyr::fill(
#' n_squirrels,
#' n_squirrels2,
#' )
fill.tbl_lazy <- function(.data, ..., .direction = c("down", "up", "updown", "downup")) {
cols_to_fill <- tidyselect::eval_select(expr(c(...)), .data)
cols_to_fill <- syms(names(cols_to_fill))
order_by_cols <- op_sort(.data)
.direction <- arg_match0(.direction, c("down", "up", "updown", "downup"))
if (is_empty(order_by_cols)) {
cli_abort(
c(
x = "{.arg .data} does not have explicit order.",
i = "Please use {.fun dbplyr::window_order} to make order explicit."
)
)
}
if (.direction == "updown") {
.data <- tidyr::fill(.data, !!!cols_to_fill, .direction = "up")
.data <- tidyr::fill(.data, !!!cols_to_fill, .direction = "down")
return(.data)
} else if (.direction == "downup") {
.data <- tidyr::fill(.data, !!!cols_to_fill, .direction = "down")
.data <- tidyr::fill(.data, !!!cols_to_fill, .direction = "up")
return(.data)
}
if (.direction == "up") {
order_by_cols <- purrr::map(order_by_cols, swap_order_direction)
}
dbplyr_fill0(
.con = remote_con(.data),
.data = .data,
cols_to_fill = cols_to_fill,
order_by_cols = order_by_cols,
.direction = .direction
)
}
swap_order_direction <- function(x) {
if (is_quosure(x)) {
x <- quo_get_expr(x)
}
if (is_call(x, "desc", n = 1)) {
call_args(x)[[1]]
} else {
expr(desc(!!x))
}
}
dbplyr_fill0 <- function(.con, .data, cols_to_fill, order_by_cols, .direction) {
UseMethod("dbplyr_fill0")
}
# databases with support for `IGNORE NULLS`
# * hive: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+WindowingAndAnalytics
# * impala: https://docs.cloudera.com/documentation/enterprise/5-11-x/topics/impala_analytic_functions.html
# * oracle: https://oracle-base.com/articles/misc/first-value-and-last-value-analytic-functions
# * redshift: https://docs.aws.amazon.com/redshift/latest/dg/r_WF_first_value.html
# * teradata: https://docs.teradata.com/r/756LNiPSFdY~4JcCCcR5Cw/V~t1FC7orR6KCff~6EUeDQ
#' @export
dbplyr_fill0.DBIConnection <- function(.con,
.data,
cols_to_fill,
order_by_cols,
.direction) {
# strategy:
# 1. construct a window
# * from the first row to the current row
# * ordered by `order_by_cols`
# * partitioned by groups if any
# 2. in this window use the last value ignoring nulls; in SQL this is (usually)
# `LAST_VALUE(<column> IGNORE NULLS)`
#
# Source: https://dzone.com/articles/how-to-fill-sparse-data-with-the-previous-non-empt
grps <- op_grps(.data)
fill_sql <- purrr::map(
cols_to_fill,
~ win_over(
last_value_sql(.con, .x),
partition = if (!is_empty(grps)) escape(ident(op_grps(.data)), con = .con),
order = translate_sql(!!!order_by_cols, con = .con),
con = .con
)
) %>%
set_names(as.character(cols_to_fill))
.data %>%
transmute(
!!!syms(colnames(.data)),
!!!fill_sql
)
}
# databases without support for `IGNORE NULLS`
# * sqlite
# * access: https://www.techonthenet.com/access/functions/
# * hana: https://help.sap.com/viewer/4fe29514fd584807ac9f2a04f6754767/2.0.04/en-US/e7ef7cc478f14a408e1af27fc1a78624.html
# * mysql: https://dev.mysql.com/doc/refman/8.0/en/window-function-descriptions.html
# * mariadb
# * postgres: https://www.postgresql.org/docs/13/functions-window.html
# * mssql: https://docs.microsoft.com/en-us/sql/t-sql/functions/first-value-transact-sql?view=sql-server-ver15
# -> `IGNORE NULLS` only in Azure SQL Edge
#' @export
dbplyr_fill0.SQLiteConnection <- function(.con,
.data,
cols_to_fill,
order_by_cols,
.direction) {
# this strategy is used for databases that don't support `IGNORE NULLS` in
# `LAST_VALUE()`.
#
# strategy:
# for each column to fill:
# 1. generate a helper column `....dbplyr_partion_x`. It creates one partition
# per non-NA value and all following NA (in the order of `order_by_cols`),
# i.e. each partition has exactly one non-NA value and any number of NA.
# 2. use the non-NA value in each partition (`max()` is just the simplest
# way to do that reliable among databases).
# 3. remove the helper column again.
partition_sql <- purrr::map(
cols_to_fill,
~ translate_sql(
cumsum(case_when(is.na(!!.x) ~ 0L, TRUE ~ 1L)),
con = .con,
vars_order = translate_sql(!!!order_by_cols, con = .con),
vars_group = op_grps(.data),
)
) %>%
set_names(paste0("..dbplyr_partion_", seq_along(cols_to_fill)))
dp <- .data %>%
mutate(!!!partition_sql)
fill_sql <- purrr::map2(
cols_to_fill, names(partition_sql),
~ translate_sql(
max(!!.x, na.rm = TRUE),
con = .con,
vars_group = c(op_grps(.data), .y),
)
) %>%
set_names(purrr::map_chr(cols_to_fill, as_name))
dp %>%
transmute(
!!!syms(colnames(.data)),
!!!fill_sql
) %>%
select(!!!colnames(.data))
}
#' @export
dbplyr_fill0.PostgreSQL <- dbplyr_fill0.SQLiteConnection
#' @export
dbplyr_fill0.PqConnection <- dbplyr_fill0.SQLiteConnection
#' @export
dbplyr_fill0.PostgreSQLConnection <- dbplyr_fill0.SQLiteConnection
#' @export
dbplyr_fill0.HDB <- dbplyr_fill0.SQLiteConnection
#' @export
dbplyr_fill0.ACCESS <- dbplyr_fill0.SQLiteConnection
#' @export
`dbplyr_fill0.Microsoft SQL Server` <- dbplyr_fill0.SQLiteConnection
#' @export
dbplyr_fill0.MariaDBConnection <- dbplyr_fill0.SQLiteConnection
#' @export
dbplyr_fill0.MySQLConnection <- dbplyr_fill0.SQLiteConnection
#' @export
dbplyr_fill0.MySQL <- dbplyr_fill0.SQLiteConnection
last_value_sql <- function(con, x) {
UseMethod("last_value_sql")
}
#' @export
last_value_sql.DBIConnection <- function(con, x) {
build_sql("LAST_VALUE(", ident(as.character(x)), " IGNORE NULLS)", con = con)
}
#' @export
`last_value_sql.Microsoft SQL Server` <- function(con, x) {
build_sql("LAST_VALUE(", ident(as.character(x)), ") IGNORE NULLS", con = con)
}
globalVariables("last_value")
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