Package 'tidyfast' reference manual

Title:	Fast Tidying of Data
Description:	Tidying functions built on 'data.table' to provide quick and efficient data manipulation with minimal overhead.
Authors:	Tyson Barrett [aut, cre] , Mark Fairbanks [ctb], Ivan Leung [ctb], Indrajeet Patil [ctb] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets)
Maintainer:	Tyson Barrett <[email protected]>
License:	GPL-3
Version:	0.4.0
Built:	2024-09-05 04:53:15 UTC
Source:	https://github.com/TysonStanley/tidyfast

tidyfast: Fast Tidying of Data

Description

Tidying functions built on 'data.table' to provide quick and efficient data manipulation with minimal overhead.

Author(s)

Maintainer: Tyson Barrett [email protected] (ORCID)

Other contributors:

Mark Fairbanks [contributor]
Ivan Leung [contributor]
Indrajeet Patil [email protected] (ORCID) (@patilindrajeets) [contributor]

Case When with data.table

Description

Does what dplyr::case_when() does, with the same syntax, but with data.table::fcase() under the hood.

Usage

dt_case_when(...)
dt_case_when(...)

Arguments

...

statements of the form: condition ~ label, where the label is applied if the condition is met

Value

Vector of the same size as the input vector

Examples


x <- rnorm(100)
dt_case_when(
  x < median(x) ~ "low",
  x >= median(x) ~ "high",
  is.na(x) ~ "other"
)

library(data.table)
temp <- data.table(
  pseudo_id = c(1, 2, 3, 4, 5),
  x = sample(1:5, 5, replace = TRUE)
)
temp[, y := dt_case_when(
  pseudo_id == 1 ~ x * 1,
  pseudo_id == 2 ~ x * 2,
  pseudo_id == 3 ~ x * 3,
  pseudo_id == 4 ~ x * 4,
  pseudo_id == 5 ~ x * 5
)]
x <- rnorm(100)
dt_case_when(
  x < median(x) ~ "low",
  x >= median(x) ~ "high",
  is.na(x) ~ "other"
)

library(data.table)
temp <- data.table(
  pseudo_id = c(1, 2, 3, 4, 5),
  x = sample(1:5, 5, replace = TRUE)
)
temp[, y := dt_case_when(
  pseudo_id == 1 ~ x * 1,
  pseudo_id == 2 ~ x * 2,
  pseudo_id == 3 ~ x * 3,
  pseudo_id == 4 ~ x * 4,
  pseudo_id == 5 ~ x * 5
)]

Count

Description

Count the numbers of observations within groups

Usage

dt_count(dt_, ..., na.rm = FALSE, wt = NULL)
dt_count(dt_, ..., na.rm = FALSE, wt = NULL)

Arguments

`dt_`	the data table to uncount
`...`	groups
`na.rm`	should any rows with missingness be removed before the count? Default is `FALSE`.
`wt`	the wt assigned to the counts (same number of rows as the data)

Value

A data.table with counts for each group (or combination of groups)

Examples


library(data.table)
dt <- data.table(
  x = rnorm(1e5),
  y = runif(1e5),
  grp = sample(1L:3L, 1e5, replace = TRUE),
  wt = runif(1e5, 1, 100)
)

dt_count(dt, grp)
dt_count(dt, grp, na.rm = TRUE)
dt_count(dt, grp, na.rm = TRUE, wt = wt)
library(data.table)
dt <- data.table(
  x = rnorm(1e5),
  y = runif(1e5),
  grp = sample(1L:3L, 1e5, replace = TRUE),
  wt = runif(1e5, 1, 100)
)

dt_count(dt, grp)
dt_count(dt, grp, na.rm = TRUE)
dt_count(dt, grp, na.rm = TRUE, wt = wt)

Fill with data.table

Description

Fills in values, similar to tidyr::fill(), by within data.table. This function relies on the Rcpp functions that drive tidyr::fill() but applies them within data.table.

Usage

dt_fill(
  dt_,
  ...,
  id = NULL,
  .direction = c("down", "up", "downup", "updown"),
  immutable = TRUE
)
dt_fill(
  dt_,
  ...,
  id = NULL,
  .direction = c("down", "up", "downup", "updown"),
  immutable = TRUE
)

Arguments

`dt_`	the data table (or if not a data.table then it is coerced with as.data.table)
`...`	the columns to fill
`id`	the grouping variable(s) to fill within
`.direction`	either "down" or "up" (down fills values down, up fills values up), or "downup" (down first then up) or "updown" (up first then down)
`immutable`	If `TRUE`, `dt_` is treated as immutable (it will not be modified in place). Alternatively, you can set `immutable = FALSE` to modify the input object.

Value

A data.table with listed columns having values filled in

Examples


set.seed(84322)
library(data.table)

x <- 1:10
dt <- data.table(
  v1 = x,
  v2 = shift(x),
  v3 = shift(x, -1L),
  v4 = sample(c(rep(NA, 10), x), 10),
  grp = sample(1:3, 10, replace = TRUE)
)
dt_fill(dt, v2, v3, v4, id = grp, .direction = "downup")
dt_fill(dt, v2, v3, v4, id = grp)
dt_fill(dt, .direction = "up")
set.seed(84322)
library(data.table)

x <- 1:10
dt <- data.table(
  v1 = x,
  v2 = shift(x),
  v3 = shift(x, -1L),
  v4 = sample(c(rep(NA, 10), x), 10),
  grp = sample(1:3, 10, replace = TRUE)
)
dt_fill(dt, v2, v3, v4, id = grp, .direction = "downup")
dt_fill(dt, v2, v3, v4, id = grp)
dt_fill(dt, .direction = "up")

Hoist: Fast Unnesting of Vectors

Description

Quickly unnest vectors nested in list columns. Still experimental (has some potentially unexpected behavior in some situations)!

Usage

dt_hoist(dt_, ...)
dt_hoist(dt_, ...)

Arguments

`dt_`	the data table to unnest
`...`	the columns to unnest (must all be the sample length when unnested); use bare names of the variables

Examples


library(data.table)
dt <- data.table(
  x = rnorm(1e5),
  y = runif(1e5),
  nested1 = lapply(1:10, sample, 10, replace = TRUE),
  nested2 = lapply(c("thing1", "thing2"), sample, 10, replace = TRUE),
  id = 1:1e5
)

dt_hoist(dt, nested1, nested2)
library(data.table)
dt <- data.table(
  x = rnorm(1e5),
  y = runif(1e5),
  nested1 = lapply(1:10, sample, 10, replace = TRUE),
  nested2 = lapply(c("thing1", "thing2"), sample, 10, replace = TRUE),
  id = 1:1e5
)

dt_hoist(dt, nested1, nested2)

Fast Nesting

Description

Quickly nest data tables (similar to dplyr::group_nest()).

Usage

dt_nest(dt_, ..., .key = "data")
dt_nest(dt_, ..., .key = "data")

Arguments

`dt_`	the data table to nest
`...`	the variables to group by
`.key`	the name of the list column; default is "data"

Value

A data.table with a list column containing data.tables

Examples


library(data.table)
dt <- data.table(
  x = rnorm(1e5),
  y = runif(1e5),
  grp = sample(1L:3L, 1e5, replace = TRUE)
)

dt_nest(dt, grp)
library(data.table)
dt <- data.table(
  x = rnorm(1e5),
  y = runif(1e5),
  grp = sample(1L:3L, 1e5, replace = TRUE)
)

dt_nest(dt, grp)

Pivot data from wide to long

Description

dt_pivot_wider() "widens" data, increasing the number of columns and decreasing the number of rows. The inverse transformation is dt_pivot_longer(). Syntax based on the tidyr equivalents.

Usage

dt_pivot_longer(
  dt_,
  cols = NULL,
  names_to = "name",
  values_to = "value",
  values_drop_na = FALSE,
  ...
)
dt_pivot_longer(
  dt_,
  cols = NULL,
  names_to = "name",
  values_to = "value",
  values_drop_na = FALSE,
  ...
)

Arguments

`dt_`	The data table to pivot longer
`cols`	Column selection. If empty, uses all columns. Can use -colname to unselect column(s)
`names_to`	Name of the new "names" column. Must be a string.
`values_to`	Name of the new "values" column. Must be a string.
`values_drop_na`	If TRUE, rows will be dropped that contain NAs.
`...`	Additional arguments to pass to 'melt.data.table()'

Value

A reshaped data.table into longer format

Examples


library(data.table)
example_dt <- data.table(x = c(1, 2, 3), y = c(4, 5, 6), z = c("a", "b", "c"))

dt_pivot_longer(example_dt,
  cols = c(x, y),
  names_to = "stuff",
  values_to = "things"
)

dt_pivot_longer(example_dt,
  cols = -z,
  names_to = "stuff",
  values_to = "things"
)
library(data.table)
example_dt <- data.table(x = c(1, 2, 3), y = c(4, 5, 6), z = c("a", "b", "c"))

dt_pivot_longer(example_dt,
  cols = c(x, y),
  names_to = "stuff",
  values_to = "things"
)

dt_pivot_longer(example_dt,
  cols = -z,
  names_to = "stuff",
  values_to = "things"
)

Pivot data from long to wide

Description

dt_pivot_wider() "widens" data, increasing the number of columns and decreasing the number of rows. The inverse transformation is dt_pivot_longer(). Syntax based on the tidyr equivalents.

Usage

dt_pivot_wider(dt_, id_cols = NULL, names_from, names_sep = "_", values_from)
dt_pivot_wider(dt_, id_cols = NULL, names_from, names_sep = "_", values_from)

Arguments

`dt_`	the data table to widen
`id_cols`	A set of columns that uniquely identifies each observation. Defaults to all columns in the data table except for the columns specified in `names_from` and `values_from`. Typically used when you have additional variables that is directly related.
`names_from`	A pair of arguments describing which column (or columns) to get the name of the output column (`name_from`), and which column (or columns) to get the cell values from (`values_from`).
`names_sep`	the separator between the names of the columns
`values_from`	A pair of arguments describing which column (or columns) to get the name of the output column (`name_from`), and which column (or columns) to get the cell values from (`values_from`).

Value

A reshaped data.table into wider format

Examples


library(data.table)
example_dt <- data.table(
  z = rep(c("a", "b", "c"), 2),
  stuff = c(rep("x", 3), rep("y", 3)),
  things = 1:6
)

dt_pivot_wider(example_dt, names_from = stuff, values_from = things)
dt_pivot_wider(example_dt, names_from = stuff, values_from = things, id_cols = z)
library(data.table)
example_dt <- data.table(
  z = rep(c("a", "b", "c"), 2),
  stuff = c(rep("x", 3), rep("y", 3)),
  things = 1:6
)

dt_pivot_wider(example_dt, names_from = stuff, values_from = things)
dt_pivot_wider(example_dt, names_from = stuff, values_from = things, id_cols = z)

Set Print Method

Description

The function allows the user to define options relating to the print method for data.table.

Usage

dt_print_options(
  class = TRUE,
  topn = 5,
  rownames = TRUE,
  nrows = 100,
  trunc.cols = TRUE
)
dt_print_options(
  class = TRUE,
  topn = 5,
  rownames = TRUE,
  nrows = 100,
  trunc.cols = TRUE
)

Arguments

`class`	should the variable class be printed? (`options("datatable.print.class")`)
`topn`	the number of rows to print (both head and tail) if `nrows(DT) > nrows`. (`options("datatable.print.topn")`)
`rownames`	should rownames be printed? (`options("datatable.print.rownames")`)
`nrows`	total number of rows to print (`options("datatable.print.nrows")`)
`trunc.cols`	if `TRUE`, only the columns that fit in the console are printed (with a message stating the variables not shown, similar to `tibbles`; `options("datatable.print.trunc.cols")`). This only works on `data.table` versions higher than `1.12.6` (i.e. not currently available but anticipating the eventual release).

Value

None. This function is used for its side effect of changing options.

Examples


dt_print_options(
  class = TRUE,
  topn = 5,
  rownames = TRUE,
  nrows = 100,
  trunc.cols = TRUE
)
dt_print_options(
  class = TRUE,
  topn = 5,
  rownames = TRUE,
  nrows = 100,
  trunc.cols = TRUE
)

Separate columns with data.table

Description

Separates a column of data into others, by splitting based a separator or regular expression

Usage

dt_separate(
  dt_,
  col,
  into,
  sep = ".",
  remove = TRUE,
  fill = NA,
  fixed = TRUE,
  immutable = TRUE,
  dev = FALSE,
  ...
)
dt_separate(
  dt_,
  col,
  into,
  sep = ".",
  remove = TRUE,
  fill = NA,
  fixed = TRUE,
  immutable = TRUE,
  dev = FALSE,
  ...
)

Arguments

`dt_`	the data table (or if not a data.table then it is coerced with as.data.table)
`col`	the column to separate
`into`	the names of the new columns created from splitting `col`.
`sep`	the regular expression stating how `col` should be separated. Default is `.`.
`remove`	should `col` be removed in the returned data table? Default is `TRUE`
`fill`	if empty, fill is inserted. Default is `NA`.
`fixed`	logical. If TRUE match split exactly, otherwise use regular expressions. Has priority over perl.
`immutable`	If `TRUE`, `.dt` is treated as immutable (it will not be modified in place). Alternatively, you can set `immutable = FALSE` to modify the input object.
`dev`	If `TRUE`, the function can be used within other functions. It bypasses the usual non-standard evaluation. Default is `FALSE`.
`...`	arguments passed to `data.table::tstrplit()`

Value

A data.table with a column split into multiple columns.

Examples


library(data.table)
d <- data.table(
  x = c("A.B", "A", "B", "B.A"),
  y = 1:4
)

# defaults
dt_separate(d, x, c("c1", "c2"))

# can keep the original column with `remove = FALSE`
dt_separate(d, x, c("c1", "c2"), remove = FALSE)

# need to assign when `immutable = TRUE`
separated <- dt_separate(d, x, c("c1", "c2"), immutable = TRUE)
separated

# don't need to assign when `immutable = FALSE` (default)
dt_separate(d, x, c("c1", "c2"), immutable = FALSE)
d
library(data.table)
d <- data.table(
  x = c("A.B", "A", "B", "B.A"),
  y = 1:4
)

# defaults
dt_separate(d, x, c("c1", "c2"))

# can keep the original column with `remove = FALSE`
dt_separate(d, x, c("c1", "c2"), remove = FALSE)

# need to assign when `immutable = TRUE`
separated <- dt_separate(d, x, c("c1", "c2"), immutable = TRUE)
separated

# don't need to assign when `immutable = FALSE` (default)
dt_separate(d, x, c("c1", "c2"), immutable = FALSE)
d

Select helpers

Description

These functions allow you to select variables based on their names.

dt_starts_with(): Starts with a prefix
dt_starts_with(): Ends with a suffix
dt_contains(): Contains a literal string
dt_everything(): Matches all variables

Usage

dt_starts_with(match)

dt_contains(match)

dt_ends_with(match)

dt_everything()
dt_starts_with(match)

dt_contains(match)

dt_ends_with(match)

dt_everything()

Arguments

match

a character string to match to variable names

Value

None. To be used within the ⁠dt_pivot_*⁠ functions.

Examples

library(data.table)

# example of using it with `dt_pivot_longer()`
df <- data.table(row = 1, var = c("x", "y"), a = 1:2, b = 3:4)
pv <- dt_pivot_wider(df,
  names_from = var,
  values_from = c(dt_starts_with("a"), dt_ends_with("b"))
)
library(data.table)

# example of using it with `dt_pivot_longer()`
df <- data.table(row = 1, var = c("x", "y"), a = 1:2, b = 3:4)
pv <- dt_pivot_wider(df,
  names_from = var,
  values_from = c(dt_starts_with("a"), dt_ends_with("b"))
)

Uncount

Description

Uncount a counted data table

Usage

dt_uncount(dt_, weights, .remove = TRUE, .id = NULL)
dt_uncount(dt_, weights, .remove = TRUE, .id = NULL)

Arguments

`dt_`	the data table to uncount
`weights`	the counts for each
`.remove`	should the weights variable be removed?
`.id`	an optional new id variable, providing a unique id for each row

Value

A data.table with a row for each uncounted column.

Examples


library(data.table)

dt_count <- data.table(
  x = LETTERS[1:3],
  w = c(2, 1, 4)
)
uncount <- dt_uncount(dt_count, w, .id = "id")
uncount[] # note that `[]` forces the printing
library(data.table)

dt_count <- data.table(
  x = LETTERS[1:3],
  w = c(2, 1, 4)
)
uncount <- dt_uncount(dt_count, w, .id = "id")
uncount[] # note that `[]` forces the printing

Unnest: Fast Unnesting of Data Tables

Description

Quickly unnest data tables, particularly those nested by dt_nest().

Usage

dt_unnest(dt_, col, keep = TRUE)
dt_unnest(dt_, col, keep = TRUE)

Arguments

`dt_`	the data table to unnest
`col`	the column to unnest
`keep`	whether to keep the nested column, default is `TRUE`

Examples


library(data.table)
dt <- data.table(
  x = rnorm(1e5),
  y = runif(1e5),
  grp = sample(1L:3L, 1e5, replace = TRUE)
)

nested <- dt_nest(dt, grp)
dt_unnest(nested, col = data)
library(data.table)
dt <- data.table(
  x = rnorm(1e5),
  y = runif(1e5),
  grp = sample(1L:3L, 1e5, replace = TRUE)
)

nested <- dt_nest(dt, grp)
dt_unnest(nested, col = data)

Package 'tidyfast'

Help Index

tidyfast: Fast Tidying of Data

Description

Author(s)

Case When with data.table

Description

Usage

Arguments

Value

Examples

Count

Description

Usage

Arguments

Value

Examples

Fill with data.table

Description

Usage

Arguments

Value

Examples

Hoist: Fast Unnesting of Vectors

Description

Usage

Arguments

Examples

Fast Nesting

Description

Usage

Arguments

Value

Examples

Pivot data from wide to long

Description

Usage

Arguments

Value

Examples

Pivot data from long to wide

Description

Usage

Arguments

Value

Examples

Set Print Method

Description

Usage

Arguments

Value

Examples

Separate columns with data.table

Description

Usage

Arguments

Value

Examples

Select helpers

Description

Usage

Arguments

Value

Examples

Uncount

Description

Usage

Arguments

Value

Examples

Unnest: Fast Unnesting of Data Tables

Description

Usage

Arguments

Examples