Package 'splitstackshape' reference manual

Title:	Stack and Reshape Datasets After Splitting Concatenated Values
Description:	Online data collection tools like Google Forms often export multiple-response questions with data concatenated in cells. The concat.split (cSplit) family of functions splits such data into separate cells. The package also includes functions to stack groups of columns and to reshape wide data, even when the data are "unbalanced"---something which reshape (from base R) does not handle, and which melt and dcast from reshape2 do not easily handle.
Authors:	Ananda Mahto
Maintainer:	Ananda Mahto <[email protected]>
License:	GPL-3
Version:	1.4.8
Built:	2025-02-19 02:57:49 UTC
Source:	https://github.com/mrdwab/splitstackshape

splitstackshape

Description

Stack and Reshape Datasets After Splitting Concatenated Values

Details

Package:	splitstackshape
Type:	Package
Version:	1.4.8
Date:	2019-04-21
License:	GPL-3

Online data collection tools like Google Forms often export multiple-response questions with data concatenated in cells. The concat.split() family of functions splits such data into separate cells. The package also includes functions to stack groups of columns and to reshape wide data, even when the data are "unbalanced"—something which stats::reshape() does not handle, and which reshape2::melt() and reshape2::dcast() from reshape2 do not easily handle.

Author(s)

Ananda Mahto

Maintainer: Ananda Mahto [email protected]

Examples


## concat.split
head(cSplit(concat.test, "Likes", drop = TRUE))

## Reshape
set.seed(1)
mydf <- data.frame(id_1 = 1:6, id_2 = c("A", "B"), varA.1 = sample(letters, 6),
                   varA.2 = sample(letters, 6), varA.3 = sample(letters, 6),
                   varB.2 = sample(10, 6), varB.3 = sample(10, 6), 
                   varC.3 = rnorm(6))
mydf
Reshape(mydf, id.vars = c("id_1", "id_2"),
        var.stubs = c("varA", "varB", "varC"))

## Stacked
Stacked(data = mydf, id.vars = c("id_1", "id_2"),
        var.stubs = c("varA", "varB", "varC"),
        sep = ".")
## Not run: 
## Processing times
set.seed(1)
Nrow <- 1000000
Ncol <- 10
mybigdf <- cbind(id = 1:Nrow, as.data.frame(matrix(rnorm(Nrow*Ncol),
                                                   nrow=Nrow)))
head(mybigdf)
dim(mybigdf)
tail(mybigdf)
A <- names(mybigdf)
names(mybigdf) <- c("id", paste("varA", 1:3, sep = "_"),
                    paste("varB", 1:4, sep = "_"),
                    paste("varC", 1:3, sep = "_"))
system.time({
   O1 <- Reshape(mybigdf, id.vars = "id",
   var.stubs = c("varA", "varB", "varC"), sep = "_")
   O1 <- O1[order(O1$id, O1$time), ]
})
system.time({
   O2 <- merged.stack(mybigdf, id.vars="id",
   var.stubs=c("varA", "varB", "varC"), sep = "_")
})
system.time({
   O3 <- Stacked(mybigdf, id.vars="id",
   var.stubs=c("varA", "varB", "varC"), sep = "_")
})
DT <- data.table(mybigdf)
system.time({
   O4 <- merged.stack(DT, id.vars="id",
   var.stubs=c("varA", "varB", "varC"), sep = "_")
})

## End(Not run)

## concat.split
head(cSplit(concat.test, "Likes", drop = TRUE))

## Reshape
set.seed(1)
mydf <- data.frame(id_1 = 1:6, id_2 = c("A", "B"), varA.1 = sample(letters, 6),
                   varA.2 = sample(letters, 6), varA.3 = sample(letters, 6),
                   varB.2 = sample(10, 6), varB.3 = sample(10, 6), 
                   varC.3 = rnorm(6))
mydf
Reshape(mydf, id.vars = c("id_1", "id_2"),
        var.stubs = c("varA", "varB", "varC"))

## Stacked
Stacked(data = mydf, id.vars = c("id_1", "id_2"),
        var.stubs = c("varA", "varB", "varC"),
        sep = ".")
## Not run: 
## Processing times
set.seed(1)
Nrow <- 1000000
Ncol <- 10
mybigdf <- cbind(id = 1:Nrow, as.data.frame(matrix(rnorm(Nrow*Ncol),
                                                   nrow=Nrow)))
head(mybigdf)
dim(mybigdf)
tail(mybigdf)
A <- names(mybigdf)
names(mybigdf) <- c("id", paste("varA", 1:3, sep = "_"),
                    paste("varB", 1:4, sep = "_"),
                    paste("varC", 1:3, sep = "_"))
system.time({
   O1 <- Reshape(mybigdf, id.vars = "id",
   var.stubs = c("varA", "varB", "varC"), sep = "_")
   O1 <- O1[order(O1$id, O1$time), ]
})
system.time({
   O2 <- merged.stack(mybigdf, id.vars="id",
   var.stubs=c("varA", "varB", "varC"), sep = "_")
})
system.time({
   O3 <- Stacked(mybigdf, id.vars="id",
   var.stubs=c("varA", "varB", "varC"), sep = "_")
})
DT <- data.table(mybigdf)
system.time({
   O4 <- merged.stack(DT, id.vars="id",
   var.stubs=c("varA", "varB", "varC"), sep = "_")
})

## End(Not run)

Create a Binary Matrix from a List of Character Values

Description

Create a binary matrix from a list of character values

Usage

charMat(listOfValues, fill = NA, mode = "binary")
charMat(listOfValues, fill = NA, mode = "binary")

Arguments

`listOfValues`	A `list` of input values to be inserted in a matrix.
`fill`	The initializing fill value for the empty matrix.
`mode`	Either `"binary"` or `"value"`. Defaults to `"binary"`.

Details

This is primarily a helper function for the concat.split() function when creating the "expanded" structure. The input is anticipated to be a list of values obtained using base::strsplit().

Value

A matrix.

Author(s)

Ananda Mahto

Examples


invec <- c("rock,electro","electro","rock,jazz")
A <- strsplit(invec, ",")
splitstackshape:::charMat(A)
splitstackshape:::charMat(A, 0)
splitstackshape:::charMat(A, mode = "value")

invec <- c("rock,electro","electro","rock,jazz")
A <- strsplit(invec, ",")
splitstackshape:::charMat(A)
splitstackshape:::charMat(A, 0)
splitstackshape:::charMat(A, mode = "value")

Split Concatenated Cells in a Dataset

Description

The concat.split function takes a column with multiple values, splits the values into a list or into separate columns, and returns a new data.frame or data.table.

Usage

concat.split(data, split.col, sep = ",", structure = "compact",
  mode = NULL, type = NULL, drop = FALSE, fixed = FALSE,
  fill = NA, ...)
concat.split(data, split.col, sep = ",", structure = "compact",
  mode = NULL, type = NULL, drop = FALSE, fixed = FALSE,
  fill = NA, ...)

Arguments

`data`	The source `data.frame` or `data.table`.
`split.col`	The variable that needs to be split; can be specified either by the column number or the variable name.
`sep`	The character separating each value (defaults to `","`).
`structure`	Can be either `"compact"`, `"expanded"`, or `list`. Defaults to `"compact"`. See Details.
`mode`	Can be either `"binary"` or `"value"` (where `"binary"` is default and it recodes values to 1 or `NA`, like Boolean data, but without assuming 0 when data is not available). This setting only applies when `structure = "expanded"`; a warning message will be issued if used with other structures.
`type`	Can be either `"numeric"` or `"character"` (where `"numeric"` is default). This setting only applies when `structure = "expanded"`; a warning message will be issued if used with other structures.
`drop`	Logical (whether to remove the original variable from the output or not). Defaults to `FALSE`.
`fixed`	Is the input for the `sep` value fixed, or a regular expression? See Details.
`fill`	The "fill" value for missing values when `structure = "expanded"`. Defaults to `NA`.
`...`	Additional arguments to `cSplit()`.

Details

structure

"compact" creates as many columns as the maximum length of the resulting split. This is the most useful general-case application of this function.
When the input is numeric, "expanded" creates as many columns as the maximum value of the input data. This is most useful when converting to mode = "binary".
"list" creates a single new column that is structurally a list within a data.frame or data.table.

fixed

When structure = "expanded" or structure = "list", it is possible to supply a a regular expression containing the characters to split on. For example, to split on ",", ";", or "|", you can set sep = ",|;|\|" or sep = "[,;|]", and fixed = FALSE to split on any of those characters.

Note

This is more of a "legacy" or "convenience" wrapper function encompassing the features available in the separated functions of cSplit(), cSplit_l(), and cSplit_e().

Author(s)

Ananda Mahto

Examples


## Load some data
temp <- head(concat.test)

# Split up the second column, selecting by column number
concat.split(temp, 2)

# ... or by name, and drop the offensive first column
concat.split(temp, "Likes", drop = TRUE)

# The "Hates" column uses a different separator
concat.split(temp, "Hates", sep = ";", drop = TRUE)

## Not run: 
# You'll get a warning here, when trying to retain the original values
concat.split(temp, 2, mode = "value", drop = TRUE)

## End(Not run)

# Try again. Notice the differing number of resulting columns
concat.split(temp, 2, structure = "expanded",
mode = "value", type = "numeric", drop = TRUE)

# Let's try splitting some strings... Same syntax
concat.split(temp, 3, drop = TRUE)

# Strings can also be split to binary representations
concat.split(temp, 3, structure = "expanded",
type = "character", fill = 0, drop = TRUE)

# Split up the "Likes column" into a list variable; retain original column
head(concat.split(concat.test, 2, structure = "list", drop = FALSE))

# View the structure of the output to verify
# that the new column is a list; note the
# difference between "Likes" and "Likes_list".
str(concat.split(temp, 2, structure = "list", drop = FALSE))

## Load some data
temp <- head(concat.test)

# Split up the second column, selecting by column number
concat.split(temp, 2)

# ... or by name, and drop the offensive first column
concat.split(temp, "Likes", drop = TRUE)

# The "Hates" column uses a different separator
concat.split(temp, "Hates", sep = ";", drop = TRUE)

## Not run: 
# You'll get a warning here, when trying to retain the original values
concat.split(temp, 2, mode = "value", drop = TRUE)

## End(Not run)

# Try again. Notice the differing number of resulting columns
concat.split(temp, 2, structure = "expanded",
mode = "value", type = "numeric", drop = TRUE)

# Let's try splitting some strings... Same syntax
concat.split(temp, 3, drop = TRUE)

# Strings can also be split to binary representations
concat.split(temp, 3, structure = "expanded",
type = "character", fill = 0, drop = TRUE)

# Split up the "Likes column" into a list variable; retain original column
head(concat.split(concat.test, 2, structure = "list", drop = FALSE))

# View the structure of the output to verify
# that the new column is a list; note the
# difference between "Likes" and "Likes_list".
str(concat.split(temp, 2, structure = "list", drop = FALSE))

Split Concatenated Cells into a Condensed Format

Description

The default splitting method for concat.split. Formerly based on read.concat() but presently a simple wrapper around cSplit().

Usage

concat.split.compact(data, split.col, sep = ",", drop = FALSE,
  fixed = TRUE, ...)
concat.split.compact(data, split.col, sep = ",", drop = FALSE,
  fixed = TRUE, ...)

Arguments

`data`	The input `data.frame` or `data.table`.
`split.col`	The column that need to be split.
`sep`	The character separating each value.
`drop`	Logical. Should the original variable be dropped? Defaults to `FALSE`.
`fixed`	Logical. Should the split character be treated as a fixed pattern (`TRUE`) or a regular expression (`FALSE`)? Defaults to `TRUE`.
`...`	optional arguments to pass to `cSplit`.

Value

A data.table.

Note

THIS FUNCTION IS DEPRECATED AND WILL BE REMOVED FROM LATER VERSIONS OF "SPLITSTACKSHAPE". It no longer does anything different from cSplit(). It is recommended that you transition your code to the cSplit function instead.

Author(s)

Ananda Mahto

Examples


## Not run: 
temp <- head(concat.test)
concat.split.compact(temp, "Likes")
concat.split.compact(temp, 4, ";")

## Extra arguments to cSplit
concat.split.compact(temp, "Siblings", drop = TRUE, stripWhite = TRUE)

## End(Not run)

## Not run: 
temp <- head(concat.test)
concat.split.compact(temp, "Likes")
concat.split.compact(temp, 4, ";")

## Extra arguments to cSplit
concat.split.compact(temp, "Siblings", drop = TRUE, stripWhite = TRUE)

## End(Not run)

Split Concatenated Values into their Corresponding Column Position

Description

"Expand" concatenated numeric or character values to their relevant position in a data.frame or data.table or create a binary representation of such data.

Usage

cSplit_e(data, split.col, sep = ",", mode = NULL, type = "numeric",
  drop = FALSE, fixed = TRUE, fill = NA)
cSplit_e(data, split.col, sep = ",", mode = NULL, type = "numeric",
  drop = FALSE, fixed = TRUE, fill = NA)

Arguments

`data`	The source `data.frame` or `data.table`.
`split.col`	The variable that needs to be split (either name or index position).
`sep`	The character separating each value. Can also be a regular expression.
`mode`	Can be either `"binary"` (where presence of a number in a given column is converted to "1") or `"value"` (where the value is retained and not recoded to "1"). Defaults to `"binary"`.
`type`	Can be either `"numeric"` (where the items being split are integers) or `"character"` (where the items being split are character strings). Defaults to `"numeric"`.
`drop`	Logical. Should the original variable be dropped? Defaults to `FALSE`.
`fixed`	Used for `base::strsplit()` for allowing regular expressions to be used.
`fill`	Desired "fill" value. Defaults to `NA`.

Value

A data.frame or data.table depending on the source input.

Author(s)

Ananda Mahto

Examples


temp <- head(concat.test)
cSplit_e(temp, "Likes")
cSplit_e(temp, 4, ";", fill = 0)
 
## The old function name still works
concat.split.expanded(temp, "Likes")
concat.split.expanded(temp, 4, ";", fill = 0)
concat.split.expanded(temp, 4, ";", mode = "value", drop = TRUE)
concat.split.expanded(temp, "Siblings", type = "character", drop = TRUE)

temp <- head(concat.test)
cSplit_e(temp, "Likes")
cSplit_e(temp, 4, ";", fill = 0)
 
## The old function name still works
concat.split.expanded(temp, "Likes")
concat.split.expanded(temp, 4, ";", fill = 0)
concat.split.expanded(temp, 4, ";", mode = "value", drop = TRUE)
concat.split.expanded(temp, "Siblings", type = "character", drop = TRUE)

Split Concatenated Cells into a List Format

Description

Takes a column in a data.frame or data.table with multiple values, splits the values into a list, and returns a new data.frame or data.table.

Usage

cSplit_l(data, split.col, sep = ",", drop = FALSE, fixed = FALSE)
cSplit_l(data, split.col, sep = ",", drop = FALSE, fixed = FALSE)

Arguments

`data`	The source `data.frame` or `data.table`.
`split.col`	The variable that needs to be split (either name or index position).
`sep`	The character separating each value. Can also be a regular expression.
`drop`	Logical. Should the original variable be dropped? Defaults to `FALSE`.
`fixed`	Used for `base::strsplit()` for allowing regular expressions to be used.

Value

A data.frame or data.table with the concatenated column split and added as a list.

Author(s)

Ananda Mahto

Examples


temp <- head(concat.test)
str(cSplit_l(temp, "Likes"))
cSplit_l(temp, 4, ";")

## The old function name still works
str(concat.split.list(temp, "Likes"))
concat.split.list(temp, 4, ";")
concat.split.list(temp, 4, ";", drop = TRUE)

temp <- head(concat.test)
str(cSplit_l(temp, "Likes"))
cSplit_l(temp, 4, ";")

## The old function name still works
str(concat.split.list(temp, "Likes"))
concat.split.list(temp, 4, ";")
concat.split.list(temp, 4, ";", drop = TRUE)

Split Concatenated Cells and Optionally Reshape the Output

Description

This is a wrapper for the cSplit() function to maintain backwards compatibility with earlier versions of the "splitstackshape" package. It allows the user to split multiple columns at once and optionally convert the results into a "long" format.

Usage

concat.split.multiple(data, split.cols, seps = ",", direction = "wide",
  ...)
concat.split.multiple(data, split.cols, seps = ",", direction = "wide",
  ...)

Arguments

`data`	The source `data.frame` or `data.table`.
`split.cols`	A vector of columns that need to be split.
`seps`	A vector of the separator character used in each column. If all columns use the same character, you can enter that single character.
`direction`	The desired form of the resulting `data.frame` or `data.table`, either `"wide"` or `"long"`. Defaults to `"wide"`.
`...`	Other arguments to `cSplit()`.

Value

A data.table.

Author(s)

Ananda Mahto

Examples


## Not run: 
temp <- head(concat.test)
concat.split.multiple(temp, split.cols = c("Likes", "Hates", "Siblings"),
                      seps = c(",", ";", ","))
concat.split.multiple(temp, split.cols = c("Likes", "Siblings"),
                      seps = ",", direction = "long")

## End(Not run)

## Not run: 
temp <- head(concat.test)
concat.split.multiple(temp, split.cols = c("Likes", "Hates", "Siblings"),
                      seps = c(",", ";", ","))
concat.split.multiple(temp, split.cols = c("Likes", "Siblings"),
                      seps = ",", direction = "long")

## End(Not run)

Example Dataset with Concatenated Cells

Description

This is a sample dataset to demonstrate the different features of the concat.split() family of functions.

Format

A data.frame in which many columns contain concatenated cells.

Split Concatenated Values into Separate Values

Description

The cSplit function is designed to quickly and conveniently split concatenated data into separate values.

Usage

cSplit(indt, splitCols, sep = ",", direction = "wide", fixed = TRUE,
  drop = TRUE, stripWhite = TRUE, makeEqual = NULL,
  type.convert = TRUE)
cSplit(indt, splitCols, sep = ",", direction = "wide", fixed = TRUE,
  drop = TRUE, stripWhite = TRUE, makeEqual = NULL,
  type.convert = TRUE)

Arguments

`indt`	The input `data.frame` or `data.table`.
`splitCols`	The column or columns that need to be split.
`sep`	The values that serve as a delimiter within each column. This can be a single value if all columns have the same delimiter, or a vector of values in the same order as the delimiters in each of the `splitCols`.
`direction`	The desired direction of the results, either `"wide"` or `"long"`.
`fixed`	Logical. Should the split character be treated as a fixed pattern (`TRUE`) or a regular expression (`FALSE`)? Defaults to `TRUE`.
`drop`	Logical. Should the original concatenated column be dropped? Defaults to `TRUE`.
`stripWhite`	Logical. If there is whitespace around the delimiter in the concatenated columns, should it be stripped prior to splitting? Defaults to `TRUE`.
`makeEqual`	Logical. Should all groups be made to be the same length? Defaults to `FALSE`.
`type.convert`	Logical. Should `utils::type.convert()` be used to convert the result of each column? This would add a little to the execution time.

Value

A data.table with the values split into new columns or rows.

Note

The cSplit function replaces most of the earlier concat.split* functions. The earlier functions remain for compatibility purposes, but now they are essentially wrappers for the cSplit function.

Author(s)

Ananda Mahto

Examples


## Sample data
temp <- head(concat.test)

## Split the "Likes" column
cSplit(temp, "Likes")

## Split the "Likes" and "Hates" columns --
##   they have different delimiters...
cSplit(temp, c("Likes", "Hates"), c(",", ";"))

## Split "Siblings" into a long form...
cSplit(temp, "Siblings", ",", direction = "long")

## Split "Siblings" into a long form, not removing whitespace
cSplit(temp, "Siblings", ",", direction = "long", stripWhite = FALSE)

## Split a vector
y <- c("a_b_c", "a_b", "c_a_b")
cSplit(data.frame(y), "y", "_")

## Sample data
temp <- head(concat.test)

## Split the "Likes" column
cSplit(temp, "Likes")

## Split the "Likes" and "Hates" columns --
##   they have different delimiters...
cSplit(temp, c("Likes", "Hates"), c(",", ";"))

## Split "Siblings" into a long form...
cSplit(temp, "Siblings", ",", direction = "long")

## Split "Siblings" into a long form, not removing whitespace
cSplit(temp, "Siblings", ",", direction = "long", stripWhite = FALSE)

## Split a vector
y <- c("a_b_c", "a_b", "c_a_b")
cSplit(data.frame(y), "y", "_")

Expand the Rows of a Dataset

Description

Expands (replicates) the rows of a data.frame or data.table, either by a fixed number, a specified vector, or a value contained in one of the columns in the source data.frame or data.table.

Usage

expandRows(dataset, count, count.is.col = TRUE, drop = TRUE)
expandRows(dataset, count, count.is.col = TRUE, drop = TRUE)

Arguments

`dataset`	The input `data.frame` or `data.table`.
`count`	The numeric vector of counts OR the column from the dataset that contains the count data. If `count` is a single digit, it is assumed that all rows should be repeated by this amount.
`count.is.col`	Logical. Is the `count` value a column from the input dataset? Defaults to `TRUE`.
`drop`	Logical. If `count.is.col = TRUE`, should the "count" column be dropped from the result? Defaults to `TRUE`.

Value

A data.frame or data.table, depending on the input.

Author(s)

Ananda Mahto

References

http://stackoverflow.com/a/19519828/1270695

Examples


mydf <- data.frame(x = c("a", "b", "q"), 
                   y = c("c", "d", "r"), 
                   count = c(2, 5, 3))
library(data.table)
DT <- as.data.table(mydf)
mydf
expandRows(mydf, "count")
expandRows(DT, "count", drop = FALSE)
expandRows(mydf, count = 3) ## This takes values from the third column!
expandRows(mydf, count = 3, count.is.col = FALSE)
expandRows(mydf, count = c(1, 5, 9), count.is.col = FALSE)
expandRows(DT, count = c(1, 5, 9), count.is.col = FALSE)

mydf <- data.frame(x = c("a", "b", "q"), 
                   y = c("c", "d", "r"), 
                   count = c(2, 5, 3))
library(data.table)
DT <- as.data.table(mydf)
mydf
expandRows(mydf, "count")
expandRows(DT, "count", drop = FALSE)
expandRows(mydf, count = 3) ## This takes values from the third column!
expandRows(mydf, count = 3, count.is.col = FALSE)
expandRows(mydf, count = c(1, 5, 9), count.is.col = FALSE)
expandRows(DT, count = c(1, 5, 9), count.is.col = FALSE)

Convert All Factor Columns to Character Columns

Description

Sometimes, we forget to use the stringsAsFactors argument when using utils::read.table() and related functions. By default, R converts character columns to factors. Instead of re-reading the data, the FacsToChars function will identify which columns are currently factors, and convert them all to characters.

Usage

FacsToChars(mydf)
FacsToChars(mydf)

Arguments

mydf

The name of your data.frame

Author(s)

Ananda Mahto

Examples


## Some example data
dat <- data.frame(title = c("title1", "title2", "title3"),
         author = c("author1", "author2", "author3"),
         customerID = c(1, 2, 1))

str(dat) # current structure
dat2 <- splitstackshape:::FacsToChars(dat)
str(dat2) # Your new object
str(dat)  # Original object is unaffected

## Some example data
dat <- data.frame(title = c("title1", "title2", "title3"),
         author = c("author1", "author2", "author3"),
         customerID = c(1, 2, 1))

str(dat) # current structure
dat2 <- splitstackshape:::FacsToChars(dat)
str(dat2) # Your new object
str(dat)  # Original object is unaffected

Add an "id" Variable to a Dataset

Description

Many functions will not work properly if there are duplicated ID variables in a dataset. This function is a convenience function for .N from the "data.table" package to create an .id variable that when used in conjunction with the existing ID variables, should be unique.

Usage

getanID(data, id.vars = NULL)
getanID(data, id.vars = NULL)

Arguments

`data`	The input `data.frame` or `data.table`.
`id.vars`	The variables that should be treated as ID variables. Defaults to `NULL`, at which point all variables are used to create the new ID variable.

Value

The input dataset (as a data.table) if ID variables are unique, or the input dataset with a new column named .id.

Author(s)

Ananda Mahto

Examples


mydf <- data.frame(IDA = c("a", "a", "a", "b", "b"),
                   IDB = c(1, 1, 1, 1, 1), values = 1:5)
mydf
getanID(mydf, c("IDA", "IDB"))

mydf <- data.frame(IDA = c("a", "a", "a", "b", "b"),
                   IDB = c(1, 2, 1, 1, 2), values = 1:5)
mydf
getanID(mydf, 1:2)



mydf <- data.frame(IDA = c("a", "a", "a", "b", "b"),
                   IDB = c(1, 1, 1, 1, 1), values = 1:5)
mydf
getanID(mydf, c("IDA", "IDB"))

mydf <- data.frame(IDA = c("a", "a", "a", "b", "b"),
                   IDB = c(1, 2, 1, 1, 2), values = 1:5)
mydf
getanID(mydf, 1:2)

Unlist a Column Stored as a List

Description

Unlists a column stored as a list into a long form.

Usage

listCol_l(inDT, listcol, drop = TRUE)
listCol_l(inDT, listcol, drop = TRUE)

Arguments

`inDT`	The input dataset.
`listcol`	The name of the column stored as a `list`.
`drop`	Logical. Should the original column be dropped? Defaults to `TRUE`.

Value

A data.table.

Author(s)

Ananda Mahto

Examples


dat <- data.frame(A = 1:3, B = I(list(c(1, 2), c(1, 3, 5), c(4))))
listCol_l(dat, "B")

dat <- data.frame(A = 1:3, B = I(list(c(1, 2), c(1, 3, 5), c(4))))
listCol_l(dat, "B")

Flatten a Column Stored as a List

Description

Flattens a column stored as a list into a wide form.

Usage

listCol_w(inDT, listcol, drop = TRUE, fill = NA_character_)
listCol_w(inDT, listcol, drop = TRUE, fill = NA_character_)

Arguments

`inDT`	The input dataset.
`listcol`	The name of the column stored as a `list`.
`drop`	Logical. Should the original column be dropped? Defaults to `TRUE`.
`fill`	The desired fill value. Defaults to `NA_character_`.

Value

A data.table.

Author(s)

Ananda Mahto

Examples


dat <- data.frame(A = 1:3, B = I(list(c(1, 2), c(1, 3, 5), c(4))))
listCol_w(dat, "B")

dat <- data.frame(A = 1:3, B = I(list(c(1, 2), c(1, 3, 5), c(4))))
listCol_w(dat, "B")

Take a List of Stacked data.tables and Merge Them

Description

A wrapper around the Stacked function to merge the resulting list into a single data.table.

Usage

merged.stack(data, id.vars = NULL, var.stubs, sep, keep.all = TRUE,
  ...)
merged.stack(data, id.vars = NULL, var.stubs, sep, keep.all = TRUE,
  ...)

Arguments

`data`	The input `data.frame`.
`id.vars`	The columns to be used as "ID" variables. Defaults to `NULL`, at which point, all names which are not identified as variable groups are used as the identifiers.
`var.stubs`	The prefixes of the variable groups.
`sep`	The character that separates the "variable name" from the "times" in the source `data.frame`. Alternatively, can be set to `"var.stubs"` (in quotes) if you do not have a value for `sep`.
`keep.all`	Logical. Should all the variables in the source `data.frame` be kept (`keep.all = TRUE`) or only those which comprise the `id.vars` and split data from the `var.stubs` (`keep.all = FALSE`).
`...`	Other arguments to be passed on to `Stacked` (for example, `keep.rownames` to retain the rownames of the input dataset, or `atStart`, in case `sep = "var.stubs"` is specified).

Value

A merged data.table.

Note

The keyed argument to Stacked has been hard- coded to TRUE to make merge work.

Author(s)

Ananda Mahto

Examples


set.seed(1)
mydf <- data.frame(id_1 = 1:6, id_2 = c("A", "B"),
                   varA.1 = sample(letters, 6),
                   varA.2 = sample(letters, 6),
                   varA.3 = sample(letters, 6),
                   varB.2 = sample(10, 6),
                   varB.3 = sample(10, 6),
                   varC.3 = rnorm(6))
mydf
merged.stack(mydf, var.stubs = c("varA", "varB", "varC"), sep = ".")



set.seed(1)
mydf <- data.frame(id_1 = 1:6, id_2 = c("A", "B"),
                   varA.1 = sample(letters, 6),
                   varA.2 = sample(letters, 6),
                   varA.3 = sample(letters, 6),
                   varB.2 = sample(10, 6),
                   varB.3 = sample(10, 6),
                   varC.3 = rnorm(6))
mydf
merged.stack(mydf, var.stubs = c("varA", "varB", "varC"), sep = ".")

Dataset Names as a Character Vector, Always

Description

A convenience function using either character vectors or numeric vectors to specify a subset of names of a data.frame.

Usage

Names(data, invec)
Names(data, invec)

Arguments

`data`	The input `data.frame`.
`invec`	The `names` you want.

Value

A character vector of the desired names.

Author(s)

Ananda Mahto

Examples


mydf <- data.frame(a = 1:2, b = 3:4, c = 5:6)
splitstackshape:::Names(mydf, c("a", "c"))
splitstackshape:::Names(mydf, c(1, 3))

mydf <- data.frame(a = 1:2, b = 3:4, c = 5:6)
splitstackshape:::Names(mydf, c("a", "c"))
splitstackshape:::Names(mydf, c(1, 3))

Split Basic Alphanumeric Strings Which Have No Separators

Description

Used to split strings like "Abc8" into "Abc" and "8".

Usage

NoSep(data, charfirst = TRUE)
NoSep(data, charfirst = TRUE)

Arguments

`data`	The vector of strings to be split.
`charfirst`	Is the string constructed with characters at the start or numbers? Defaults to `TRUE`.

Value

A data.frame with two columns, .var and .time_1.

Note

This is a helper function for the Stacked() and Reshape() functions.

Author(s)

Ananda Mahto

Examples


x <- paste0("Var", LETTERS[1:3], 1:3)
splitstackshape:::NoSep(x)

y <- paste0(1:3, "Var", LETTERS[1:3])
splitstackshape:::NoSep(y, charfirst = FALSE)

x <- paste0("Var", LETTERS[1:3], 1:3)
splitstackshape:::NoSep(x)

y <- paste0(1:3, "Var", LETTERS[1:3])
splitstackshape:::NoSep(y, charfirst = FALSE)

Create a Numeric Matrix from a List of Values

Description

Create a numeric matrix from a list of values

Usage

numMat(listOfValues, fill = NA, mode = "binary")
numMat(listOfValues, fill = NA, mode = "binary")

Arguments

`listOfValues`	A `list` of input values to be inserted in a matrix.
`fill`	The initializing fill value for the empty matrix.
`mode`	Either `"binary"` or `"value"`. Defaults to `"binary"`.

Details

This is primarily a helper function for the concat.split() function when creating the "expanded" structure. The input is anticipated to be a list of values obtained using base::strsplit().

Value

A matrix.

Author(s)

Ananda Mahto

Examples


invec <- c("1,2,4,5,6", "1,2,4,5,6", "1,2,4,5,6",
           "1,2,4,5,6", "-1,1,2,5,6", "1,2,5,6")
A <- strsplit(invec, ",")
splitstackshape:::numMat(A)
splitstackshape:::numMat(A, fill = 0)
splitstackshape:::numMat(A, mode = "value")

invec <- c("1,2,4,5,6", "1,2,4,5,6", "1,2,4,5,6",
           "1,2,4,5,6", "-1,1,2,5,6", "1,2,5,6")
A <- strsplit(invec, ",")
splitstackshape:::numMat(A)
splitstackshape:::numMat(A, fill = 0)
splitstackshape:::numMat(A, mode = "value")

Extract All Names From a Dataset Other Than the Ones Listed

Description

A convenience function for setdiff(names(data), -some_vector_of_names-).

Usage

othernames(data, toremove)
othernames(data, toremove)

Arguments

`data`	The input `data.frame`.
`toremove`	The `names` you want to exclude.

Value

A character vector of the remaining names.

Author(s)

Ananda Mahto

Examples


mydf <- data.frame(a = 1:2, b = 3:4, c = 5:6)
splitstackshape:::othernames(mydf, "a")

mydf <- data.frame(a = 1:2, b = 3:4, c = 5:6)
splitstackshape:::othernames(mydf, "a")

Read Concatenated Character Vectors Into a data.frame

Description

Originally a helper function for the concat.split.compact() function. This function has now been effectively replaced by cSplit().

Usage

read.concat(data, col.prefix, sep, ...)
read.concat(data, col.prefix, sep, ...)

Arguments

`data`	The input data.
`col.prefix`	The desired column prefix for the output `data.frame`.
`sep`	The character that acts as a delimiter.
`...`	Other arguments to pass to `utils::read.table()`.

Value

A data.frame.

Author(s)

Ananda Mahto

Examples


vec <- c("a,b", "c,d,e", "f, g", "h, i, j,k")
splitstackshape:::read.concat(vec, "var", ",")

## More than 5 lines the same
## `read.table` would fail with this
vec <- c("12,51,34,17", "84,28,17,10", "11,43,28,15",
"80,26,17,91", "10,41,25,13", "97,35,23,12,13")
splitstackshape:::read.concat(vec, "var", ",")

vec <- c("a,b", "c,d,e", "f, g", "h, i, j,k")
splitstackshape:::read.concat(vec, "var", ",")

## More than 5 lines the same
## `read.table` would fail with this
vec <- c("12,51,34,17", "84,28,17,10", "11,43,28,15",
"80,26,17,91", "10,41,25,13", "97,35,23,12,13")
splitstackshape:::read.concat(vec, "var", ",")

Reshape Wide Data Into a Semi-long Form

Description

The stats::reshape() function in base R is very handy when you want a semi-long (or semi-wide) data.frame. However, base R's reshape has problems is with "unbalanced" panel data, for instance data where one variable was measured at three points in time, and another only twice.

Usage

Reshape(data, id.vars = NULL, var.stubs, sep = ".", rm.rownames, ...)
Reshape(data, id.vars = NULL, var.stubs, sep = ".", rm.rownames, ...)

Arguments

`data`	The source `data.frame`.
`id.vars`	The variables that serve as unique identifiers. Defaults to `NULL`, at which point, all names which are not identified as variable groups are used as the identifiers.
`var.stubs`	The prefixes of the variable groups.
`sep`	The character that separates the "variable name" from the "times" in the wide `data.frame`.
`rm.rownames`	Ignored as `data.table`s do not have rownames anyway.
`...`	Further arguments to `NoSep()` in case the separator is of a different form.

Details

This function was written to overcome that limitation of dealing with unbalanced data, but is also appropriate for basic wide-to-long reshaping tasks.

Related functions like utils::stack() in base R and reshape2::melt() in "reshape2" are also very handy when you want a "long" reshaping of data, but they result in a very long structuring of your data, not the "semi-wide" format that reshape produces. data.table::melt() can produce output like reshape, but it also expects an equal number of measurements for each variable.

Value

A "long" data.table of the reshaped data that retains the attributes added by base R's reshape function.

Author(s)

Ananda Mahto

Examples


set.seed(1)
mydf <- data.frame(id_1 = 1:6, id_2 = c("A", "B"), varA.1 = sample(letters, 6),
                 varA.2 = sample(letters, 6), varA.3 = sample(letters, 6),
                 varB.2 = sample(10, 6), varB.3 = sample(10, 6),
                 varC.3 = rnorm(6))
mydf

## Note that these data are unbalanced
## reshape() will not work
## Not run: 
reshape(mydf, direction = "long", idvar=1:2, varying=3:ncol(mydf))

## End(Not run)

## The Reshape() function can handle such scenarios

Reshape(mydf, id.vars = c("id_1", "id_2"),
       var.stubs = c("varA", "varB", "varC"))

set.seed(1)
mydf <- data.frame(id_1 = 1:6, id_2 = c("A", "B"), varA.1 = sample(letters, 6),
                 varA.2 = sample(letters, 6), varA.3 = sample(letters, 6),
                 varB.2 = sample(10, 6), varB.3 = sample(10, 6),
                 varC.3 = rnorm(6))
mydf

## Note that these data are unbalanced
## reshape() will not work
## Not run: 
reshape(mydf, direction = "long", idvar=1:2, varying=3:ncol(mydf))

## End(Not run)

## The Reshape() function can handle such scenarios

Reshape(mydf, id.vars = c("id_1", "id_2"),
       var.stubs = c("varA", "varB", "varC"))

Stack Columns from a Wide Form to a Long Form

Description

A function to conveniently stack groups of wide columns into a long form which can then be merged together.

Usage

Stacked(data, id.vars = NULL, var.stubs, sep, keep.all = TRUE,
  keyed = TRUE, keep.rownames = FALSE, ...)
Stacked(data, id.vars = NULL, var.stubs, sep, keep.all = TRUE,
  keyed = TRUE, keep.rownames = FALSE, ...)

Arguments

`data`	The source `data.frame`.
`id.vars`	The variables that serve as unique identifiers. Defaults to `NULL`, at which point, all names which are not identified as variable groups are used as the identifiers.
`var.stubs`	The prefixes of the variable groups.
`sep`	The character that separates the "variable name" from the "times" in the wide `data.frame`. Alternatively, can be set to `"var.stubs"` (in quotes) if you do not have a value for `sep`.
`keep.all`	Logical. Should all the variables from the source `data.frame` be kept (`keep.all = TRUE`) or should the resulting `data.table` comprise only columns for the `id.vars`, `var.stubs`, and "times" (`keep.all = FALSE`). Other variables are recycled to appropriate length. For this to work, both `id.vars` and `var.stubs` must be specified.
`keyed`	Logical. Should the `Stacked` function automatically set the `key` for the resulting `data.table`s. If `TRUE` (default) the `key` is set to the `id.vars` and the "time" variables that are created by `Stacked`.
`keep.rownames`	Logical. Should rownames be kept when converting the input to a `data.table`? Defaults to `FALSE`.
`...`	Other arguments to be passed on when `sep = "var.stubs"` (specifically, `atStart`: A logical argument to indicate whether the stubs come at the start or at the end of the variable names).

Value

A list of data.tables with one data.table for each "var.stub". The key is set to the id.vars and .time_# vars.

Note

This is the function internally called by merged.stack.

Author(s)

Ananda Mahto

Examples


set.seed(1)
mydf <- data.frame(id_1 = 1:6, id_2 = c("A", "B"),
                   varA.1 = sample(letters, 6),
                   varA.2 = sample(letters, 6),
                   varA.3 = sample(letters, 6),
                   varB.2 = sample(10, 6),
                   varB.3 = sample(10, 6),
                   varC.3 = rnorm(6))
mydf
Stacked(data = mydf, var.stubs = c("varA", "varB", "varC"), sep = ".")



set.seed(1)
mydf <- data.frame(id_1 = 1:6, id_2 = c("A", "B"),
                   varA.1 = sample(letters, 6),
                   varA.2 = sample(letters, 6),
                   varA.3 = sample(letters, 6),
                   varB.2 = sample(10, 6),
                   varB.3 = sample(10, 6),
                   varC.3 = rnorm(6))
mydf
Stacked(data = mydf, var.stubs = c("varA", "varB", "varC"), sep = ".")

Take a Stratified Sample From a Dataset

Description

The stratified function samples from a data.table in which one or more columns can be used as a "stratification" or "grouping" variable. The result is a new data.table with the specified number of samples from each group.

Usage

stratified(indt, group, size, select = NULL, replace = FALSE,
  keep.rownames = FALSE, bothSets = FALSE, ...)
stratified(indt, group, size, select = NULL, replace = FALSE,
  keep.rownames = FALSE, bothSets = FALSE, ...)

Arguments

`indt`	The input `data.table`.
`group`	The column or columns that should be used to create the groups. Can be a character vector of column names (recommended) or a numeric vector of column positions. Generally, if you are using more than one variable to create your "strata", you should list them in the order of slowest varying to quickest varying. This can be a vector of names or column indexes.
`size`	The desired sample size. If `size` is a value between `0` and `1` expressed as a decimal, size is set to be proportional to the number of observations per group. If `size` is a single positive integer, it will be assumed that you want the same number of samples from each group. If `size` is a named vector, the function will check to see whether the length of the vector matches the number of groups and that the names match the group names.
`select`	A named list containing levels from the `"group"` variables in which you are interested. The list names must be present as variable names for the input dataset.
`replace`	Logical. Should sampling be with replacement? Defaults to `FALSE`.
`keep.rownames`	Logical. If the input is a `data.frame` with `rownames`, `as.data.table` would normally drop the rownames. If `TRUE`, the rownames would be retained in a column named `rn`. Defaults to `FALSE`.
`bothSets`	Logical. Should both the sampled and non-sampled sets be returned as a `list`? Defaults to `FALSE`.
`...`	Optional arguments to `base::sample()`.

Value

If bothSets = TRUE, a list of two data.tables; otherwise, a data.table.

Note

Slightly different sizes than requested: Because of how computers deal with floating-point arithmetic, and because R uses a "round to even" approach, the size per strata that results when specifying a proportionate sample may be one sample higher or lower per strata than you might have expected.

Author(s)

Ananda Mahto

Examples


# Generate a sample data.frame to play with
set.seed(1)
DF <- data.frame(
  ID = 1:100,
  A = sample(c("AA", "BB", "CC", "DD", "EE"), 100, replace = TRUE),
  B = rnorm(100), C = abs(round(rnorm(100), digits=1)),
  D = sample(c("CA", "NY", "TX"), 100, replace = TRUE),
  E = sample(c("M", "F"), 100, replace = TRUE))

# Take a 10% sample from all -A- groups in DF
stratified(DF, "A", .1)

# Take a 10% sample from only "AA" and "BB" groups from -A- in DF
stratified(DF, "A", .1, select = list(A = c("AA", "BB")))

# Take 5 samples from all -D- groups in DF, specified by column number
stratified(DF, group = 5, size = 5)

# Use a two-column strata: -E- and -D-
stratified(DF, c("E", "D"), size = .15)

# Use a two-column strata (-E- and -D-) but only use cases where -E- == "M"
stratified(DF, c("E", "D"), .15, select = list(E = "M"))

## As above, but where -E- == "M" and -D- == "CA" or "TX"
stratified(DF, c("E", "D"), .15, select = list(E = "M", D = c("CA", "TX")))

# Use a three-column strata: -E-, -D-, and -A-
stratified(DF, c("E", "D", "A"), size = 2)

## Not run: 
# The following will produce errors
stratified(DF, "D", c(5, 3))
stratified(DF, "D", c(5, 3, 2))

## End(Not run)

# Sizes using a named vector
stratified(DF, "D", c(CA = 5, NY = 3, TX = 2))

# Works with multiple groups as well
stratified(DF, c("D", "E"), 
           c("NY F" = 2, "NY M" = 3, "TX F" = 1, "TX M" = 1,
             "CA F" = 5, "CA M" = 1))

# Generate a sample data.frame to play with
set.seed(1)
DF <- data.frame(
  ID = 1:100,
  A = sample(c("AA", "BB", "CC", "DD", "EE"), 100, replace = TRUE),
  B = rnorm(100), C = abs(round(rnorm(100), digits=1)),
  D = sample(c("CA", "NY", "TX"), 100, replace = TRUE),
  E = sample(c("M", "F"), 100, replace = TRUE))

# Take a 10% sample from all -A- groups in DF
stratified(DF, "A", .1)

# Take a 10% sample from only "AA" and "BB" groups from -A- in DF
stratified(DF, "A", .1, select = list(A = c("AA", "BB")))

# Take 5 samples from all -D- groups in DF, specified by column number
stratified(DF, group = 5, size = 5)

# Use a two-column strata: -E- and -D-
stratified(DF, c("E", "D"), size = .15)

# Use a two-column strata (-E- and -D-) but only use cases where -E- == "M"
stratified(DF, c("E", "D"), .15, select = list(E = "M"))

## As above, but where -E- == "M" and -D- == "CA" or "TX"
stratified(DF, c("E", "D"), .15, select = list(E = "M", D = c("CA", "TX")))

# Use a three-column strata: -E-, -D-, and -A-
stratified(DF, c("E", "D", "A"), size = 2)

## Not run: 
# The following will produce errors
stratified(DF, "D", c(5, 3))
stratified(DF, "D", c(5, 3, 2))

## End(Not run)

# Sizes using a named vector
stratified(DF, "D", c(CA = 5, NY = 3, TX = 2))

# Works with multiple groups as well
stratified(DF, c("D", "E"), 
           c("NY F" = 2, "NY M" = 3, "TX F" = 1, "TX M" = 1,
             "CA F" = 5, "CA M" = 1))

Package 'splitstackshape'

Help Index

splitstackshape

Description

Details

Author(s)

Examples

Create a Binary Matrix from a List of Character Values

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Split Concatenated Cells in a Dataset

Description

Usage

Arguments

Details

Note

Author(s)

See Also

Examples

Split Concatenated Cells into a Condensed Format

Description

Usage

Arguments

Value

Note

Author(s)

See Also

Examples

Split Concatenated Values into their Corresponding Column Position

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Split Concatenated Cells into a List Format

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Split Concatenated Cells and Optionally Reshape the Output

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Example Dataset with Concatenated Cells

Description

Format

Split Concatenated Values into Separate Values

Description

Usage

Arguments

Value

Note

Author(s)

See Also

Examples

Expand the Rows of a Dataset

Description

Usage

Arguments

Value

Author(s)

References

Examples

Convert All Factor Columns to Character Columns

Description