Title:	Tidy Verbs for Fast Data Manipulation
Version:	1.8.2
Depends:	R (≥ 4.1.0)
Description:	A toolkit of tidy data manipulation verbs with 'data.table' as the backend. Combining the merits of syntax elegance from 'dplyr' and computing performance from 'data.table', 'tidyfst' intends to provide users with state-of-the-art data manipulation tools with least pain. This package is an extension of 'data.table'. While enjoying a tidy syntax, it also wraps combinations of efficient functions to facilitate frequently-used data operations.
URL:	https://github.com/hope-data-science/tidyfst, https://hope-data-science.github.io/tidyfst/
BugReports:	https://github.com/hope-data-science/tidyfst/issues
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	data.table (≥ 1.15.0), fst (≥ 0.9.0), stringr (≥ 1.5.0)
Suggests:	knitr, rmarkdown, nycflights13, pryr, tidyr, ggplot2, dplyr, bench, testthat, tidytext
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-05-07 04:55:13 UTC; Admin
Author:	Tian-Yuan Huang [aut, cre]
Maintainer:	Tian-Yuan Huang <huang.tian-yuan@qq.com>
Repository:	CRAN
Date/Publication:	2025-05-07 05:20:02 UTC

Arrange entries in data.frame

Description

Order the rows of a data frame rows by the values of selected columns.

Usage

arrange_dt(.data, ...)

Arguments

Value

data.table

See Also

arrange

Examples

iris %>% arrange_dt(Sepal.Length)

# minus for decreasing order
iris %>% arrange_dt(-Sepal.Length)

# arrange by multiple variables
iris %>% arrange_dt(Sepal.Length,Petal.Length)

Save a data.frame as a fst table

Description

This function first export the data.frame to a temporal file, and then parse it back as a fst table (class name is "fst_table").

Usage

as_fst(.data)

Arguments

Value

An object of class fst_table

Examples

## Not run: 
  iris %>%
    as_fst() -> iris_fst
  iris_fst

## End(Not run)

Bind multiple data frames by row

Description

Bind any number of data frames by row, making a longer result. Similar to 'dplyr::bind_rows', however, columns with same names but different data types would be coerced to a single proper data type.

Usage

bind_rows_dt(...)

Arguments

Value

data.table

See Also

bind_rows,rbindlist

Examples

bind_rows_dt(iris[1:3,],iris[6:8,])

# data frames with same name but different type
# numeric data would be coerced to character data in this case
df1 <- data.frame(x = 1:2, y = letters[1:2])
df2 <- data.frame(x = 4:5, y = 1:2)
bind_rows_dt(df1, df2)

Compute TF–IDF Using data.table with Optional Counting and Grouping

Description

This function computes term frequency–inverse document frequency (tf–idf) on a dataset with one row per term occurrence (or pre-counted). It preserves original column names and returns new columns: - 'n': raw count (computed or user-supplied) - 'tf': term frequency per document - 'idf': inverse document frequency per group (or corpus) - 'tf_idf': tf × idf If 'group_col' is 'NULL', all documents are treated as a single group.

Usage

bind_tf_idf_dt(.data, group_col = NULL, doc_col, term_col, n_col = NULL)

Arguments

Value

A data.table containing: - Original grouping, document, and term columns - 'n', 'tf', 'idf', and 'tf_idf'

See Also

bind_tf_idf

Examples

# With groups
df <- data.frame(
  category = rep(c("A","B"), each = 6),
  doc_id   = rep(c("d1","d2","d3"), times = 4),
  word     = c("apple","banana","apple","banana","cherry","apple",
               "dog","cat","dog","mouse","cat","dog"),
  stringsAsFactors = FALSE
)
result <- bind_tf_idf_dt(df, "category", "doc_id", "word")
result

# Without groups
df %>%
  filter_dt(category == "A") %>%
  bind_tf_idf_dt(doc_col = "doc_id",term_col = "word")

# With counts provided
df %>%
  filter_dt(category == "A") %>%
  count_dt() %>%
  bind_tf_idf_dt(doc_col = "doc_id",term_col = "word",n_col = "n")
df %>%
  count_dt() %>%
  bind_tf_idf_dt(group_col = "category",
                 doc_col = "doc_id",
                 term_col = "word",n_col = "n")

Get the column name of the max/min number each row

Description

For a data.frame with numeric values, add a new column specifying the column name of the first max/min value each row.

Usage

col_max(.data, ..., .name = "max_col")

col_min(.data, ..., .name = "min_col")

Arguments

Value

A data.table

References

https://stackoverflow.com/questions/17735859/for-each-row-return-the-column-name-of-the-largest-value

Examples

set.seed(199057)
DT <- data.table(matrix(sample(10, 100, TRUE), ncol=10))
DT
col_max(DT)
col_max(DT,V1:V3)
col_max(DT,.name = "max_col_name")
col_min(DT)
col_min(DT,2:4)

col_max(iris)

Complete a data frame with missing combinations of data

Description

Turns implicit missing values into explicit missing values. All the combinations of column values (should be unique) will be constructed. Other columns will be filled with NAs or constant value.

Usage

complete_dt(.data, ..., fill = NA)

Arguments

Details

When the provided columns with addtion data are of different length, all the unique combinations would be returned. This operation should be used only on unique entries, and it will always returned the unique entries.

If you supply fill parameter, these values will also replace existing explicit missing values in the data set.

Value

data.table

See Also

complete

Examples

df <- data.table(
  group = c(1:2, 1),
  item_id = c(1:2, 2),
  item_name = c("a", "b", "b"),
  value1 = 1:3,
  value2 = 4:6
)

df %>% complete_dt(item_id,item_name)
df %>% complete_dt(item_id,item_name,fill = 0)
df %>% complete_dt("item")
df %>% complete_dt(item_id=1:3)
df %>% complete_dt(item_id=1:3,group=1:2)
df %>% complete_dt(item_id=1:3,group=1:3,item_name=c("a","b","c"))

Count observations by group

Description

Count the unique values of one or more variables.

Usage

count_dt(.data, ..., sort = TRUE, .name = "n")

add_count_dt(.data, ..., .name = "n")

Arguments

Value

data.table

See Also

count

Examples

iris %>% count_dt(Species)
iris %>% count_dt(Species,.name = "count")
iris %>% add_count_dt(Species)
iris %>% add_count_dt(Species,.name = "N")

mtcars %>% count_dt(cyl,vs)
mtcars %>% count_dt("cyl|vs")
mtcars %>% count_dt(cyl,vs,.name = "N",sort = FALSE)
mtcars %>% add_count_dt(cyl,vs)
mtcars %>% add_count_dt("cyl|vs")

Cumulative mean

Description

Returns a vector whose elements are the cumulative mean of the elements of the argument.

Usage

cummean(x)

Arguments

Examples

cummean(1:10)

Select distinct/unique rows in data.frame

Description

Select only unique/distinct rows from a data frame.

Usage

distinct_dt(.data, ..., .keep_all = FALSE, fromLast = FALSE)

Arguments

Value

data.table

See Also

distinct

Examples

iris %>% distinct_dt()
iris %>% distinct_dt(Species)
iris %>% distinct_dt(Species,.keep_all = TRUE)
mtcars %>% distinct_dt(cyl,vs)
mtcars %>% distinct_dt(cyl,vs,.keep_all = TRUE)
mtcars %>% distinct_dt(cyl,vs,.keep_all = TRUE,fromLast = TRUE)

Dump, replace and fill missing values in data.frame

Description

A set of tools to deal with missing values in data.frames. It can dump, replace, fill (with next or previous observation) or delete entries according to their missing values.

Usage

drop_na_dt(.data, ...)

replace_na_dt(.data, ..., to)

delete_na_cols(.data, prop = NULL, n = NULL)

delete_na_rows(.data, prop = NULL, n = NULL)

fill_na_dt(.data, ..., direction = "down")

shift_fill(x, direction = "down")

Arguments

Details

drop_na_dt drops the entries with NAs in specific columns. fill_na_dt fill NAs with observations ahead ("down") or below ("up"), which is also known as last observation carried forward (LOCF) and next observation carried backward(NOCB).

delete_na_cols could drop the columns with NA proportion larger than or equal to "prop" or NA number larger than or equal to "n", delete_na_rows works alike but deals with rows.

shift_fill could fill a vector with missing values.

Value

data.table

References

https://stackoverflow.com/questions/23597140/how-to-find-the-percentage-of-nas-in-a-data-frame

https://stackoverflow.com/questions/2643939/remove-columns-from-dataframe-where-all-values-are-na

https://stackoverflow.com/questions/7235657/fastest-way-to-replace-nas-in-a-large-data-table

See Also

drop_na,replace_na, fill

Examples

df <- data.table(x = c(1, 2, NA), y = c("a", NA, "b"))
 df %>% drop_na_dt()
 df %>% drop_na_dt(x)
 df %>% drop_na_dt(y)
 df %>% drop_na_dt(x,y)

 df %>% replace_na_dt(to = 0)
 df %>% replace_na_dt(x,to = 0)
 df %>% replace_na_dt(y,to = 0)
 df %>% replace_na_dt(x,y,to = 0)

 df %>% fill_na_dt(x)
 df %>% fill_na_dt() # not specified, fill all columns
 df %>% fill_na_dt(y,direction = "up")

x = data.frame(x = c(1, 2, NA, 3), y = c(NA, NA, 4, 5),z = rep(NA,4))
x
x %>% delete_na_cols()
x %>% delete_na_cols(prop = 0.75)
x %>% delete_na_cols(prop = 0.5)
x %>% delete_na_cols(prop = 0.24)
x %>% delete_na_cols(n = 2)

x %>% delete_na_rows(prop = 0.6)
x %>% delete_na_rows(n = 2)

# shift_fill
y = c("a",NA,"b",NA,"c")

shift_fill(y) # equals to
shift_fill(y,"down")

shift_fill(y,"up")

Fast creation of dummy variables

Description

Quickly create dummy (binary) columns from character and factor type columns in the inputted data (and numeric columns if specified.) This function is useful for statistical analysis when you want binary columns rather than character columns.

Usage

dummy_dt(.data, ..., longname = TRUE)

Arguments

Details

If no columns provided, will return the original data frame. When NA exist in the input column, they would also be considered. If the input character column contains both NA and string "NA", they would be merged.

This function is inspired by fastDummies package, but provides simple and precise usage, whereas fastDummies::dummy_cols provides more features for statistical usage.

Value

data.table

References

https://stackoverflow.com/questions/18881073/creating-dummy-variables-in-r-data-table

See Also

dummy_cols

Examples

iris %>% dummy_dt(Species)
iris %>% dummy_dt(Species,longname = FALSE)

mtcars %>% head() %>% dummy_dt(vs,am)
mtcars %>% head() %>% dummy_dt("cyl|gear")

# when there are NAs in the column
df <- data.table(x = c("a", "b", NA, NA),y = 1:4)
df %>%
  dummy_dt(x)

# when NA  and "NA" both exist, they would be merged
df <- data.table(x = c("a", "b", NA, "NA"),y = 1:4)
df %>%
  dummy_dt(x)

Read and write fst files

Description

Wrapper for read_fst and write_fst from fst, but use a different default. For data import, always return a data.table. For data export, always compress the data to the smallest size.

Usage

export_fst(x, path, compress = 100, uniform_encoding = TRUE)

import_fst(
  path,
  columns = NULL,
  from = 1,
  to = NULL,
  as.data.table = TRUE,
  old_format = FALSE
)

Arguments

Value

'import_fst' returns a data.table with the selected columns and rows. 'export_fst' writes 'x' to a 'fst' file and invisibly returns 'x' (so you can use this function in a pipeline).

See Also

read_fst

Examples

## Not run: 
export_fst(iris,"iris_fst_test.fst")
iris_dt = import_fst("iris_fst_test.fst")
iris_dt
unlink("iris_fst_test.fst")

## End(Not run)

Filter entries in data.frame

Description

Choose rows where conditions are true.

Usage

filter_dt(.data, ...)

Arguments

Value

data.table

See Also

filter

Examples

iris %>% filter_dt(Sepal.Length > 7)
iris %>% filter_dt(Sepal.Length == max(Sepal.Length))

# comma is not supported in tidyfst after v0.9.8
# which means you can't use:
## Not run: 
 iris %>% filter_dt(Sepal.Length > 7, Sepal.Width > 3)

## End(Not run)
# use following code instead
iris %>% filter_dt(Sepal.Length > 7 & Sepal.Width > 3)

Parse,inspect and extract data.table from fst file

Description

A tookit of APIs for reading fst file as data.table, could select by column, row and conditional filtering.

Usage

parse_fst(path)

slice_fst(ft, row_no)

select_fst(ft, ...)

filter_fst(ft, ...)

summary_fst(ft)

Arguments

Details

summary_fst could provide some basic information about the fst table.

Value

parse_fst returns a fst_table class.

select_fst and filter_fst returns a data.table.

See Also

fst, metadata_fst

Examples

## Not run: 
  fst::write_fst(iris,"iris_test.fst")
  # parse the file but not reading it
  parse_fst("iris_test.fst") -> ft
  ft

  class(ft)
  lapply(ft,class)
  names(ft)
  dim(ft)
  summary_fst(ft)

  # get the data by query
  ft %>% slice_fst(1:3)
  ft %>% slice_fst(c(1,3))

  ft %>% select_fst(Sepal.Length)
  ft %>% select_fst(Sepal.Length,Sepal.Width)
  ft %>% select_fst("Sepal.Length")
  ft %>% select_fst(1:3)
  ft %>% select_fst(1,3)
  ft %>% select_fst("Se")
  ft %>% select_fst("nothing")
  ft %>% select_fst("Se|Sp")
  ft %>% select_fst(cols = names(iris)[2:3])

  ft %>% filter_fst(Sepal.Width > 3)
  ft %>% filter_fst(Sepal.Length > 6 , Species == "virginica")
  ft %>% filter_fst(Sepal.Length > 6 & Species == "virginica" & Sepal.Width < 3)

  unlink("iris_test.fst")

## End(Not run)

Group by variable(s) and implement operations

Description

Carry out data manipulation within specified groups. Different from group_dt, the implementation is split into two operations, namely grouping and implementation.

Using setkey and setkeyv in data.table to carry out group_by-like functionalities in dplyr. This is not only convenient but also efficient in computation.

Usage

group_by_dt(.data, ..., cols = NULL)

group_exe_dt(.data, ...)

Arguments

Details

group_by_dt and group_exe_dt are a pair of functions to be used in combination. It utilizes the feature of key setting in data.table, which provides high performance for group operations, especially when you have to operate by specific groups frequently.

Value

A data.table with keys

Examples

# aggregation after grouping using group_exe_dt
as.data.table(iris) -> a
a %>%
  group_by_dt(Species) %>%
  group_exe_dt(head(1))

a %>%
  group_by_dt(Species) %>%
  group_exe_dt(
    head(3) %>%
      summarise_dt(sum = sum(Sepal.Length))
  )

mtcars %>%
  group_by_dt("cyl|am") %>%
  group_exe_dt(
    summarise_dt(mpg_sum = sum(mpg))
  )
# equals to
mtcars %>%
  group_by_dt(cols = c("cyl","am")) %>%
  group_exe_dt(
    summarise_dt(mpg_sum = sum(mpg))
  )

Data manipulation within groups

Description

Carry out data manipulation within specified groups.

Usage

group_dt(.data, by = NULL, ...)

rowwise_dt(.data, ...)

Arguments

Details

If you want to use summarise_dt and mutate_dt in group_dt, it is better to use the "by" parameter in those functions, that would be much faster because you don't have to use .SD (which takes extra time to copy).

Value

data.table

References

https://stackoverflow.com/questions/36802385/use-by-each-row-for-data-table

Examples

iris %>% group_dt(by = Species,slice_dt(1:2))
iris %>% group_dt(Species,filter_dt(Sepal.Length == max(Sepal.Length)))
iris %>% group_dt(Species,summarise_dt(new = max(Sepal.Length)))

# you can pipe in the `group_dt`
iris %>% group_dt(Species,
                  mutate_dt(max= max(Sepal.Length)) %>%
                    summarise_dt(sum=sum(Sepal.Length)))

# for users familiar with data.table, you can work on .SD directly
# following codes get the first and last row from each group
iris %>%
  group_dt(
    by = Species,
    rbind(.SD[1],.SD[.N])
  )

#' # for summarise_dt, you can use "by" to calculate within the group
mtcars %>%
  summarise_dt(
   disp = mean(disp),
   hp = mean(hp),
   by = cyl
)

  # but you could also, of course, use group_dt
 mtcars %>%
   group_dt(by =.(vs,am),
     summarise_dt(avg = mean(mpg)))

  # and list of variables could also be used
 mtcars %>%
   group_dt(by =list(vs,am),
            summarise_dt(avg = mean(mpg)))

# examples for `rowwise_dt`
df <- data.table(x = 1:2, y = 3:4, z = 4:5)

df %>% mutate_dt(m = mean(c(x, y, z)))

df %>% rowwise_dt(
  mutate_dt(m = mean(c(x, y, z)))
)

Read a fst file by chunks

Description

For 'import_fst_chunked', if a large fst file which could not be imported into the memory all at once, this function could read the fst file by chunks and preprocessed the chunk to ensure the results yielded by the chunks are small enough to be summarised in the end. For 'get_fst_chunk_size', this function can measure the memory used by a specified row number.

Usage

import_fst_chunked(
  path,
  chunk_size = 10000L,
  chunk_f = identity,
  combine_f = rbindlist
)

get_fst_chunk_size(path, nrows)

Arguments

Value

For 'import_fst_chunked', default to the whole data.frame in data.table. Could be adjusted to any type. For 'get_fst_chunk_size', return the file size.

See Also

read_csv_chunked

Examples

## Not run: 
  # Generate some random data frame with 10 million rows and various column types
  nr_of_rows <- 1e7
  df <- data.frame(
    Logical = sample(c(TRUE, FALSE, NA), prob = c(0.85, 0.1, 0.05), nr_of_rows, replace = TRUE),
    Integer = sample(1L:100L, nr_of_rows, replace = TRUE),
    Real = sample(sample(1:10000, 20) / 100, nr_of_rows, replace = TRUE),
    Factor = as.factor(sample(labels(UScitiesD), nr_of_rows, replace = TRUE))
  )

  # Write the file to disk
  fst_file <- tempfile(fileext = ".fst")
  write_fst(df, fst_file)

  # Get the size of 10000 rows
  get_fst_chunk_size(fst_file,1e4)

  # File all rows that Integer == 7 by chunks
  import_fst_chunked(fst_file,chunk_f = \(x) x[Integer==7])


## End(Not run)

Impute missing values with mean, median or mode

Description

Impute the columns of data.frame with its mean, median or mode.

Usage

impute_dt(.data, ..., .func = "mode")

Arguments

Value

A data.table

Examples

Pclass <- c(3, 1, 3, 1, 3, 2, 2, 3, NA, NA)
Sex <- c('male', 'male', 'female', 'female', 'female',
         'female', NA, 'male', 'female', NA)
Age <- c(22, 38, 26, 35, NA,
         45, 25, 39, 28, 40)
SibSp <- c(0, 1, 3, 1, 2, 3, 2, 2, NA, 0)
Fare <- c(7.25, 71.3, 7.92, NA, 8.05, 8.46, 51.9, 60, 32, 15)
Embarked <- c('S', NA, 'S', 'Q', 'Q', 'S', 'C', 'S', 'C', 'S')
data <- data.frame('Pclass' = Pclass,
 'Sex' = Sex, 'Age' = Age, 'SibSp' = SibSp,
 'Fare' = Fare, 'Embarked' = Embarked)

data
data %>% impute_dt() # defalut uses "mode" as `.func`
data %>% impute_dt(is.numeric,.func = "mean")
data %>% impute_dt(is.numeric,.func = "median")

my_fun = function(x){
  x[is.na(x)] = (max(x,na.rm = TRUE) - min(x,na.rm = TRUE))/2
  x
}
data %>% impute_dt(is.numeric,.func = my_fun)

Short cut to data.table

Description

To use facilities provided by data.table, but do not have to load data.table package.

Usage

in_dt(.data, ...)

as_dt(.data)

Arguments

Details

The as_dt could turn any data frame to data.table class. If the data is not a data frame, return error.

The in_dt function creates a virtual environment in data.table, it could be piped well because it still follows the principals of tidyfst, which are: (1) Never use in place replacement and (2) Always recieves a data frame (data.frame/tibble/data.table) and returns a data.table. Therefore, the in place functions like := will still return the results.

See Also

data.table

Examples

iris %>% as_dt()
iris %>% in_dt(order(-Sepal.Length),.SD[.N],by=Species)

Set operations for data frames

Description

Wrappers of set operations in data.table. Only difference is it could be applied to non-data.table data frames by recognizing and coercing them to data.table automatically.

Usage

intersect_dt(x, y, all = FALSE)

union_dt(x, y, all = FALSE)

setdiff_dt(x, y, all = FALSE)

setequal_dt(x, y, all = TRUE)

Arguments

Value

A data.table

See Also

setops

Examples

x = iris[c(2,3,3,4),]
x2 = iris[2:4,]
y = iris[c(3:5),]

intersect_dt(x, y)            # intersect
intersect_dt(x, y, all=TRUE)  # intersect all
setdiff_dt(x, y)              # except
setdiff_dt(x, y, all=TRUE)    # except all
union_dt(x, y)                # union
union_dt(x, y, all=TRUE)      # union all
setequal_dt(x, x2, all=FALSE) # setequal
setequal_dt(x, x2)            # setequal all

Join tables

Description

The mutating joins add columns from 'y' to 'x', matching rows based on the keys:

* 'inner_join_dt()': includes all rows in 'x' and 'y'. * 'left_join_dt()': includes all rows in 'x'. * 'right_join_dt()': includes all rows in 'y'. * 'full_join_dt()': includes all rows in 'x' or 'y'.

Filtering joins filter rows from 'x' based on the presence or absence of matches in 'y':

* 'semi_join_dt()' return all rows from 'x' with a match in 'y'. * 'anti_join_dt()' return all rows from 'x' without a match in 'y'.

Usage

inner_join_dt(x, y, by = NULL, on = NULL, suffix = c(".x", ".y"))

left_join_dt(x, y, by = NULL, on = NULL, suffix = c(".x", ".y"))

right_join_dt(x, y, by = NULL, on = NULL, suffix = c(".x", ".y"))

full_join_dt(x, y, by = NULL, on = NULL, suffix = c(".x", ".y"))

anti_join_dt(x, y, by = NULL, on = NULL)

semi_join_dt(x, y, by = NULL, on = NULL)

Arguments

Value

A data.table

Examples

workers = fread("
    name company
    Nick Acme
    John Ajax
    Daniela Ajax
")

positions = fread("
    name position
    John designer
    Daniela engineer
    Cathie manager
")

workers %>% inner_join_dt(positions)
workers %>% left_join_dt(positions)
workers %>% right_join_dt(positions)
workers %>% full_join_dt(positions)

# filtering joins
workers %>% anti_join_dt(positions)
workers %>% semi_join_dt(positions)

# To suppress the message, supply 'by' argument
workers %>% left_join_dt(positions, by = "name")

# Use a named 'by' if the join variables have different names
positions2 = setNames(positions, c("worker", "position")) # rename first column in 'positions'
workers %>% inner_join_dt(positions2, by = c("name" = "worker"))

# the syntax of 'on' could be a bit different
workers %>% inner_join_dt(positions2,on = "name==worker")

Fast lead/lag for vectors

Description

Find the "next" or "previous" values in a vector. It has wrapped data.table's shift function.

Usage

lead_dt(x, n = 1L, fill = NA)

lag_dt(x, n = 1L, fill = NA)

Arguments

Value

A vector

See Also

lead,shift

Examples

lead_dt(1:5)
lag_dt(1:5)
lead_dt(1:5,2)
lead_dt(1:5,n = 2,fill = 0)

Pivot data from wide to long

Description

Turning a wide table to its longer form. It takes multiple columns and collapses into key-value pairs.

Usage

longer_dt(.data, ..., name = "name", value = "value", na.rm = FALSE)

Arguments

Value

A data.table

See Also

wider_dt, melt, pivot_longer

Examples

## Example 1:
stocks = data.frame(
  time = as.Date('2009-01-01') + 0:9,
  X = rnorm(10, 0, 1),
  Y = rnorm(10, 0, 2),
  Z = rnorm(10, 0, 4)
)

stocks

stocks %>%
  longer_dt(time)

stocks %>%
  longer_dt("ti")

# Example 2:


  library(tidyr)

  billboard %>%
    longer_dt(
      -"wk",
      name = "week",
      value = "rank",
      na.rm = TRUE
    )

  # or use:
  billboard %>%
    longer_dt(
      artist,track,date.entered,
      name = "week",
      value = "rank",
      na.rm = TRUE
    )

  # or use:
  billboard %>%
    longer_dt(
      1:3,
      name = "week",
      value = "rank",
      na.rm = TRUE
    )

Conversion between tidy table and named matrix

Description

Convenient fucntions to implement conversion between tidy table and named matrix.

Usage

mat_df(m)

df_mat(df, row, col, value)

Arguments

Value

For mat_df, a data.frame. For df_mat, a named matrix.

Examples

mm = matrix(c(1:8,NA),ncol = 3,dimnames = list(letters[1:3],LETTERS[1:3]))
mm
tdf = mat_df(mm)
tdf
mat = df_mat(tdf,row,col,value)
setequal(mm,mat)

tdf %>%
  setNames(c("A","B","C")) %>%
  df_mat(A,B,C)

Mutate columns in data.frame

Description

Adds or updates columns in data.frame.

Usage

mutate_dt(.data, ..., by)

transmute_dt(.data, ..., by)

Arguments

Value

data.table

See Also

mutate

Examples

iris %>% mutate_dt(one = 1,Sepal.Length = Sepal.Length + 1)
iris %>% transmute_dt(one = 1,Sepal.Length = Sepal.Length + 1)
# add group number with symbol `.GRP`
iris %>% mutate_dt(id = 1:.N,grp = .GRP,by = Species)

Conditional update of columns in data.table

Description

Update or add columns when the given condition is met.

mutate_when integrates mutate and case_when in dplyr and make a new tidy verb for data.table. mutate_vars is a super function to do updates in specific columns according to conditions.

Usage

mutate_when(.data, when, ..., by)

mutate_vars(.data, .cols = NULL, .func, ..., by)

Arguments

Value

data.table

See Also

select_dt, case_when

Examples

iris[3:8,]
iris[3:8,] %>%
  mutate_when(Petal.Width == .2,
              one = 1,Sepal.Length=2)

iris %>% mutate_vars("Pe",scale)
iris %>% mutate_vars(is.numeric,scale)
iris %>% mutate_vars(-is.factor,scale)
iris %>% mutate_vars(1:2,scale)
iris %>% mutate_vars(.func = as.character)

Nest and unnest

Description

Create or melt list columns in data.frame.

Analogous function for nest and unnest in tidyr. unnest_dt will automatically remove other list-columns except for the target list-columns (which would be unnested later). Also, squeeze_dt is designed to merge multiple columns into list column.

Usage

nest_dt(.data, ..., mcols = NULL, .name = "ndt")

unnest_dt(.data, ...)

squeeze_dt(.data, ..., .name = "ndt")

chop_dt(.data, ...)

unchop_dt(.data, ...)

Arguments

Details

In the nest_dt, the data would be nested to a column named 'ndt', which is short for nested data.table.

The squeeze_dt would not remove the originial columns.

The unchop_dt is the reverse operation of chop_dt.

These functions are experiencing the experimental stage, especially the unnest_dt. If they don't work on some circumtances, try tidyr package.

Value

data.table, nested or unnested

References

https://www.r-bloggers.com/much-faster-unnesting-with-data-table/

https://stackoverflow.com/questions/25430986/create-nested-data-tables-by-collapsing-rows-into-new-data-tables

See Also

nest, chop

Examples

# examples for nest_dt
# nest by which columns?
 mtcars %>% nest_dt(cyl)
 mtcars %>% nest_dt("cyl")
 mtcars %>% nest_dt(cyl,vs)
 mtcars %>% nest_dt(vs:am)
 mtcars %>% nest_dt("cyl|vs")
 mtcars %>% nest_dt(c("cyl","vs"))

 # change the nested column name
 mtcars %>% nest_dt(cyl,.name = "data")

# nest two columns directly
iris %>% nest_dt(mcols = list(petal="^Pe",sepal="^Se"))

# nest more flexibly
iris %>% nest_dt(mcols = list(ndt1 = 1:3,
  ndt2 = "Pe",
  ndt3 = Sepal.Length:Sepal.Width))

# examples for unnest_dt
# unnest which column?
 mtcars %>% nest_dt("cyl|vs") %>%
   unnest_dt(ndt)
 mtcars %>% nest_dt("cyl|vs") %>%
   unnest_dt("ndt")

df <- data.table(
  a = list(c("a", "b"), "c"),
  b = list(c(TRUE,TRUE),FALSE),
  c = list(3,c(1,2)),
  d = c(11, 22)
)

df
df %>% unnest_dt(a)
df %>% unnest_dt(2)
df %>% unnest_dt("c")
df %>% unnest_dt(cols = names(df)[3])

# You can unnest multiple columns simultaneously
df %>% unnest_dt(1:3)
df %>% unnest_dt(a,b,c)
df %>% unnest_dt("a|b|c")

# examples for squeeze_dt
# nest which columns?
iris %>% squeeze_dt(1:2)
iris %>% squeeze_dt("Se")
iris %>% squeeze_dt(Sepal.Length:Petal.Width)
iris %>% squeeze_dt(1:2,.name = "data")

# examples for chop_dt
df <- data.table(x = c(1, 1, 1, 2, 2, 3), y = 1:6, z = 6:1)
df %>% chop_dt(y,z)
df %>% chop_dt(y,z) %>% unchop_dt(y,z)

Extract the nth value from a vector

Description

nth get the value from a vector with its position, while maxth and minth get the nth highest or lowest value from the vector.

Usage

nth(v, n = 1)

maxth(v, n = 1)

minth(v, n = 1)

Arguments

Value

A single value.

References

https://stackoverflow.com/questions/2453326/fastest-way-to-find-second-third-highest-lowest-value-in-vector-or-column/66367996#66367996

Examples

x = 1:10
nth(x, 1)
nth(x, 5)
nth(x, -2)

y = c(10,3,4,5,2,1,6,9,7,8)
maxth(y,3)
minth(y,3)

Nice printing of report the Space Allocated for an Object

Description

Provides an estimate of the memory that is being used to store an R object. A wrapper of 'object.size', but use a nicer printing unit.

Usage

object_size(object)

Arguments

Value

An object of class "object_size"

Examples

iris %>% object_size()

Count pairs of items within a group

Description

Count the number of times each pair of items appear together within a group. For example, this could count the number of times two words appear within documents. This function has referred to pairwise_count in widyr package, but with very different defaults on several parameters.

Usage

pairwise_count_dt(
  .data,
  .group,
  .value,
  upper = FALSE,
  diag = FALSE,
  sort = TRUE
)

Arguments

Value

A data.table with 3 columns (named as "V1","V2" and "n"), containing combinations in "V1" and "V2", and counts in "n".

See Also

pairwise_count

Examples

dat <- data.table(group = rep(1:5, each = 2),
              letter = c("a", "b",
                         "a", "c",
                         "a", "c",
                         "b", "e",
                         "b", "f"))
pairwise_count_dt(dat,group,letter)
pairwise_count_dt(dat,group,letter,sort = FALSE)
pairwise_count_dt(dat,group,letter,upper = TRUE)
pairwise_count_dt(dat,group,letter,diag = TRUE)
pairwise_count_dt(dat,group,letter,diag = TRUE,upper = TRUE)

# The column name could be specified using character.
pairwise_count_dt(dat,"group","letter")

Add percentage to counts in data.frame

Description

Add percentage for counts in the data.frame, both numeric and character with '

Usage

percent(x, digits = 1)

add_prop(.data, count_name = last(names(.data)), digits = 1)

Arguments

References

https://stackoverflow.com/questions/7145826/how-to-format-a-number-as-percentage-in-r

Examples

 percent(0.9057)
 percent(0.9057,3)

 iris %>%
   count_dt(Species) %>%
   add_prop()

 iris %>%
   count_dt(Species) %>%
   add_prop(count_name = "n",digits = 2)

Load or unload R package(s)

Description

This function is a wrapper for require and detach. pkg_load checks to see if a package is installed, if not it attempts to install the package from CRAN. pkg_unload can detach one or more loaded packages.

Usage

pkg_load(..., pkg_names = NULL)

pkg_unload(..., pkg_names = NULL)

Arguments

See Also

require, detach, p_load, p_unload

Examples

## Not run: 
pkg_load(data.table)
pkg_unload(data.table)

pkg_load(stringr,fst)
pkg_unload(stringr,fst)

pkg_load(pkg_names = c("data.table","fst"))
p_unload(pkg_names = c("data.table","fst"))

pkg_load(data.table,stringr,fst)
pkg_unload("all") # shortcut to unload all loaded packages

## End(Not run)

Set global printing method for data.table

Description

This function allow user to define how data.table is printed.

Usage

print_options(
  topn = 5,
  nrows = 100,
  class = TRUE,
  row.names = TRUE,
  col.names = "auto",
  print.keys = TRUE,
  trunc.cols = FALSE
)

Arguments

Details

Notice that tidyfst has a slightly different printing default for data.table, which is it always prints the keys and variable class (not like data.table).

Value

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

See Also

print.data.table

Examples

iris %>% as.data.table()
print_options(topn = 3,trunc.cols = TRUE)
iris %>% as.data.table()

# set all settings to default in tidyfst
print_options()
iris %>% as.data.table()

Pull out a single variable

Description

Extract vector from data.frame, works likt '[['. Analogous function for pull in dplyr

Usage

pull_dt(.data, col)

Arguments

Value

vector

See Also

pull

Examples

mtcars %>% pull_dt(2)
mtcars %>% pull_dt(cyl)
mtcars %>% pull_dt("cyl")

Recode number or strings

Description

Recode discrete variables, including numerice and character variable.

Usage

rec_num(x, rec, keep = TRUE)

rec_char(x, rec, keep = TRUE)

Arguments

Value

A vector.

See Also

rec

Examples

x = 1:10
x
rec_num(x, rec = "1=10; 4=2")
rec_num(x, rec = "1:3=1; 4:6=2")
rec_num(x, rec = "1:3=1; 4:6=2",keep = FALSE)

y = letters[1:5]
y
rec_char(y,rec = "a=A;b=B")
rec_char(y,rec = "a,b=A;c,d=B")
rec_char(y,rec = "a,b=A;c,d=B",keep = FALSE)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

data.table

%chin%, %like%, %notin%, as.data.table, between, CJ, copy, data.table, fcase, fcoalesce, fintersect, fread, frollapply, fsetdiff, fsetequal, funion, fwrite, key, like, rbindlist, rleid, rleidv, set, setDF, setDT, setnames, tables, uniqueN

stringr

%>%

Change column order

Description

Change the position of columns, using the same syntax as 'select_dt()'. Check similar function as 'relocate' in dplyr.

Usage

relocate_dt(.data, ..., how = "first", where = NULL)

Arguments

Value

A data.table with rearranged columns.

See Also

relocate

Examples

df <- data.table(a = 1, b = 1, c = 1, d = "a", e = "a", f = "a")
df
df %>% relocate_dt(f)
df %>% relocate_dt(a,how = "last")

df %>% relocate_dt(is.character)
df %>% relocate_dt(is.numeric, how = "last")
df %>% relocate_dt("[aeiou]")

df %>% relocate_dt(a, how = "after",where = f)
df %>% relocate_dt(f, how = "before",where = a)
df %>% relocate_dt(f, how = "before",where = c)
df %>% relocate_dt(f, how = "after",where = c)

df2 <- data.table(a = 1, b = "a", c = 1, d = "a")
df2 %>% relocate_dt(is.numeric,
                    how = "after",
                    where = is.character)
df2 %>% relocate_dt(is.numeric,
                    how="before",
                    where = is.character)

Rename column in data.frame

Description

Rename one or more columns in the data.frame.

Usage

rename_dt(.data, ...)

rename_with_dt(.data, .fn, ...)

Arguments

Value

data.table

See Also

rename

Examples

iris %>%
  rename_dt(sl = Sepal.Length,sw = Sepal.Width) %>%
  head()
iris %>% rename_with_dt(toupper)
iris %>% rename_with_dt(toupper,"^Pe")

Fast value replacement in data frame

Description

While replace_na_dt could replace all NAs to another value, replace_dt could replace any value(s) to another specific value.

Usage

replace_dt(.data, ..., from = is.nan, to = NA)

Arguments

Value

A data.table.

See Also

replace_na_dt

Examples

iris %>% mutate_vars(is.factor,as.character) -> new_iris

new_iris %>%
  replace_dt(Species, from = "setosa",to = "SS")
new_iris %>%
  replace_dt(Species,from = c("setosa","virginica"),to = "sv")
new_iris %>%
  replace_dt(Petal.Width, from = .2,to = 2)
new_iris %>%
  replace_dt(from = .2,to = NA)
new_iris %>%
  replace_dt(is.numeric, from = function(x) x > 3, to = 9999 )

Tools for working with row names

Description

The enhanced data.frame, including tibble and data.table, do not support row names. To link to some base r facilities, there should be functions to save information in row names. These functions are analogous to rownames_to_column and column_to_rownames in tibble.

Usage

rn_col(.data, var = "rowname")

col_rn(.data, var = "rowname")

Arguments

Value

rn_col returns a data.table, col_rn returns a data frame.

Examples

 mtcars %>% rn_col()
 mtcars %>% rn_col("rn")

 mtcars %>% rn_col() -> new_mtcars

 new_mtcars %>% col_rn() -> old_mtcars
 old_mtcars
 setequal(mtcars,old_mtcars)

Round a number and make it show zeros

Description

Rounds values in its first argument to the specified number of decimal places, returning character, ensuring first decimal digits are showed even when they are zeros.

Usage

round0(x, digits = 0)

Arguments

Value

A character vector.

References

https://stackoverflow.com/questions/42105336/how-to-round-a-number-and-make-it-show-zeros

See Also

round

Examples

a = 14.0034
round0(a,2)

b = 10
round0(b,1)

Sample rows randomly from a table

Description

Select a number or proportion of rows randomly from the data frame

sample_dt is a merged version of sample_n_dt and sample_frac_dt, this could be convenient.

Usage

sample_dt(.data, n = NULL, prop = NULL, replace = FALSE, by = NULL)

sample_n_dt(.data, size, replace = FALSE, by = NULL)

sample_frac_dt(.data, size, replace = FALSE, by = NULL)

Arguments

Value

data.table

See Also

sample_n,sample_frac

Examples

sample_n_dt(mtcars, 10)
sample_n_dt(mtcars, 50, replace = TRUE)
sample_frac_dt(mtcars, 0.1)
sample_frac_dt(mtcars, 1.5, replace = TRUE)


sample_dt(mtcars,n=10)
sample_dt(mtcars,prop = 0.1)


# sample by group(s)
iris %>% sample_n_dt(2,by = "Species")
iris %>% sample_frac_dt(.1,by = "Species")

mtcars %>% sample_n_dt(1,by = c("cyl","vs"))

Select column from data.frame

Description

Select specific column(s) via various ways. One can select columns by their column names, indexes or regular expression recognizing the column name(s).

Usage

select_dt(.data, ..., cols = NULL, negate = FALSE)

select_mix(.data, ..., rm.dup = TRUE)

Arguments

Value

data.table

See Also

select, select_if

Examples

iris %>% select_dt(Species)
iris %>% select_dt(Sepal.Length,Sepal.Width)
iris %>% select_dt(Sepal.Length:Petal.Length)
iris %>% select_dt(-Sepal.Length)
iris %>% select_dt(-Sepal.Length,-Petal.Length)
iris %>% select_dt(-(Sepal.Length:Petal.Length))
iris %>% select_dt(c("Sepal.Length","Sepal.Width"))
iris %>% select_dt(-c("Sepal.Length","Sepal.Width"))
iris %>% select_dt(1)
iris %>% select_dt(-1)
iris %>% select_dt(1:3)
iris %>% select_dt(-(1:3))
iris %>% select_dt(1,3)
iris %>% select_dt("Pe")
iris %>% select_dt(-"Se")
iris %>% select_dt(!"Se")
iris %>% select_dt("Pe",negate = TRUE)
iris %>% select_dt("Pe|Sp")
iris %>% select_dt(cols = 2:3)
iris %>% select_dt(cols = 2:3,negate = TRUE)
iris %>% select_dt(cols = c("Sepal.Length","Sepal.Width"))
iris %>% select_dt(cols = names(iris)[2:3])

iris %>% select_dt(is.factor)
iris %>% select_dt(-is.factor)
iris %>% select_dt(!is.factor)

# select_mix could provide flexible mix selection
select_mix(iris, Species,"Sepal.Length")
select_mix(iris,1:2,is.factor)

select_mix(iris,Sepal.Length,is.numeric)
# set rm.dup to FALSE could save the duplicated column names
select_mix(iris,Sepal.Length,is.numeric,rm.dup = FALSE)

Separate a character column into two columns using a regular expression separator

Description

Given either regular expression, separate_dt() turns a single character column into two columns.

Usage

separate_dt(
  .data,
  separated_colname,
  into,
  sep = "[^[:alnum:]]+",
  remove = TRUE
)

Arguments

See Also

separate, unite_dt

Examples

df <- data.frame(x = c(NA, "a.b", "a.d", "b.c"))
df %>% separate_dt(x, c("A", "B"))
# equals to
df %>% separate_dt("x", c("A", "B"))

# If you just want the second variable:
df %>% separate_dt(x,into = c(NA,"B"))

Subset rows using their positions

Description

'slice_dt()' lets you index rows by their (integer) locations. It allows you to select, remove, and duplicate rows. It is accompanied by a number of helpers for common use cases:

* 'slice_head_dt()' and 'slice_tail_dt()' select the first or last rows. * 'slice_sample_dt()' randomly selects rows. * 'slice_min_dt()' and 'slice_max_dt()' select rows with highest or lowest values of a variable.

Usage

slice_dt(.data, ..., by = NULL)

slice_head_dt(.data, n, by = NULL)

slice_tail_dt(.data, n, by = NULL)

slice_max_dt(.data, order_by, n, by = NULL, with_ties = TRUE)

slice_min_dt(.data, order_by, n, by = NULL, with_ties = TRUE)

slice_sample_dt(.data, n, replace = FALSE, by = NULL)

Arguments

Value

A data.table

See Also

slice

Examples

a = iris
slice_dt(a,1,2)
slice_dt(a,2:3)
slice_dt(a,141:.N)
slice_dt(a,1,.N)
slice_head_dt(a,5)
slice_head_dt(a,0.1)
slice_tail_dt(a,5)
slice_tail_dt(a,0.1)
slice_max_dt(a,Sepal.Length,10)
slice_max_dt(a,Sepal.Length,10,with_ties = FALSE)
slice_min_dt(a,Sepal.Length,10)
slice_min_dt(a,Sepal.Length,10,with_ties = FALSE)
slice_sample_dt(a,10)
slice_sample_dt(a,0.1)


# use by to slice by group

## following codes get the same results
slice_dt(a,1:3,by = "Species")
slice_dt(a,1:3,by = Species)
slice_dt(a,1:3,by = .(Species))

slice_head_dt(a,2,by = Species)
slice_tail_dt(a,2,by = Species)

slice_max_dt(a,Sepal.Length,3,by = Species)
slice_max_dt(a,Sepal.Length,3,by = Species,with_ties = FALSE)
slice_min_dt(a,Sepal.Length,3,by = Species)
slice_min_dt(a,Sepal.Length,3,by = Species,with_ties = FALSE)

# in `slice_sample_dt`, "by" could only take character class
slice_sample_dt(a,.1,by = "Species")
slice_sample_dt(a,3,by = "Species")
slice_sample_dt(a,51,replace = TRUE,by = "Species")

Case insensitive table joining like SQL

Description

Work like the '*_join_dt' series functions, joining tables with common or customized keys in various ways. The only difference is the joining is case insensitive like SQL.

Usage

sql_join_dt(x, y, by = NULL, type = "inner", suffix = c(".x", ".y"))

Arguments

Value

A data.table

See Also

join

Examples

dt1 = data.table(x = c("A","b"),y = 1:2)
dt2 = data.table(x = c("a","B"),z = 4:5)
sql_join_dt(dt1,dt2)

Summarise columns to single values

Description

Summarise group of values into one value for each group. If there is only one group, then only one value would be returned. The summarise function should always return a single value.

Usage

summarise_dt(.data, ..., by = NULL)

summarize_dt(.data, ..., by = NULL)

summarise_when(.data, when, ..., by = NULL)

summarize_when(.data, when, ..., by = NULL)

summarise_vars(.data, .cols = NULL, .func, ..., by)

summarize_vars(.data, .cols = NULL, .func, ..., by)

Arguments

Details

summarise_vars could complete summarise on specific columns.

Value

data.table

See Also

summarise

Examples

iris %>% summarise_dt(avg = mean(Sepal.Length))
iris %>% summarise_dt(avg = mean(Sepal.Length),by = Species)
mtcars %>% summarise_dt(avg = mean(hp),by = .(cyl,vs))

# the data.table way
mtcars %>% summarise_dt(cyl_n = .N, by = .(cyl, vs)) # `.` is short for list

iris %>% summarise_vars(is.numeric,min)
iris %>% summarise_vars(-is.factor,min)
iris %>% summarise_vars(1:4,min)

iris %>% summarise_vars(is.numeric,min,by ="Species")
mtcars %>% summarise_vars(is.numeric,mean,by = c("vs", "am"))

# use multiple functions on multiple columns
iris %>%
  summarise_vars(is.numeric,.func = list(mean,sd,median))
iris %>%
  summarise_vars(is.numeric,.func = list(mean,sd,median),by = Species)

Convenient print of time taken

Description

Convenient printing of time elapsed. A wrapper of data.table::timetaken, but showing the results more directly.

Usage

sys_time_print(expr)

pst(expr)

Arguments

Value

A character vector of the form HH:MM:SS, or SS.MMMsec if under 60 seconds (invisibly for show_time). See examples.

See Also

timetaken, system.time

Examples

sys_time_print(Sys.sleep(1))

a = iris
sys_time_print({
  res = iris %>%
    mutate_dt(one = 1)
})
res

Efficient transpose of data.frame

Description

An efficient way to transpose data frames(data.frame/data.table/tibble).

Usage

t_dt(.data)

Arguments

Details

This function would return the original data.frame structure, keeping all the row names and column names. If the row names are not available or, "V1,V2..." will be provided.

Value

A transposed data.frame

Examples

t_dt(iris)
t_dt(mtcars)

"Uncount" a data frame

Description

Duplicating rows according to a weighting variable. This is the opposite operation of 'count_dt'. Analogous to 'tidyr::uncount'.

Usage

uncount_dt(.data, wt, .remove = TRUE)

Arguments

See Also

count, uncount

Examples

df <- data.table(x = c("a", "b"), n = c(1, 2))
uncount_dt(df, n)
uncount_dt(df,n,FALSE)

Unite multiple columns into one by pasting strings together

Description

Convenience function to paste together multiple columns into one.

Usage

unite_dt(
  .data,
  united_colname,
  ...,
  sep = "_",
  remove = FALSE,
  na2char = FALSE
)

Arguments

See Also

unite,separate_dt

Examples

df <- expand.grid(x = c("a", NA), y = c("b", NA))
df

# Treat missing value as NA, default
df %>% unite_dt("z", x:y, remove = FALSE)
# Treat missing value as character "NA"
df %>% unite_dt("z", x:y, na2char = TRUE, remove = FALSE)
df %>%
  unite_dt("xy", x:y)

# Select all columns
iris %>% unite_dt("merged_name",".")

Use UTF-8 for character encoding in a data frame

Description

fread from data.table could not recognize the encoding and return the correct form, this could be unconvenient for text mining tasks. The utf8-encoding could use "UTF-8" as the encoding to override the current encoding of characters in a data frame.

Usage

utf8_encoding(.data)

Arguments

Value

A data.table with characters in UTF-8 encoding

Pivot data from long to wide

Description

Transform a data frame from long format to wide by increasing the number of columns and decreasing the number of rows.

Usage

wider_dt(.data, ..., name, value = NULL, fun = NULL, fill = NA)

Arguments

Details

The parameter of 'name' and 'value' should always be provided and should be explicit called (with the parameter names attached).

Value

data.table

See Also

longer_dt, dcast, pivot_wider

Examples

 stocks = data.frame(
   time = as.Date('2009-01-01') + 0:9,
   X = rnorm(10, 0, 1),
   Y = rnorm(10, 0, 2),
   Z = rnorm(10, 0, 4)
 ) %>%
   longer_dt(time) -> longer_stocks

 longer_stocks

 longer_stocks %>%
   wider_dt("time",
            name = "name",
            value = "value")

 longer_stocks %>%
   mutate_dt(one = 1) %>%
   wider_dt("time",
            name = "name",
            value = "one")

## using "fun" parameter for aggregation
DT <- data.table(v1 = rep(1:2, each = 6),
                 v2 = rep(rep(1:3, 2), each = 2),
                 v3 = rep(1:2, 6),
                 v4 = rnorm(6))
## for each combination of (v1, v2), add up all values of v4
DT %>%
  wider_dt(v1,v2,
           value = "v4",
           name = ".",
           fun = sum)