splitstackshape

Description

Stack and Reshape Datasets After Splitting Concatenated Values

Details

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 mrdwab@gmail.com

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)

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)

Arguments

Author(s)

Ananda Mahto

See Also

utils::read.table()

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

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)

Arguments

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))

Split Basic Alphanumeric Strings Which Have No Separators

Description

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

Usage

NoSep(data, charfirst = TRUE)

Arguments

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

See Also

base::strsplit()

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)

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, ...)

Arguments

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

See Also

Stacked() , utils::stack() , stats::reshape() , reshape2::melt() , data.table::melt()

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"))

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 merge d together.

Usage

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

Arguments

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

See Also

stack , melt from "reshape2".

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 = ".")

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)

Arguments

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

See Also

concat.split()

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", "_")

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")

Arguments

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

See Also

base::strsplit() , numMat() .

Examples

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, ...)

Arguments

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

See Also

cSplit() , cSplit_l() , cSplit_e()

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))

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, ...)

Arguments

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

See Also

read.concat() , cSplit()

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)

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)

Arguments

Value

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

Author(s)

Ananda Mahto

See Also

cSplit() , cSplit_l() , numMat() , charMat()

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)

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)

Arguments

Value

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

Author(s)

Ananda Mahto

See Also

cSplit() , cSplit_e()

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)

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",
 ...)

Arguments

Value

A data.table.

Author(s)

Ananda Mahto

See Also

cSplit()

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)

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.

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)

Arguments

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)

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)

Arguments

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)

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)

Arguments

Value

A data.table.

Author(s)

Ananda Mahto

See Also

listCol_w to flatten a list column into a "wide" format.

Examples

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_)

Arguments

Value

A data.table.

Author(s)

Ananda Mahto

See Also

listCol_l to unlist a list column into a "long" format.

Examples

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,
 ...)

Arguments

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

See Also

Stacked , Reshape

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 = ".")

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")

Arguments

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

See Also

base::strsplit() , charMat() .

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")

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)

Arguments

Value

A character vector of the remaining names.

Author(s)

Ananda Mahto

See Also

base::setdiff()

Examples

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, ...)

Arguments

Value

A data.frame.

Author(s)

Ananda Mahto

See Also

utils::read.table()

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", ",")

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, ...)

Arguments

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

See Also

sampling::strata() from the "strata" package; dplyr::sample_n() and dplyr::sample_frac() from "dplyr".

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))