Title: Fast Tidying of Data
Version: 0.4.0
Description: Tidying functions built on 'data.table' to provide quick and efficient data manipulation with minimal overhead.
Depends: R (≥ 3.1)
Imports: data.table (≥ 1.13.4), cpp11
Suggests: covr, dplyr, magrittr, remotes, spelling, testthat (≥ 3.0.0), tidyr, knitr
LinkingTo: cpp11 (≥ 0.2.6)
Encoding: UTF-8
Language: en-US
License: GPL-3
RoxygenNote: 7.3.0
Config/testthat/edition: 3
Config/testthat/parallel: true
NeedsCompilation: yes
Packaged: 2024-02-02 08:53:21 UTC; tysonbarrett
Author: Tyson Barrett ORCID iD [aut, cre], Mark Fairbanks [ctb], Ivan Leung [ctb], Indrajeet Patil ORCID iD [ctb] (Twitter: @patilindrajeets)
Maintainer: Tyson Barrett <t.barrett88@gmail.com>
Repository: CRAN
Date/Publication: 2024-02-02 09:30:03 UTC

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 t.barrett88@gmail.com (ORCID)

Other contributors:

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(...)

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
)]

Count

Description

Count the numbers of observations within groups

Usage

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)

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
)

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")

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_, ...)

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)

Fast Nesting

Description

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

Usage

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)

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,
  ...
)

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"
)

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)

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)

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
)

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
)

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,
  ...
)

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

Select helpers

Description

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

Usage

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"))
)

Uncount

Description

Uncount a counted data table

Usage

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

Unnest: Fast Unnesting of Data Tables

Description

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

Usage

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)

fcase from data.table

Description

See data.table::fcase() for details.