Package 'gtools'

two

asc(char, simplify = TRUE)

chr(ascii)
asc(char, simplify = TRUE)

chr(ascii)

## ascii codes for lowercase letters
asc(letters)

## uppercase letters from ascii codes
chr(65:90)

## works on muti-character strings
(tmp <- asc("hello!"))
chr(tmp)

## Use 'simplify=FALSE' to return the result as a list
(tmp <- asc("hello!", simplify = FALSE))
chr(tmp)

## When simplify=FALSE the results can be...
asc(c("a", "e", "i", "o", "u", "y")) # a vector
asc(c("ae", "io", "uy")) # or a matrix

## When simplify=TRUE the results are always a list...
asc(c("a", "e", "i", "o", "u", "y"), simplify = FALSE)
asc(c("ae", "io", "uy"), simplify = FALSE)
## ascii codes for lowercase letters
asc(letters)

## uppercase letters from ascii codes
chr(65:90)

## works on muti-character strings
(tmp <- asc("hello!"))
chr(tmp)

## Use 'simplify=FALSE' to return the result as a list
(tmp <- asc("hello!", simplify = FALSE))
chr(tmp)

## When simplify=FALSE the results can be...
asc(c("a", "e", "i", "o", "u", "y")) # a vector
asc(c("ae", "io", "uy")) # or a matrix

## When simplify=TRUE the results are always a list...
asc(c("a", "e", "i", "o", "u", "y"), simplify = FALSE)
asc(c("ae", "io", "uy"), simplify = FALSE)
ASCIIfy(x, bytes = 2, fallback = "?")
ASCIIfy(x, bytes = 2, fallback = "?")
write(ASCIIfy(x), "file.txt")
cat(paste(ASCIIfy(x), collapse="\n"), "\n", sep="")
cities <- c("S\u00e3o Paulo", "Reykjav\u00edk")
print(cities)
ASCIIfy(cities, 1)
ASCIIfy(cities, 2)

athens <- "\u0391\u03b8\u03ae\u03bd\u03b1"
print(athens)
ASCIIfy(athens)

cities <- c("S\u00e3o Paulo", "Reykjav\u00edk")
print(cities)
ASCIIfy(cities, 1)
ASCIIfy(cities, 2)

athens <- "\u0391\u03b8\u03ae\u03bd\u03b1"
print(athens)
ASCIIfy(athens)
ask(msg = "Press <RETURN> to continue: ", con = stdin())
ask(msg = "Press <RETURN> to continue: ", con = stdin())

# use default prompt
ask()

silly <- function() {
  age <- ask("How old aroe you? ")
  age <- as.numeric(age)
  cat("In 10 years you will be", age + 10, "years old!\n")
}
# use default prompt
ask()

silly <- function() {
  age <- ask("How old aroe you? ")
  age <- as.numeric(age)
  cat("In 10 years you will be", age + 10, "years old!\n")
}
assert(...)

capture(expression, collapse = "\n")

sprint(x, ...)
assert(...)

capture(expression, collapse = "\n")

sprint(x, ...)

data(badDend)
data(badDend)
baseOf(v, base = 10, len = 1)
baseOf(v, base = 10, len = 1)

# decimal representation
baseOf(123)

# binary representation
baseOf(123, base = 2)

# octal representation
baseOf(123, base = 8)

# hexadecimal representation
baseOf(123, base = 16)

# hexadecimal with more typical letter-notation
c(0:9, LETTERS)[baseOf(123, 16)]

# hexadecimal again, now showing a single string
paste(c(0:9, LETTERS)[baseOf(123, 16)], collapse = "")

# decimal representation but filling leading zeroes
baseOf(123, len = 5)

# and converting that back
sum(2^(4:0) * baseOf(123, len = 5))

# hashing and a tabular venn diagram derived from it
m <- matrix(sample(c(FALSE, TRUE), replace = TRUE, size = 300), ncol = 4)
colnames(m) <- c("strong", "colorful", "nice", "humorous")
names(dimnames(m)) <- c("samples", "features")
head(m)

m.val <- apply(m, 1, function(X) {
  return(sum(2^((ncol(m) - 1):0) * X))
})
m.val.rle <- rle(sort(m.val))
m.counts <- cbind(
  baseOf(m.val.rle$value, base = 2, len = ncol(m)),
  m.val.rle$lengths
)
colnames(m.counts) <- c(colnames(m), "num")
rownames(m.counts) <- apply(m.counts[, 1:ncol(m)], 1, paste, collapse = "")
m.counts[1 == m.counts[, "nice"] & 1 == m.counts[, "humorous"], , drop = FALSE]
m.counts[, "num", drop = TRUE]
# decimal representation
baseOf(123)

# binary representation
baseOf(123, base = 2)

# octal representation
baseOf(123, base = 8)

# hexadecimal representation
baseOf(123, base = 16)

# hexadecimal with more typical letter-notation
c(0:9, LETTERS)[baseOf(123, 16)]

# hexadecimal again, now showing a single string
paste(c(0:9, LETTERS)[baseOf(123, 16)], collapse = "")

# decimal representation but filling leading zeroes
baseOf(123, len = 5)

# and converting that back
sum(2^(4:0) * baseOf(123, len = 5))

# hashing and a tabular venn diagram derived from it
m <- matrix(sample(c(FALSE, TRUE), replace = TRUE, size = 300), ncol = 4)
colnames(m) <- c("strong", "colorful", "nice", "humorous")
names(dimnames(m)) <- c("samples", "features")
head(m)

m.val <- apply(m, 1, function(X) {
  return(sum(2^((ncol(m) - 1):0) * X))
})
m.val.rle <- rle(sort(m.val))
m.counts <- cbind(
  baseOf(m.val.rle$value, base = 2, len = ncol(m)),
  m.val.rle$lengths
)
colnames(m.counts) <- c(colnames(m), "num")
rownames(m.counts) <- apply(m.counts[, 1:ncol(m)], 1, paste, collapse = "")
m.counts[1 == m.counts[, "nice"] & 1 == m.counts[, "humorous"], , drop = FALSE]
m.counts[, "num", drop = TRUE]
binsearch(
  fun,
  range,
  ...,
  target = 0,
  lower = ceiling(min(range)),
  upper = floor(max(range)),
  maxiter = 100,
  showiter = FALSE
)
binsearch(
  fun,
  range,
  ...,
  target = 0,
  lower = ceiling(min(range)),
  upper = floor(max(range)),
  maxiter = 100,
  showiter = FALSE
)

### Toy examples

# search for x=10
binsearch(function(x) x - 10, range = c(0, 20))

# search for x=10.1
binsearch(function(x) x - 10.1, range = c(0, 20))

### Classical toy example

# binary search for the index of 'M' among the sorted letters
fun <- function(X) {
  ifelse(LETTERS[X] > "M", 1,
    ifelse(LETTERS[X] < "M", -1, 0)
  )
}

binsearch(fun, range = 1:26)
# returns $where=13
LETTERS[13]

### Substantive example, from genetics
## Not run: 
library(genetics)
# Determine the necessary sample size to detect all alleles with
# frequency 0.07 or greater with probability 0.95.
power.fun <- function(N) 1 - gregorius(N = N, freq = 0.07)$missprob

binsearch(power.fun, range = c(0, 100), target = 0.95)

# equivalent to
gregorius(freq = 0.07, missprob = 0.05)

## End(Not run)

### Toy examples

# search for x=10
binsearch(function(x) x - 10, range = c(0, 20))

# search for x=10.1
binsearch(function(x) x - 10.1, range = c(0, 20))

### Classical toy example

# binary search for the index of 'M' among the sorted letters
fun <- function(X) {
  ifelse(LETTERS[X] > "M", 1,
    ifelse(LETTERS[X] < "M", -1, 0)
  )
}

binsearch(fun, range = 1:26)
# returns $where=13
LETTERS[13]

### Substantive example, from genetics
## Not run: 
library(genetics)
# Determine the necessary sample size to detect all alleles with
# frequency 0.07 or greater with probability 0.95.
power.fun <- function(N) 1 - gregorius(N = N, freq = 0.07)$missprob

binsearch(power.fun, range = c(0, 100), target = 0.95)

# equivalent to
gregorius(freq = 0.07, missprob = 0.05)

## End(Not run)
capwords(
  s,
  strict = FALSE,
  AP = TRUE,
  onlyfirst = FALSE,
  preserveMixed = FALSE,
  sep = " "
)
capwords(
  s,
  strict = FALSE,
  AP = TRUE,
  onlyfirst = FALSE,
  preserveMixed = FALSE,
  sep = " "
)

capwords("a function to capitalize words in a title")
capwords("a function to capitalize words in a title", AP = FALSE)

capwords("testing the iProduct for defects")
capwords("testing the iProduct for defects", strict = TRUE)
capwords("testing the iProduct for defects", onlyfirst = TRUE)
capwords("testing the iProduct for defects", preserveMixed = TRUE)

capwords("title_using_underscores_as_separators", sep = "_")
capwords("a function to capitalize words in a title")
capwords("a function to capitalize words in a title", AP = FALSE)

capwords("testing the iProduct for defects")
capwords("testing the iProduct for defects", strict = TRUE)
capwords("testing the iProduct for defects", onlyfirst = TRUE)
capwords("testing the iProduct for defects", preserveMixed = TRUE)

capwords("title_using_underscores_as_separators", sep = "_")
checkRVersion(quiet = FALSE)
checkRVersion(quiet = FALSE)

try(
   ver <- checkRVersion()
)
print(ver)
try(
   ver <- checkRVersion()
)
print(ver)
combinations(n, r, v = 1:n, set = TRUE, repeats.allowed = FALSE)

permutations(n, r, v = 1:n, set = TRUE, repeats.allowed = FALSE)
combinations(n, r, v = 1:n, set = TRUE, repeats.allowed = FALSE)

permutations(n, r, v = 1:n, set = TRUE, repeats.allowed = FALSE)

combinations(3,2,letters[1:3])
combinations(3,2,letters[1:3],repeats=TRUE)

permutations(3,2,letters[1:3])
permutations(3,2,letters[1:3],repeats=TRUE)

## Not run: 
# To use large 'n', you need to change the default recusion limit
options(expressions=1e5)
cmat <- combinations(300,2)
dim(cmat) # 44850 by 2 

## End(Not run)

combinations(3,2,letters[1:3])
combinations(3,2,letters[1:3],repeats=TRUE)

permutations(3,2,letters[1:3])
permutations(3,2,letters[1:3],repeats=TRUE)

## Not run: 
# To use large 'n', you need to change the default recusion limit
options(expressions=1e5)
cmat <- combinations(300,2)
dim(cmat) # 44850 by 2 

## End(Not run)
defmacro(..., expr)

strmacro(..., expr, strexpr)
defmacro(..., expr)

strmacro(..., expr, strexpr)
 mul <- defmacro(a, b, expr={a*b}) 

####
# macro for replacing a specified missing value indicator with NA
# within a dataframe
###
setNA <- defmacro(df, var, values,
  expr = {
    df$var[df$var %in% values] <- NA
  }
)

# create example data using 999 as a missing value indicator
d <- data.frame(
  Grp = c("Trt", "Ctl", "Ctl", "Trt", "Ctl", "Ctl", "Trt", "Ctl", "Trt", "Ctl"),
  V1 = c(1, 2, 3, 4, 5, 6, 999, 8, 9, 10),
  V2 = c(1, 1, 1, 1, 1, 2, 999, 2, 999, 999),
  stringsAsFactors = TRUE
)
d

# Try it out
setNA(d, V1, 999)
setNA(d, V2, 999)
d

###
# Expression macro
###
plot.d <- defmacro(df, var, DOTS,
  col = "red", title = "", expr =
    plot(df$var ~ df$Grp, type = "b", col = col, main = title, ...)
)

plot.d(d, V1)
plot.d(d, V1, col = "blue")
plot.d(d, V1, lwd = 4) # use optional 'DOTS' argument

###
# String macro (note the quoted text in the calls below)
#
# This style of macro can be useful when you are reading
# function arguments from a text file
###
plot.s <- strmacro(DF, VAR,
  COL = "'red'", TITLE = "''", DOTS = "", expr =
    plot(DF$VAR ~ DF$Grp, type = "b", col = COL, main = TITLE, DOTS)
)

plot.s("d", "V1")
plot.s(DF = "d", VAR = "V1", COL = '"blue"')
plot.s("d", "V1", DOTS = "lwd=4") # use optional 'DOTS' argument

#######
# Create a macro that defines new functions
######
plot.sf <- defmacro(
  type = "b", col = "black",
  title = deparse(substitute(x)), DOTS, expr =
    function(x, y) plot(x, y, type = type, col = col, main = title, ...)
)

plot.red <- plot.sf(col = "red", title = "Red is more Fun!")
plot.blue <- plot.sf(col = "blue", title = "Blue is Best!", lty = 2)

plot.red(1:100, rnorm(100))
plot.blue(1:100, rnorm(100))
####
# macro for replacing a specified missing value indicator with NA
# within a dataframe
###
setNA <- defmacro(df, var, values,
  expr = {
    df$var[df$var %in% values] <- NA
  }
)

# create example data using 999 as a missing value indicator
d <- data.frame(
  Grp = c("Trt", "Ctl", "Ctl", "Trt", "Ctl", "Ctl", "Trt", "Ctl", "Trt", "Ctl"),
  V1 = c(1, 2, 3, 4, 5, 6, 999, 8, 9, 10),
  V2 = c(1, 1, 1, 1, 1, 2, 999, 2, 999, 999),
  stringsAsFactors = TRUE
)
d

# Try it out
setNA(d, V1, 999)
setNA(d, V2, 999)
d

###
# Expression macro
###
plot.d <- defmacro(df, var, DOTS,
  col = "red", title = "", expr =
    plot(df$var ~ df$Grp, type = "b", col = col, main = title, ...)
)

plot.d(d, V1)
plot.d(d, V1, col = "blue")
plot.d(d, V1, lwd = 4) # use optional 'DOTS' argument

###
# String macro (note the quoted text in the calls below)
#
# This style of macro can be useful when you are reading
# function arguments from a text file
###
plot.s <- strmacro(DF, VAR,
  COL = "'red'", TITLE = "''", DOTS = "", expr =
    plot(DF$VAR ~ DF$Grp, type = "b", col = COL, main = TITLE, DOTS)
)

plot.s("d", "V1")
plot.s(DF = "d", VAR = "V1", COL = '"blue"')
plot.s("d", "V1", DOTS = "lwd=4") # use optional 'DOTS' argument

#######
# Create a macro that defines new functions
######
plot.sf <- defmacro(
  type = "b", col = "black",
  title = deparse(substitute(x)), DOTS, expr =
    function(x, y) plot(x, y, type = type, col = col, main = title, ...)
)

plot.red <- plot.sf(col = "red", title = "Red is more Fun!")
plot.blue <- plot.sf(col = "blue", title = "Blue is Best!", lty = 2)

plot.red(1:100, rnorm(100))
plot.blue(1:100, rnorm(100))
ddirichlet(x, alpha)

rdirichlet(n, alpha)
ddirichlet(x, alpha)

rdirichlet(n, alpha)

x <- rdirichlet(20, c(1, 1, 1))

ddirichlet(x, c(1, 1, 1))
x <- rdirichlet(20, c(1, 1, 1))

ddirichlet(x, c(1, 1, 1))
foldchange(num, denom)

logratio2foldchange(logratio, base = 2)

foldchange2logratio(foldchange, base = 2)
foldchange(num, denom)

logratio2foldchange(logratio, base = 2)

foldchange2logratio(foldchange, base = 2)

a <- 1:21
b <- 21:1

f <- foldchange(a, b)

cbind(a, b, f)
a <- 1:21
b <- 21:1

f <- foldchange(a, b)

cbind(a, b, f)
getDependencies(
  pkgs,
  dependencies = c("Depends", "Imports", "LinkingTo"),
  installed = TRUE,
  available = TRUE,
  base = FALSE,
  recommended = FALSE
)
getDependencies(
  pkgs,
  dependencies = c("Depends", "Imports", "LinkingTo"),
  installed = TRUE,
  available = TRUE,
  base = FALSE,
  recommended = FALSE
)

## Not run: 
## A locally installed package
#' getDependencies("MASS", installed = TRUE, available = FALSE)

## A package on CRAN
getDependencies("gregmisc", installed = FALSE, available = TRUE)

## Show base and recommended dependencies
getDependencies("MASS", available = FALSE, base = TRUE, recommended = TRUE)

## Download the set of packages necessary to support a local package
deps <- getDependencies("MyLocalPackage", available = FALSE)
download.packages(deps, destdir = "./R_Packages")

## End(Not run)

## Not run: 
## A locally installed package
#' getDependencies("MASS", installed = TRUE, available = FALSE)

## A package on CRAN
getDependencies("gregmisc", installed = FALSE, available = TRUE)

## Show base and recommended dependencies
getDependencies("MASS", available = FALSE, base = TRUE, recommended = TRUE)

## Download the set of packages necessary to support a local package
deps <- getDependencies("MyLocalPackage", available = FALSE)
download.packages(deps, destdir = "./R_Packages")

## End(Not run)
invalid(x)
invalid(x)

invalid(NA)
invalid()
invalid(c(NA, NA, NULL, NA))

invalid(list(a = 1, b = NULL))

x <- try(log("A"))
invalid(x)

# example use in a function
myplot <- function(x, y) {
  if (invalid(y)) {
    y <- x
    x <- 1:length(y)
  }
  plot(x, y)
}
myplot(1:10)
myplot(1:10, NA)
invalid(NA)
invalid()
invalid(c(NA, NA, NULL, NA))

invalid(list(a = 1, b = NULL))

x <- try(log("A"))
invalid(x)

# example use in a function
myplot <- function(x, y) {
  if (invalid(y)) {
    y <- x
    x <- 1:length(y)
  }
  plot(x, y)
}
myplot(1:10)
myplot(1:10, NA)
keywords(topic)
keywords(topic)

## Show all valid R keywords
## Not run: 
keywords()

## Show keywords associated with the 'merge' function
keywords(merge)
keywords("merge")

## End(Not run)
## Show all valid R keywords
## Not run: 
keywords()

## Show keywords associated with the 'merge' function
keywords(merge)
keywords("merge")

## End(Not run)
lastAdd(fun)
lastAdd(fun)

## Print a couple of cute messages when R exits.
helloWorld <- function() cat("\nHello World!\n")
byeWorld <- function() cat("\nGoodbye World!\n")

.Last <- lastAdd(byeWorld)
.Last <- lastAdd(helloWorld)

## Not run: 
q("no")

## Should yield:
##
##   Save workspace image? [y/n/c]: n
##
##   Hello World!
##
##   Goodbye World!
##
##   Process R finished at Tue Nov 22 10:28:55 2005

## End(Not run)

## Print a couple of cute messages when R exits.
helloWorld <- function() cat("\nHello World!\n")
byeWorld <- function() cat("\nGoodbye World!\n")

.Last <- lastAdd(byeWorld)
.Last <- lastAdd(helloWorld)

## Not run: 
q("no")

## Should yield:
##
##   Save workspace image? [y/n/c]: n
##
##   Hello World!
##
##   Goodbye World!
##
##   Process R finished at Tue Nov 22 10:28:55 2005

## End(Not run)
loadedPackages(silent = FALSE)
loadedPackages(silent = FALSE)

loadedPackages()
loadedPackages()
logit(x, min = 0, max = 1)

inv.logit(x, min = 0, max = 1)
logit(x, min = 0, max = 1)

inv.logit(x, min = 0, max = 1)

x <- seq(0, 10, by = 0.25)
xt <- logit(x, min = 0, max = 10)
cbind(x, xt)

y <- inv.logit(xt, min = 0, max = 10)
cbind(x, xt, y)
x <- seq(0, 10, by = 0.25)
xt <- logit(x, min = 0, max = 10)
cbind(x, xt)

y <- inv.logit(xt, min = 0, max = 10)
cbind(x, xt, y)
mixedsort(
  x,
  decreasing = FALSE,
  na.last = TRUE,
  blank.last = FALSE,
  numeric.type = c("decimal", "roman"),
  roman.case = c("upper", "lower", "both"),
  scientific = TRUE
)

mixedorder(
  x,
  decreasing = FALSE,
  na.last = TRUE,
  blank.last = FALSE,
  numeric.type = c("decimal", "roman"),
  roman.case = c("upper", "lower", "both"),
  scientific = TRUE
)
mixedsort(
  x,
  decreasing = FALSE,
  na.last = TRUE,
  blank.last = FALSE,
  numeric.type = c("decimal", "roman"),
  roman.case = c("upper", "lower", "both"),
  scientific = TRUE
)

mixedorder(
  x,
  decreasing = FALSE,
  na.last = TRUE,
  blank.last = FALSE,
  numeric.type = c("decimal", "roman"),
  roman.case = c("upper", "lower", "both"),
  scientific = TRUE
)

## compound & dose labels
Treatment <- c(
  "Control", "Aspirin 10mg/day", "Aspirin 50mg/day",
  "Aspirin 100mg/day", "Acetomycin 100mg/day",
  "Acetomycin 1000mg/day"
)

## ordinary sort puts the dosages in the wrong order
sort(Treatment)

## but mixedsort does the 'right' thing
mixedsort(Treatment)

## Here is a more complex example
x <- rev(c(
  "AA 0.50 ml", "AA 1.5 ml", "AA 500 ml", "AA 1500 ml",
  "EXP 1", "AA 1e3 ml", "A A A", "1 2 3 A", "NA", NA, "1e2",
  "", "-", "1A", "1 A", "100", "100A", "Inf"
))

mixedorder(x)

mixedsort(x) # Notice that plain numbers, including 'Inf' show up
# before strings, NAs at the end, and blanks at the
# beginning .

mixedsort(x, na.last = TRUE) # default
mixedsort(x, na.last = FALSE) # push NAs to the front

mixedsort(x, blank.last = FALSE) # default
mixedsort(x, blank.last = TRUE) # push blanks to the end

mixedsort(x, decreasing = FALSE) # default
mixedsort(x, decreasing = TRUE) # reverse sort order

## Roman numerals
chapters <- c(
  "V. Non Sequiturs", "II. More Nonsense",
  "I. Nonsense", "IV. Nonesensical Citations",
  "III. Utter Nonsense"
)
mixedsort(chapters, numeric.type = "roman")

## Lower-case Roman numerals
vals <- c(
  "xix", "xii", "mcv", "iii", "iv", "dcclxxii", "cdxcii",
  "dcxcviii", "dcvi", "cci"
)
(ordered <- mixedsort(vals, numeric.type = "roman", roman.case = "lower"))
roman2int(ordered)

## Control scientific notation for number matching:
vals <- c("3E1", "2E3", "4e0")

mixedsort(vals) # With scientfic notation
mixedsort(vals, scientific = FALSE) # Without scientfic notation
## compound & dose labels
Treatment <- c(
  "Control", "Aspirin 10mg/day", "Aspirin 50mg/day",
  "Aspirin 100mg/day", "Acetomycin 100mg/day",
  "Acetomycin 1000mg/day"
)

## ordinary sort puts the dosages in the wrong order
sort(Treatment)

## but mixedsort does the 'right' thing
mixedsort(Treatment)

## Here is a more complex example
x <- rev(c(
  "AA 0.50 ml", "AA 1.5 ml", "AA 500 ml", "AA 1500 ml",
  "EXP 1", "AA 1e3 ml", "A A A", "1 2 3 A", "NA", NA, "1e2",
  "", "-", "1A", "1 A", "100", "100A", "Inf"
))

mixedorder(x)

mixedsort(x) # Notice that plain numbers, including 'Inf' show up
# before strings, NAs at the end, and blanks at the
# beginning .

mixedsort(x, na.last = TRUE) # default
mixedsort(x, na.last = FALSE) # push NAs to the front

mixedsort(x, blank.last = FALSE) # default
mixedsort(x, blank.last = TRUE) # push blanks to the end

mixedsort(x, decreasing = FALSE) # default
mixedsort(x, decreasing = TRUE) # reverse sort order

## Roman numerals
chapters <- c(
  "V. Non Sequiturs", "II. More Nonsense",
  "I. Nonsense", "IV. Nonesensical Citations",
  "III. Utter Nonsense"
)
mixedsort(chapters, numeric.type = "roman")

## Lower-case Roman numerals
vals <- c(
  "xix", "xii", "mcv", "iii", "iv", "dcclxxii", "cdxcii",
  "dcxcviii", "dcvi", "cci"
)
(ordered <- mixedsort(vals, numeric.type = "roman", roman.case = "lower"))
roman2int(ordered)

## Control scientific notation for number matching:
vals <- c("3E1", "2E3", "4e0")

mixedsort(vals) # With scientfic notation
mixedsort(vals, scientific = FALSE) # Without scientfic notation
na.replace(x, replace, ...)
na.replace(x, replace, ...)

x <- c(1, 2, 3, NA, 6, 7, 8, NA, NA)

# Replace with a specified value
na.replace(x, "999")

# Replace with the calculated median
na.replace(x, median, na.rm = TRUE)
x <- c(1, 2, 3, NA, 6, 7, 8, NA, NA)

# Replace with a specified value
na.replace(x, "999")

# Replace with the calculated median
na.replace(x, median, na.rm = TRUE)
odd(x)

even(x)
odd(x)

even(x)

odd(4)
even(4)

odd(1:10)
even(1:10)
odd(4)
even(4)

odd(1:10)
even(1:10)
permute(x)
permute(x)

x <- 1:10
permute(x)
x <- 1:10
permute(x)
quantcut(x, q = 4, na.rm = TRUE, ...)
quantcut(x, q = 4, na.rm = TRUE, ...)

## create example data
# testonly{
set.seed(1234)
# }
x <- rnorm(1000)

## cut into quartiles
quartiles <- quantcut(x)
table(quartiles)

## cut into deciles
deciles.1 <- quantcut(x, 10)
table(deciles.1)
# or equivalently
deciles.2 <- quantcut(x, seq(0, 1, by = 0.1))

# testonly{
stopifnot(identical(deciles.1, deciles.2))
# }

## show handling of 'tied' quantiles.
x <- round(x) # discretize to create ties
stem(x) # display the ties
deciles <- quantcut(x, 10)

table(deciles) # note that there are only 5 groups (not 10)
# due to duplicates
## create example data
# testonly{
set.seed(1234)
# }
x <- rnorm(1000)

## cut into quartiles
quartiles <- quantcut(x)
table(quartiles)

## cut into deciles
deciles.1 <- quantcut(x, 10)
table(deciles.1)
# or equivalently
deciles.2 <- quantcut(x, seq(0, 1, by = 0.1))

# testonly{
stopifnot(identical(deciles.1, deciles.2))
# }

## show handling of 'tied' quantiles.
x <- round(x) # discretize to create ties
stem(x) # display the ties
deciles <- quantcut(x, 10)

table(deciles) # note that there are only 5 groups (not 10)
# due to duplicates
roman2int(roman)
roman2int(roman)

roman2int(c("I", "V", "X", "C", "L", "D", "M"))

# works regardless of case
roman2int("MMXVI")
roman2int("mmxvi")

# works beyond R's limit of 3899
val.3899 <- "MMMDCCCXCIX"
val.3900 <- "MMMCM"
val.4000 <- "MMMM"
as.numeric(as.roman(val.3899))
as.numeric(as.roman(val.3900))
as.numeric(as.roman(val.4000))

roman2int(val.3899)
roman2int(val.3900)
roman2int(val.4000)
roman2int(c("I", "V", "X", "C", "L", "D", "M"))

# works regardless of case
roman2int("MMXVI")
roman2int("mmxvi")

# works beyond R's limit of 3899
val.3899 <- "MMMDCCCXCIX"
val.3900 <- "MMMCM"
val.4000 <- "MMMM"
as.numeric(as.roman(val.3899))
as.numeric(as.roman(val.3900))
as.numeric(as.roman(val.4000))

roman2int(val.3899)
roman2int(val.3900)
roman2int(val.4000)
running(
  X,
  Y = NULL,
  fun = mean,
  width = min(length(X), 20),
  allow.fewer = FALSE,
  pad = FALSE,
  align = c("right", "center", "left"),
  simplify = TRUE,
  by,
  ...
)
running(
  X,
  Y = NULL,
  fun = mean,
  width = min(length(X), 20),
  allow.fewer = FALSE,
  pad = FALSE,
  align = c("right", "center", "left"),
  simplify = TRUE,
  by,
  ...
)

# show effect of pad
running(1:20, width = 5)
running(1:20, width = 5, pad = TRUE)

# show effect of align
running(1:20, width = 5, align = "left", pad = TRUE)
running(1:20, width = 5, align = "center", pad = TRUE)
running(1:20, width = 5, align = "right", pad = TRUE)

# show effect of simplify
running(1:20, width = 5, fun = function(x) x) # matrix
running(1:20, width = 5, fun = function(x) x, simplify = FALSE) # list

# show effect of by
running(1:20, width = 5) # normal
running(1:20, width = 5, by = 5) # non-overlapping
running(1:20, width = 5, by = 2) # starting every 2nd

# Use 'pad' to ensure correct length of vector, also show the effect
# of allow.fewer.
par(mfrow = c(2, 1))
plot(1:20, running(1:20, width = 5, allow.fewer = FALSE, pad = TRUE), type = "b")
plot(1:20, running(1:20, width = 5, allow.fewer = TRUE, pad = TRUE), type = "b")
par(mfrow = c(1, 1))

# plot running mean and central 2 standard deviation range
# estimated by *last* 40 observations
dat <- rnorm(500, sd = 1 + (1:500) / 500)
plot(dat)
sdfun <- function(x, sign = 1) mean(x) + sign * sqrt(var(x))
lines(running(dat, width = 51, pad = TRUE, fun = mean), col = "blue")
lines(running(dat, width = 51, pad = TRUE, fun = sdfun, sign = -1), col = "red")
lines(running(dat, width = 51, pad = TRUE, fun = sdfun, sign = 1), col = "red")

# plot running correlation estimated by last 40 observations (red)
# against the true local correlation (blue)
sd.Y <- seq(0, 1, length = 500)

X <- rnorm(500, sd = 1)
Y <- rnorm(500, sd = sd.Y)

plot(running(X, X + Y, width = 20, fun = cor, pad = TRUE), col = "red", type = "s")

r <- 1 / sqrt(1 + sd.Y^2) # true cor of (X,X+Y)
lines(r, type = "l", col = "blue")
# show effect of pad
running(1:20, width = 5)
running(1:20, width = 5, pad = TRUE)

# show effect of align
running(1:20, width = 5, align = "left", pad = TRUE)
running(1:20, width = 5, align = "center", pad = TRUE)
running(1:20, width = 5, align = "right", pad = TRUE)

# show effect of simplify
running(1:20, width = 5, fun = function(x) x) # matrix
running(1:20, width = 5, fun = function(x) x, simplify = FALSE) # list

# show effect of by
running(1:20, width = 5) # normal
running(1:20, width = 5, by = 5) # non-overlapping
running(1:20, width = 5, by = 2) # starting every 2nd

# Use 'pad' to ensure correct length of vector, also show the effect
# of allow.fewer.
par(mfrow = c(2, 1))
plot(1:20, running(1:20, width = 5, allow.fewer = FALSE, pad = TRUE), type = "b")
plot(1:20, running(1:20, width = 5, allow.fewer = TRUE, pad = TRUE), type = "b")
par(mfrow = c(1, 1))

# plot running mean and central 2 standard deviation range
# estimated by *last* 40 observations
dat <- rnorm(500, sd = 1 + (1:500) / 500)
plot(dat)
sdfun <- function(x, sign = 1) mean(x) + sign * sqrt(var(x))
lines(running(dat, width = 51, pad = TRUE, fun = mean), col = "blue")
lines(running(dat, width = 51, pad = TRUE, fun = sdfun, sign = -1), col = "red")
lines(running(dat, width = 51, pad = TRUE, fun = sdfun, sign = 1), col = "red")

# plot running correlation estimated by last 40 observations (red)
# against the true local correlation (blue)
sd.Y <- seq(0, 1, length = 500)

X <- rnorm(500, sd = 1)
Y <- rnorm(500, sd = sd.Y)

plot(running(X, X + Y, width = 20, fun = cor, pad = TRUE), col = "red", type = "s")

r <- 1 / sqrt(1 + sd.Y^2) # true cor of (X,X+Y)
lines(r, type = "l", col = "blue")
scat(...)
scat(...)

options(DEBUG = NULL) # makee sure DEBUG isn't set
scat("Not displayed")

options(DEBUG = TRUE)
scat("This will be displayed immediately (even in R BATCH output \n")
scat("files), provided options()$DEBUG is TRUE.")
options(DEBUG = NULL) # makee sure DEBUG isn't set
scat("Not displayed")

options(DEBUG = TRUE)
scat("This will be displayed immediately (even in R BATCH output \n")
scat("files), provided options()$DEBUG is TRUE.")
script_file(fail = c("stop", "warning", "quiet"))

script_path(fail = c("stop", "warning", "quiet"))
script_file(fail = c("stop", "warning", "quiet"))

script_path(fail = c("stop", "warning", "quiet"))

getwd()
commandArgs(trailingOnly = FALSE)

script_file("warning")
script_path("warning")
getwd()
commandArgs(trailingOnly = FALSE)

script_file("warning")
script_path("warning")
setTCPNoDelay(socket, value = TRUE)
setTCPNoDelay(socket, value = TRUE)
## Not run: 
host <- "www.r-project.org"
socket <- make.socket(host, 80)
print(socket)
setTCPNoDelay(socket, TRUE)

write.socket(socket, "GET /\n\n")
write.socket(socket, "A")
write.socket(socket, "B\n")
while ((str <- read.socket(socket)) > "") {
  cat(str)
}
close.socket(socket)

## End(Not run)

## Not run: 
host <- "www.r-project.org"
socket <- make.socket(host, 80)
print(socket)
setTCPNoDelay(socket, TRUE)

write.socket(socket, "GET /\n\n")
write.socket(socket, "A")
write.socket(socket, "B\n")
while ((str <- read.socket(socket)) > "") {
  cat(str)
}
close.socket(socket)

## End(Not run)
smartbind(..., list, fill = NA, sep = ":", verbose = FALSE)
smartbind(..., list, fill = NA, sep = ":", verbose = FALSE)

df1 <- data.frame(A = 1:10, B = LETTERS[1:10], C = rnorm(10))
df2 <- data.frame(A = 11:20, D = rnorm(10), E = letters[1:10])

# rbind would fail
## Not run: 
rbind(df1, df2)
# Error in match.names(clabs, names(xi)) : names do not match previous
# names:
# 	D, E

## End(Not run)
# but smartbind combines them, appropriately creating NA entries
smartbind(df1, df2)

# specify fill=0 to put 0 into the missing row entries
smartbind(df1, df2, fill = 0)

df1 <- data.frame(A = 1:10, B = LETTERS[1:10], C = rnorm(10))
df2 <- data.frame(A = 11:20, D = rnorm(10), E = letters[1:10])

# rbind would fail
## Not run: 
rbind(df1, df2)
# Error in match.names(clabs, names(xi)) : names do not match previous
# names:
# 	D, E

## End(Not run)
# but smartbind combines them, appropriately creating NA entries
smartbind(df1, df2)

# specify fill=0 to put 0 into the missing row entries
smartbind(df1, df2, fill = 0)
split_path(x, depth_first = TRUE)
split_path(x, depth_first = TRUE)
stars.pval(p.value)
stars.pval(p.value)

p.val <- c(0.0004, 0.0015, 0.013, 0.044, 0.067, 0.24)
stars.pval(p.val)
p.val <- c(0.0004, 0.0015, 0.013, 0.044, 0.067, 0.24)
stars.pval(p.val)
stat_mode(x, na.rm = TRUE, ties = c("all", "first", "last", "missing"), ...)
stat_mode(x, na.rm = TRUE, ties = c("all", "first", "last", "missing"), ...)

# Character vector
chr_vec <- c("a", "d", "d", "h", "h", NA, NA) # Multiple modes
stat_mode(x = chr_vec)
stat_mode(x = chr_vec, na.rm = FALSE)
stat_mode(x = chr_vec, na.rm = FALSE, ties = "first")
stat_mode(x = chr_vec, na.rm = FALSE, ties = "last")

# - # Numeric vector
# See that it keeps the original vector type
num_vec <- c(2, 3, 3, 4, 4, NA, NA)
stat_mode(x = num_vec)
stat_mode(x = num_vec, na.rm = FALSE)
stat_mode(x = num_vec, na.rm = FALSE, ties = "first")
stat_mode(x = num_vec, na.rm = FALSE, ties = "last")

# The default option is ties="all" but it is very easy for the user to control
# the ties without changing this parameter.
# Select always just one mode, being that the first mode
stat_mode(x = num_vec)[1]

# Select the first and the second stat_mode
stat_mode(x = num_vec)[c(1, 2)]

# Logical Vectors
stat_mode(x = c(TRUE, TRUE))
stat_mode(x = c(FALSE, FALSE, TRUE, TRUE))

# - # Single element cases
stat_mode(x = c(NA_real_))
stat_mode(x = 2)
stat_mode(x = NA)
stat_mode(x = c("a"))

# Not allowing multiple stat_mode, returning NA if that happens
stat_mode(x = c(1, 1, 2, 2), multiple_modes = FALSE) # multiple stat_mode
stat_mode(x = c(1, 1), multiple_modes = FALSE) # single mode

# Empty vector cases
# The ties of any empty vector will be itself (an empty vector of the same type)
stat_mode(x = double())
stat_mode(x = complex())
stat_mode(x = vector("numeric"))
stat_mode(x = vector("character"))
# Character vector
chr_vec <- c("a", "d", "d", "h", "h", NA, NA) # Multiple modes
stat_mode(x = chr_vec)
stat_mode(x = chr_vec, na.rm = FALSE)
stat_mode(x = chr_vec, na.rm = FALSE, ties = "first")
stat_mode(x = chr_vec, na.rm = FALSE, ties = "last")

# - # Numeric vector
# See that it keeps the original vector type
num_vec <- c(2, 3, 3, 4, 4, NA, NA)
stat_mode(x = num_vec)
stat_mode(x = num_vec, na.rm = FALSE)
stat_mode(x = num_vec, na.rm = FALSE, ties = "first")
stat_mode(x = num_vec, na.rm = FALSE, ties = "last")

# The default option is ties="all" but it is very easy for the user to control
# the ties without changing this parameter.
# Select always just one mode, being that the first mode
stat_mode(x = num_vec)[1]

# Select the first and the second stat_mode
stat_mode(x = num_vec)[c(1, 2)]

# Logical Vectors
stat_mode(x = c(TRUE, TRUE))
stat_mode(x = c(FALSE, FALSE, TRUE, TRUE))

# - # Single element cases
stat_mode(x = c(NA_real_))
stat_mode(x = 2)
stat_mode(x = NA)
stat_mode(x = c("a"))

# Not allowing multiple stat_mode, returning NA if that happens
stat_mode(x = c(1, 1, 2, 2), multiple_modes = FALSE) # multiple stat_mode
stat_mode(x = c(1, 1), multiple_modes = FALSE) # single mode

# Empty vector cases
# The ties of any empty vector will be itself (an empty vector of the same type)
stat_mode(x = double())
stat_mode(x = complex())
stat_mode(x = vector("numeric"))
stat_mode(x = vector("character"))
unByteCode(fun)

assignEdgewise(name, env, value)

unByteCodeAssign(fun)
unByteCode(fun)

assignEdgewise(name, env, value)

unByteCodeAssign(fun)

data(badDend)
dist2 <- function(x) as.dist(1 - cor(t(x), method = "pearson"))
hclust1 <- function(x) hclust(x, method = "single")

distance <- dist2(badDend)
cluster <- hclust1(distance)

dend <- as.dendrogram(cluster)
## Not run: 
## In R 2.3.0 and earlier crashes with a node stack overflow error
plot(dend)
## Error in xy.coords(x, y, recycle = TRUE) : node stack overflow

## End(Not run)

## convert stats:::plotNode from byte-code to interpreted-code
## (no longer available unless assignEdgewise is defined by the user)
## unByteCodeAssign(stats:::plotNode)
## illustrated in https://stackoverflow.com/questions/16559250/error-in-heatmap-2-gplots

# increase recursion limit
options("expressions" = 5e4)

# now the function does not crash
plot(dend)
data(badDend)
dist2 <- function(x) as.dist(1 - cor(t(x), method = "pearson"))
hclust1 <- function(x) hclust(x, method = "single")

distance <- dist2(badDend)
cluster <- hclust1(distance)

dend <- as.dendrogram(cluster)
## Not run: 
## In R 2.3.0 and earlier crashes with a node stack overflow error
plot(dend)
## Error in xy.coords(x, y, recycle = TRUE) : node stack overflow

## End(Not run)

## convert stats:::plotNode from byte-code to interpreted-code
## (no longer available unless assignEdgewise is defined by the user)
## unByteCodeAssign(stats:::plotNode)
## illustrated in https://stackoverflow.com/questions/16559250/error-in-heatmap-2-gplots

# increase recursion limit
options("expressions" = 5e4)

# now the function does not crash
plot(dend)

Title:	Various R Programming Tools
Description:	Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion', 'getDependencies', 'keywords', 'scat'), - calculate the logit and inverse logit transformations ('logit', 'inv.logit'), - test if a value is missing, empty or contains only NA and NULL values ('invalid'), - manipulate R's .Last function ('addLast'), - define macros ('defmacro'), - detect odd and even integers ('odd', 'even'), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'), - perform a binary search ('binsearch'), - sort strings containing both numeric and character components ('mixedsort'), - create a factor variable from the quantiles of a continuous variable ('quantcut'), - enumerate permutations and combinations ('combinations', 'permutation'), - calculate and convert between fold-change and log-ratio ('foldchange', 'logratio2foldchange', 'foldchange2logratio'), - calculate probabilities and generate random numbers from Dirichlet distributions ('rdirichlet', 'ddirichlet'), - apply a function over adjacent subsets of a vector ('running'), - modify the TCP_NODELAY ('de-Nagle') flag for socket objects, - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'), - generate significance stars from p-values ('stars.pval'), - convert characters to/from ASCII codes ('asc', 'chr'), - convert character vector to ASCII representation ('ASCIIfy'), - apply title capitalization rules to a character vector ('capwords').
Authors:	Gregory R. Warnes [aut], Ben Bolker [aut, cre] , Thomas Lumley [aut], Arni Magnusson [aut], Bill Venables [aut], Genei Ryodan [aut], Steffen Moeller [aut], Ian Wilson [ctb], Mark Davis [ctb], Nitin Jain [ctb], Scott Chamberlain [ctb]
Maintainer:	Ben Bolker <[email protected]>
License:	GPL-2
Version:	3.9.5
Built:	2024-11-11 05:47:32 UTC
Source:	https://github.com/r-gregmisc/gtools

`char`	vector of character strings
`simplify`	logical indicating whether to attempt to convert the result into a vector or matrix object. See `sapply` for details.
`ascii`	vector or list of vectors containing integer ASCII codes

`x`	a character vector, possibly containing non-ASCII characters.
`bytes`	either `1` or `2`, for single-byte (‘⁠\x00⁠’) or two-byte (‘⁠\u0000⁠’) codes.
`fallback`	an output character to use, when input characters cannot be converted.

`msg`	Character vector providing the message to be displayed
`con`	Character connection to query, defaults to `stdin()`.

`v`	A single integer value to be transformed.
`base`	The base to which to transform to.
`len`	The minimal length of the returned array.

`fun`	Monotonic function over which the search will be performed.
`range`	2-element vector giving the range for the search.
`...`	Additional parameters to the function `fun`.
`target`	Target value for `fun`. Defaults to 0.
`lower`	Lower limit of search range. Defaults to `min(range)`.
`upper`	Upper limit of search range. Defaults to `max(range)`.
`maxiter`	Maximum number of search iterations. Defaults to 100.
`showiter`	Boolean flag indicating whether the algorithm state should be printed at each iteration. Defaults to FALSE.

`call`	How the function was called.
`numiter`	The number of iterations performed
`flag`	One of the strings, "Found", "Between Elements", "Maximum number of iterations reached", "Reached lower boundary", or "Reached upper boundary."
`where`	One or two values indicating where the search terminated.
`value`	Value of the function `fun` at the values of `where`.

`s`	character string to be processed
`strict`	Logical, remove all additional capitalization.
`AP`	Logical, apply the Associated Press (AP) rules for prepositions and conjunctions that should not be capitalized in titles.
`onlyfirst`	Logical, only capitalize the first word.
`preserveMixed`	Logical, preserve the capitalization mixed-case words containing an upper-case letter after a lower-case letter.
`sep`	Character string, word separator

`n`	Size of the source vector
`r`	Size of the target vectors
`v`	Source vector. Defaults to `1:n`
`set`	Logical flag indicating whether duplicates should be removed from the source vector `v`. Defaults to `TRUE`.
`repeats.allowed`	Logical flag indicating whether the constructed vectors may include duplicated values. Defaults to `FALSE`.

`...`	macro argument list
`expr`	R expression defining the macro body
`strexpr`	character string defining the macro body

`x`	A vector containing a single random deviate or matrix containing one random deviate per row.
`alpha`	Vector or (for `ddirichlet`) matrix containing shape parameters.
`n`	Number of random vectors to generate.

`num`, `denom`	vector/matrix of numeric values
`logratio`	vector/matrix of log-ratio values
`base`	Exponential base for the log-ratio.
`foldchange`	vector/matrix of fold-change values

`pkgs`	character vector of package names
`dependencies`	character vector of dependency types to include. Choices are "Depends", "Imports", "LinkingTo", "Suggests", and "Enhances". Defaults to `c("Depends", "Imports", "LinkingTo")`.
`installed`	Logical indicating whether to pull dependency information from installed packages. Defaults to TRUE.
`available`	Logical indicating whether to pull dependency information from available packages. Defaults to TRUE.
`base`	Logical indicating whether to include dependencies on base packages that are included in the R installation. Defaults to FALSE.
`recommended`	Logical indicating whether to include dependencies on recommended packages that are included in the R installation. Defaults to FALSE.

`Package`	Package name
`Version`	Version string
`Path`	Path to package files
`SearchPath`	Either the index of the package namespace in the current search path, or '-' if the package namespace is not in the search path. '1' corresponds to the top of the search path (the first namespace searched for values).

`x`	value(s) to be transformed
`min`	Lower end of logit interval
`max`	Upper end of logit interval

`x`	Vector to be sorted.
`decreasing`	logical. Should the sort be increasing or decreasing? Note that `descending=TRUE` reverses the meanings of `na.last` and `blanks.last`.
`na.last`	for controlling the treatment of `NA` values. If `TRUE`, missing values in the data are put last; if `FALSE`, they are put first; if `NA`, they are removed.
`blank.last`	for controlling the treatment of blank values. If `TRUE`, blank values in the data are put last; if `FALSE`, they are put first; if `NA`, they are removed.
`numeric.type`	either "decimal" (default) or "roman". Are numeric values represented as decimal numbers (`numeric.type="decimal"`) or as Roman numerals (`numeric.type="roman"`)?
`roman.case`	one of "upper", "lower", or "both". Are roman numerals represented using only capital letters ('IX') or lower-case letters ('ix') or both?
`scientific`	logical. Should exponential notation be allowed for numeric values.

`x`	vector possibly containing missing (`NA`) values
`replace`	either a scalar replacement value, or a function returning a scalar value
`...`	Optional arguments to be passed to `replace`

`x`	Continuous variable.
`q`	Either a integer number of equally spaced quantile groups to create, or a vector of quantiles used for creating groups. Defaults to `q=4` which is equivalent to `q=seq(0, 1, by=0.25)`. See `quantile` for details.
`na.rm`	Boolean indicating whether missing values should be removed when computing quantiles. Defaults to TRUE.
`...`	Optional arguments passed to `cut`.

`X`	data vector
`Y`	data vector (optional)
`fun`	Function to apply. Default is `mean`
`width`	Integer giving the number of vector elements to include in the subsets. Defaults to the lesser of the length of the data and 20 elements.
`allow.fewer`	Boolean indicating whether the function should be computed for subsets with fewer than `width` points
`pad`	Boolean indicating whether the returned results should be 'padded' with NAs corresponding to sets with less than `width` elements. This only applies when when `allow.fewer` is FALSE.
`align`	One of "right", "center", or "left". This controls the relative location of ‘short’ subsets with less then `width` elements: "right" allows short subsets only at the beginning of the sequence so that all of the complete subsets are at the end of the sequence (i.e. 'right aligned'), "left" allows short subsets only at the end of the data so that the complete subsets are ‘left aligned’, and "center" allows short subsets at both ends of the data so that complete subsets are ‘centered’.
`simplify`	Boolean. If FALSE the returned object will be a list containing one element per evaluation. If TRUE, the returned object will be coerced into a vector (if the computation returns a scalar) or a matrix (if the computation returns multiple values). Defaults to FALSE.
`by`	Integer separation between groups. If `by=width` will give non-overlapping windows. Default is missing, in which case groups will start at each value in the X/Y range.
`...`	parameters to be passed to `fun`

`socket`	A socket connection object
`value`	Logical indicating whether to set (`TRUE`) or unset (`FALSE`) the flag

`...`	Data frames to combine
`list`	List containing data frames to combine
`fill`	Value to use when 'filling' missing columns. Defaults to `NA`.
`sep`	Character string used to separate column names when pasting them together.
`verbose`	Logical flag indicating whether to display processing messages. Defaults to `FALSE`.

`columns`	all columns present in any provided data frame
`rows`	a set of rows from each provided data frame, with values in columns not present in the given data frame filled with missing (`NA`) values.

`x`	character scalar. Path to be processed.
`depth_first`	logical. Should path be returned depth first? Defaults to `TRUE`.

`x`	vector of values
`na.rm`	logical. Should `NA` values be removed before processing?
`ties`	character. Which value(s) should be returned in the case of ties?
`...`	optional additional parameters.

`fun`	function to be modified
`name`	object name
`env`	namespace
`value`	new function body

Package 'gtools'

Help Index

Convert between characters and ASCII codes

Description

Usage

Arguments

Value

Functions

Author(s)

See Also

Examples

Convert Characters to ASCII

Description

Usage

Arguments

Value

Note

Author(s)

See Also

Examples

Display a prompt and collect the user's response

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Defunct Functions in package gtools

Description

Usage

Arguments

Details

See Also

Dataset That Crashes Base:::Plot.Dendogram with 'Node Stack Overflow'

Description

Format

Note

Examples

Transform an integer to an array of base-n digits

Description

Usage

Arguments

Details

Author(s)

Examples

Binary Search

Description

Usage

Arguments

Details

Value

Note

Author(s)

See Also

Examples

Capitalize Words for Titles

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Check if a newer version of R is available

Description

Usage

Arguments

Details

Value

Note

Author(s)

See Also

Examples

Enumerate the Combinations or Permutations of the Elements of a Vector

Description

Usage

Defunct Functions in package `gtools`