tidier (version 0.2.0)

mutate_: Drop-in replacement for mutate

Description

Provides supercharged version of mutate with group_by, order_by and aggregation over arbitrary window frame around a row for dataframes and lazy (remote) tbls of class tbl_lazy.

Usage

mutate_(
  x,
  ...,
  .by,
  .order_by,
  .frame,
  .index,
  .desc = FALSE,
  .complete = FALSE
)

Value

data.frame or tbl_lazy

Arguments

x

(data.frame or tbl_lazy)

...

expressions to be passed to mutate

.by

(character vector, optional: Yes) Columns to group by

.order_by

(string, optional: Yes) Columns to order by

.frame

(vector, optional: Yes) Vector of length 2 indicating the number of rows to consider before and after the current row. When argument .index is provided (typically a column of type date or datetime), before and after can be interval objects. See examples. When input is tbl_lazy, only number of rows as vector of length 2 is supported.

.index

(string, optional: Yes, default: NULL) index column. This is supported when input is a dataframe only.

.desc

(flag, default: FALSE) Whether to order in descending order

.complete

(flag, default: FALSE) This will be passed to slider::slide / slider::slide_vec. Should the function be evaluated on complete windows only? If FALSE or NULL, the default, then partial computations will be allowed. This is supported when input is a dataframe only.

Details

A window function returns a value for every input row of a dataframe or lazy_tbl based on a group of rows (frame) in the neighborhood of the input row. This function implements computation over groups (partition_by in SQL) in a predefined order (order_by in SQL) across a neighborhood of rows (frame) defined by a (up, down) where

• up/down are number of rows before and after the corresponding row

• up/down are interval objects (ex: c(days(2), days(1))). Interval objects are currently supported for dataframe only. (not tbl_lazy)

This implementation is inspired by spark's window API.

Implementation Details:

For dataframe input:

• Iteration per row over the window is implemented using the versatile slider.

• Application of a window aggregation can be optionally run in parallel over multiple groups (see argument .by) by setting a future parallel backend. This is implemented using furrr package.

• function subsumes regular usecases of mutate

For tbl_lazy input:

• Uses dbplyr::window_order and dbplyr::window_frame to translate to partition_by and window frame specification.

See Also

mutate

Examples

library("magrittr")
# example 1 (simple case with dataframe)
# Using iris dataset,
# compute cumulative mean of column `Sepal.Length`
# ordered by `Petal.Width` and `Sepal.Width` columns
# grouped by `Petal.Length` column

iris %>%
  tidier::mutate_(sl_mean = mean(Sepal.Length),
                  .order_by = c("Petal.Width", "Sepal.Width"),
                  .by = "Petal.Length",
                  .frame = c(Inf, 0),
                  ) %>%
  dplyr::slice_min(n = 3, Petal.Width, by = Species)

# example 2 (detailed case with dataframe)
# Using a sample airquality dataset,
# compute mean temp over last seven days in the same month for every row

set.seed(101)
airquality %>%
  # create date column
  dplyr::mutate(date_col = lubridate::make_date(1973, Month, Day)) %>%
  # create gaps by removing some days
  dplyr::slice_sample(prop = 0.8) %>%
  dplyr::arrange(date_col) %>%
  # compute mean temperature over last seven days in the same month
  tidier::mutate_(avg_temp_over_last_week = mean(Temp, na.rm = TRUE),
                  .order_by = "Day",
                  .by = "Month",
                  .frame = c(lubridate::days(7), # 7 days before current row
                            lubridate::days(-1) # do not include current row
                            ),
                  .index = "date_col"
                  )
# example 3
airquality %>%
   # create date column as character
   dplyr::mutate(date_col =
                   as.character(lubridate::make_date(1973, Month, Day))
                 ) %>%
   tibble::as_tibble() %>%
   # as `tbl_lazy`
   dbplyr::memdb_frame() %>%
   mutate_(avg_temp = mean(Temp),
           .by = "Month",
           .order_by = "date_col",
           .frame = c(3, 3)
           ) %>%
   dplyr::collect() %>%
   dplyr::select(Ozone, Solar.R, Wind, Temp, Month, Day, date_col, avg_temp)