Package {tidier}

Drop-in replacement for dplyr::mutate

Description

Provides supercharged version of dplyr::mutate with .by (group by), .order_by and .frame aggregation over arbitrary window frame

Usage

mutate(x, ..., .by, .order_by, .frame, .complete = FALSE)

Arguments

Details

A window function returns a value for every input row of a dataframe 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

• rows_between: Number of rows before and after the corresponding row. Example: c(2, 1)

• range_between: Range is numeric or interval objects Example: c(days(2), days(1))

This implementation is inspired by spark's window API. The output has the same row order as the input independent of the order_by.

Value

data.frame

See Also

rows_between(), range_between(), mutate()

Examples

library("magrittr") # for pipe
# example 1: rows between
# Using iris dataset,
# compute cumulative mean of column `Sepal.Length`
# ordered by `Petal.Width` and `Sepal.Width` columns
# grouped by `Petal.Length` column

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

# example 2: range between
# 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) %>%
  # 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 = date_col,
                 .by = Month,
                 .frame = range_between(
                            lubridate::days(7), # 7 days before current row
                            lubridate::days(-1) # do not include current row
                            )
                 )

# example 3: custom function / modeling over window frame
fit_lm_safe = function(df, formula, min_rows = 2) {
  if (is.null(df) || nrow(df) < min_rows) {
    return(NULL)
  }

  tryCatch(
    lm(formula, data = df),
    error = function(e) NULL
  )
}

mtcars %>%
  mutate(s = list(fit_lm_safe(pick(everything()), mpg ~ .)),
         .by = c(cyl, vs),
         .order_by = qsec,
         .frame = range_between(-1, 3),
         .complete = FALSE
         ) %>%
  head(10)

## Not run: 
# example 4: parallel execution across many groups using tidyr::nest and furrr
future::plan(future::multisession(workers = 2))

iris %>%
  tidyr::nest(.by = Species) %>%
  dplyr::mutate(
    data = furrr::future_map(data, ~ .x %>%
                        mutate(
                          sl_mean = mean(Sepal.Length),
                          .order_by = Petal.Width,
                          .frame = rows_between(2, 2)
                        )
                     )
  ) %>%
  tidyr::unnest(data)

## End(Not run)

Create a frame indicating range between window

Description

Create a frame indicating range between window

Usage

range_between(before, after)

Arguments

Value

object of class range_between_frame, frame

See Also

rows_between(), range_between(), mutate()

Remove non-list columns when same are present in a list column

Description

Remove non-list columns when same are present in a list column

Usage

remove_common_nested_columns(df, list_column)

Arguments

Value

dataframe

Create a frame indicating rows between window

Description

Create a frame indicating rows between window

Usage

rows_between(before, after)

Arguments

Value

object of class rows_between_frame, frame

See Also

rows_between(), range_between(), mutate()