Package 'broman'

Description

%in% returns logical vector indicating values that do not have a match. ⁠%win%⁠ returns a vector of the values that have a match. ⁠%wnin%⁠ returns a vector of the values that do not have a match.

Usage

x %nin% table

x %win% table

x %wnin% table

Arguments

Value

⁠%nin%⁠ returns a logical vector of the same length of x, indicating which values are not in table.

⁠%win%⁠ returns a sub-vector of x with the values that were found in table.

⁠%wnin%⁠ returns a sub-vector of x with the values that were not found in table.

See Also

base::match()

Examples

vals <- c("a", "xa", "b")
vals %nin% letters
vals %win% letters
vals %wnin% letters

Description

Convert a number to a string, with commas every 3rd digit

Usage

add_commas(numbers)

Arguments

Value

Character string with numbers written like "7,547,085".

Examples

add_commas(c(231, 91310, 2123, 9911001020, 999723285))

Description

Align two vectors using their names attributes, either expanding with NAs or reducing to the common values.

Usage

align_vectors(x, y, expand = TRUE)

Arguments

Value

A list with two components, x and y

Description

Use the graphics::locator() function to indicate the endpoints of an arrow and then plot it.

Usage

arrowlocator(
  reverse = FALSE,
  horizontal = FALSE,
  vertical = FALSE,
  length = 0.1,
  ...
)

Arguments

Details

Use graphics::locator() to indicate the two endpoints of an arrow and then draw it.

Value

The locations of the endpoints of the arrow, as a two-row matrix. The first row indicates the location of the tail of the arrow; the second row indicates the location of the head of the arrow.

See Also

graphics::arrows(), graphics::locator()

Examples

## Not run: 
plot(0,0,type="n", xlab="", ylab="", xlim=c(0,100), ylim=c(0,100))
arrowlocator(col="blue", lwd=2)

## End(Not run)

Description

Get the names of the attributes of an object

Usage

attrnames(object)

Arguments

Details

It just does names(attributes(object)).

Value

Vector of character strings with the names of the attributes.

Examples

x <- matrix(1:100, ncol=5)
colnames(x) <- LETTERS[1:5]
attrnames(x)

Description

Creates different vectors of related colors that may be useful for figures.

Usage

brocolors(
  set = c("general", "general2", "bg", "bgpng", "CC", "CCalt", "f2", "sex", "main",
    "crayons", "web")
)

Arguments

Value

Vector of character strings representing the chosen set of colors, in RGB.

See Also

plot_crayons()

Examples

par(mar=c(0.6,5.1,0.6,0.6))
plot(0, 0, type="n", xlab="", ylab="", xlim=c(0, 9), ylim=c(8.5, 0), yaxs="i",
     xaxt="n", yaxt="n", xaxs="i")
axis(side=2, at=1:8, c("general", "general2", "bg", "bgpng", "CC", "f2", "sex", "main"), las=1)

gen <- brocolors("general")
points(seq(along=gen), rep(1,length(gen)), pch=21, bg=gen, cex=4)
text(seq(along=gen), rep(c(0.55, 0.7), length(gen))[seq(along=gen)], names(gen))

gen2 <- brocolors("general2")
points(seq(along=gen2), rep(2,length(gen2)), pch=21, bg=gen2, cex=4)
text(seq(along=gen2), rep(1+c(0.55, 0.7), length(gen2))[seq(along=gen2)], names(gen2))

points(1, 3, pch=21, bg=brocolors("bg"), cex=4)
points(1, 4, pch=21, bg=brocolors("bgpng"), cex=4)

CC <- brocolors("CC")
points(seq(along=CC), rep(5,length(CC)), pch=21, bg=CC, cex=4)
text(seq(along=CC), rep(4+c(0.55, 0.7), length(CC))[seq(along=CC)], names(CC))

f2 <- brocolors("f2")
points(seq(along=f2), rep(6,length(f2)), pch=21, bg=f2, cex=4)
text(seq(along=f2), rep(5.7, length(f2)), names(f2))

sex <- brocolors("sex")
points(seq(along=sex), rep(7,length(sex)), pch=21, bg=sex, cex=4)
text(seq(along=sex), rep(6.7, length(sex)), names(sex))

points(1, 8, pch=21, bg=brocolors("main"), cex=4)

Description

Print the version number of the currently installed version of R/broman.

Usage

bromanversion()

Value

A character string with the version number of the currently installed version of R/broman.

Examples

bromanversion()

Description

Check whether two objects are the same, including their patterns of NAs.

Usage

cf(a, b)

Arguments

Details

It's not very complicated: ((is.na(a) & is.na(b)) | (!is.na(a) & !is.na(b) & a == b))

Value

Boolean object with TRUE indicating an element is the same.

Examples

x <- c(5, 8, 9, NA, 3, NA)
y <- c(5, 2, 9, 4, NA, NA)
cf(x,y)

x <- matrix(rnorm(1000), ncol=20)
x[sample(seq(along=x), 100)] <- NA
all(cf(x,x))
dim(cf(x,x))

y <- x
y[4,8] <- NA
sum(!cf(x,y))
y[6,2] <- 18
sum(!cf(x,y))
y[6,5] <- 32
sum(!cf(x,y))

x <- as.data.frame(x)
y <- as.data.frame(y)
sum(!cf(x,y))

x <- as.list(x)
y <- as.list(y)
sapply(cf(x,y), function(a) sum(!a))

Description

Calculate a p-value for a chi-square test by Monte Carlo simulation.

Usage

chisq(tab, n.sim = 1000)

Arguments

Details

This is like the function stats::chisq.test(), but calculates an approximate P-value rather than refering to asymptotics. This will be better for large, sparse tables.

Value

A single number: the P-value testing independence of rows and columns in the table.

See Also

stats::chisq.test(), stats::fisher.test(), fisher()

Examples

TeaTasting <- matrix(c(3,1,1,3),nrow=2)
chisq(TeaTasting,1000)

Description

Uses grayplot() to plot a set of confidence intervals.

Usage

ciplot(
  est,
  se = NULL,
  lo = NULL,
  hi = NULL,
  SEmult = 2,
  labels = NULL,
  rotate = FALSE,
  ...
)

Arguments

Details

Calls grayplot() with special choices of graphics parameters, as in dotplot().

Provide either se or both lo and hi. In the case that se is used, the intervals will be est +/- SEmult * se.

If labels is not provided, group names are taken from the names(est). If that is also missing, we use capital letters.

You can control the CI line widths with ci_lwd and the color of the CI segments with ci_col. You can control the width of the segments at the top and bottom with ci_endseg.

Value

None.

See Also

grayplot(), dotplot()

Examples

x <- rnorm(40, c(1,3))
g <- rep(c("A", "B"), 20)
me <- tapply(x, g, mean)
se <- tapply(x, g, function(a) sd(a)/sqrt(sum(!is.na(a))))
ciplot(me, se) # default is +/- 2 SE
ciplot(me, se, SEmult=1)
ciplot(me, se, rotate=TRUE)
lo <- me - 2*se
hi <- me + 2*se
ciplot(me, lo=lo, hi=hi)

Description

Convert a color to RGB and then to RGB with alpha transparency

Usage

colwalpha(color, alpha = 1)

Arguments

Value

A character string representing a color

Examples

colwalpha(c("blue", "red"), 0.5)

Description

For all pairs of rows in a matrix, calculate the proportion of mismatches or the RMS difference.

Usage

compare_rows(mat, method = c("prop_mismatches", "rms_difference"))

Arguments

Value

A square matrix of dimension nrow(mat) with NAs on the diagonal and the calculated statistic in the body.

Examples

n <- 10
p <- 200
x <- matrix(sample(1:4, n*p, replace=TRUE), ncol=p)
d <- compare_rows(x)

Description

Convert a number to hexidecimal notation.

Usage

convert2hex(d)

Arguments

Value

The input in hex, as character strings.

See Also

hex2dec()

Examples

convert2hex(333)
dec2hex(333)
dec2hex(0:30)

Description

Vector of colors corresponding to Crayola crayons

Usage

crayons(color_names = NULL, ...)

Arguments

Value

Vector of named RGB colors

References

https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors

See Also

plot_crayons(), brocolors()

Description

Like the grayplot() function, but with one axis assumed to be categorical.

Usage

dotplot(group, y, jiggle = NULL, max_jiggle = 0.45, rotate = FALSE, ...)

Arguments

Details

Calls grayplot() with special choices of graphics parameters for the case of categorical x.

If group is a factor, the order of the groups is as in the levels. Otherwise, we take sort(unique(group)). So if you want to control the order of the levels, make group a factor with the levels in the desired order, for example group <- factor(group, levels=unique(group)).

Value

None.

See Also

grayplot(), timeplot()

Examples

x <- rnorm(40, c(1,3))
g <- rep(c("A", "B"), 20)
dotplot(g, x)
dotplot(g, x, "fixed")
dotplot(g, x, runif(length(g), -0.25, 0.25))

Description

Turn a matrix of data into an SVG of how it might look in Excel

Usage

excel_fig(
  mat,
  file = NULL,
  cellwidth = 80,
  cellheight = 26,
  textsize = 16,
  fig_width = NULL,
  fig_height = NULL,
  border = "#CECECE",
  headcol = "#E9E9E9",
  headborder = "#969696",
  headtextcol = "#626262",
  textcol = "black",
  row_names = FALSE,
  col_names = TRUE,
  hilitcells = NULL,
  hilitcolor = "#F0DCDB",
  lwd = 1,
  direct2svg = FALSE,
  mar = rep(0.1, 4)
)

Arguments

Examples

df <- data.frame(id=    c(101,  102,  103),
                 sex=   c("M",  "F",  "M"),
                 weight=c(22.3, 15.8, 19.7),
                 stringsAsFactors=FALSE)
excel_fig(df, col_names=TRUE)

Description

exit R without saving workspace.

Usage

exit()

Details

This just calls q("no")

Value

None.

Description

Convert a factor with numeric levels to a non-factor

Usage

fac2num(x)

Arguments

Value

The input factor made a numeric vector

Examples

x <- factor(c(3, 4, 9, 4, 9), levels=c(3,4,9))
fac2num(x)

Description

Performs a sampling version of Fisher's exact test for a two-way contingency table.

Usage

fisher(tab, n.sim = 1000)

Arguments

Details

This is like the function stats::fisher.test(), but calculates an approximate P-value rather than performing a complete enumeration. This will be better for large, sparse tables.

Value

A single number: the P-value testing independence of rows and columns in the table.

See Also

stats::chisq.test(), stats::fisher.test(), chisq()

Examples

TeaTasting <- matrix(c(3,1,1,3),nrow=2)
fisher(TeaTasting,1000)

Description

Determine the precision of a number, as the number of digits past the decimal point.

Usage

get_precision(x, ...)

Arguments

Details

If the number is expressed in scientific notation, we take the number of digits

Value

A vector of integers, with the number of digits (to the last non-zero digit) past the decimal point.

Description

Like the plot function, but using a gray background just for the plot region.

Usage

grayplot(
  x,
  y = NULL,
  ...,
  type = "p",
  hlines = NULL,
  hlines.col = "white",
  hlines.lty = 1,
  hlines.lwd = 1,
  vlines = NULL,
  vlines.col = "white",
  vlines.lty = 1,
  vlines.lwd = 1,
  xat = NULL,
  yat = NULL,
  bgcolor = "gray90",
  pch = 21,
  bg = "lightblue",
  col = "black",
  v_over_h = FALSE
)

Arguments

Details

Calls plot() with type="n", then graphics::rect() to get the background, and then graphics::points(). Additional arguments you can include: mgp.x and mgp.y (like mgp, for controlling parameters of axis labels, but separate for x- and y-axis).

Value

None.

See Also

dotplot(), timeplot(), graphics::par(), graphics::rect(), graphics::points()

Examples

x <- rnorm(100)
y <- x+rnorm(100, 0, 0.7)
grayplot(x, y, col="slateblue", pch=16)
at <- seq(-3, 3)
grayplot(x, y, col="violetred", pch=16, hlines=at, vlines=at)
grayplot(x, col="Orchid", pch=16, bgcolor="gray80",
         hlines=seq(-4, 4, by=0.5), hlines.lwd=c(3,1),
         vlines=seq(0, 100, by=5), vlines.lwd=c(3,1,1,1))

Description

Scatterplot with a gray background and with points with missing values shown in separate panels near the margins.

Usage

grayplot_na(
  x,
  y = NULL,
  type = "p",
  bgcolor = "gray90",
  v_over_h = FALSE,
  pch = 21,
  bg = "lightblue",
  col = "black",
  force = c("none", "x", "y", "both"),
  ...
)

Arguments

Details

Calls plot() with 'type="n", then graphics::rect() to get the background, and then graphics::points().

There are a bunch of hidden graphical arguments you can include: na.width controls the proportional width devoted to the NA boxes, and na.gap the proportion for the gap between the NA boxes and the main plot region. mgp.x and mgp.y (like mgp, for controlling parameters of axis labels, but separate for x- and y-axis). Also hlines to indicate locations of of horizontal gridlines, and hlines.col, hlines.lwd, and hlines.lty to set their color, width, and type. hlines=NA suppresses the grid lines. Similarly vlines, vlines.col, vlines.lwd, and vlines.lty. xat and yat are for specifying the locations of x- and y-axis labels, respectively. xat=NA and yat=NA indicate no labels.

Value

None.

See Also

grayplot(), dotplot()

Examples

n <- 100
x <- rnorm(n)
y <- x+rnorm(n, 0, 0.7)
x[sample(n, 10)] <- NA

grayplot_na(x, y)

grayplot_na(x, y, force="y")

y[sample(n, 10)] <- NA
grayplot_na(x, y)

Description

View the html version of a help file while running R via ESS within emacs.

Usage

h(...)

Arguments

Details

This just calls the function utils::help() using the argument htmlhelp=TRUE.

Value

No return value.

See Also

utils::help(), utils::help.start()

Examples

h(read.cross)

Description

Convert a number from hexidecimal to decimal notation.

Usage

hex2dec(h)

Arguments

Value

The input converted from hexadecimal to decimal notation.

Author(s)

Karl W Broman, [email protected]

See Also

dec2hex()

Examples

hex2dec("14D")
hex2dec(0:30)

Description

Utility function to plot histogram with graphics::lines().

Usage

histlines(x, y = NULL, breaks, use = c("counts", "density"))

Arguments

Details

If x and y are both provided, x is interpreted to be the breaks for a histogram, and y is a vector of counts or density values for each interval. These are then revised so that they may be plotted with graphics::lines(). If y is NULL, x is taken to be the data. In this case graphics::hist() is called with breaks=breaks, and either the counts or density are used as y.

Value

A data.frame with two columns: x and y.

See Also

graphics::hist(), graphics::lines()

Examples

x <- rnorm(1000, mean=20, sd=5)
# basic use
out <- hist(x, breaks=60, plot=FALSE)
plot(histlines(out$breaks, out$counts),
     type="l", lwd=2, xlab="x", ylab="counts", las=1)
# alternative use
plot(histlines(x, breaks=60, use="density"),
     type="l", lwd=2, xlab="x", ylab="Density", las=1)
# comparing two distributions
z <- rnorm(1000, mean=25, sd=5)
br <- seq(min(c(x,z)), max(c(x,z)), len=50)
xlines <- histlines(x, breaks=br, use="density")
zlines <- histlines(z, breaks=br, use="density")
ymx <- max(c(xlines$y, zlines$y))*1.05
plot(xlines, ylim=c(0, ymx), yaxs="i", xaxs="i",
     type="l", lwd=2, xlab="x", ylab="Density", las=1,
     col="blue")
lines(zlines, lwd=2 , col="red")

Description

Spread points out horizontally so that, in dot plot of quantitative response in multiple categories, the separate points can be seen.

Usage

jiggle(
  group,
  y,
  method = c("random", "fixed"),
  hnum = 35,
  vnum = 40,
  maxvalue = 0.45
)

Arguments

Details

The "random" method is similar to base::jitter() but with amount of jiggling proportional to the number of nearby points. The "fixed" method is similar to the beeswarm package

Value

Numeric vector with amounts to jiggle the points horizontally

See Also

base::jitter(), dotplot()

Description

Sys.Date as a string, in a few different formats

Usage

kbdate(format = c("dateonly", "standard"), date = Sys.time())

Arguments

Value

A character string representation of the date/time

See Also

base::Sys.time(), base::date()

Examples

kbdate()
kbdate("standard")

Description

Get the number of unique values in a vector

Usage

lenuniq(vec, na.rm = TRUE)

Arguments

Details

It just does length(unique(vec)) or, if na.rm=TRUE (the default) length(unique(vec[!is.na(vec)]))

Value

Number of unique values.

Examples

x <- c(1, 2, 1, 3, 1, 1, 2, 2, 3, NA, NA, 1)
lenuniq(x)
lenuniq(x, na.rm=FALSE)

Description

Run make within a package directory

Usage

make(pkg = ".", makefile = "Makefile", target = "", quiet = FALSE)

Arguments

Value

Exit value from base::system() with intern=FALSE

See Also

devtools::load_all()

Examples

## Not run: make() # run make within working directory
make("/path/to/mypackage") # run make within /path/to/mypackage

## End(Not run)

Description

Boxplot-like figure for many groups, with lines connecting selected quantiles.

Usage

manyboxplot(
  x,
  probs = c(0.05, 0.1, 0.25),
  dotcol = "blue",
  linecol = c("black", "red", "green", "orange"),
  ...
)

Arguments

Details

Calculates quantiles of the columns of x and then plots dots or lines at median plus lines at a series of quantiles, using grayplot() for the actual plot.

Value

None.

See Also

grayplot()

Examples

mu <- c(rnorm(50, 0, 0.3), rnorm(50, 2, 0.3)) # vector of means
x <- t(matrix(rnorm(1000*100, mu), ncol=1000))
manyboxplot(x, c(0.05, 0.25), ylim=range(x),
           dotcol=c("blue","green")[(1:100 > 50) + 1],
           hlines=seq(-4, 6, by=2),
           vlines=c(1, seq(20, 100, by=20)))

Description

Take the maximum of the absolute values of the input

Usage

maxabs(x, na.rm = FALSE)

Arguments

Value

The maximum of the absolute value of the input

Examples

x <- c(5, -2, 8, -20, 2.3)
maxabs(x)

Description

A matrix of scatterplots is produced; it's similar to graphics::pairs(), but with only the upper triangle is made.

Usage

mypairs(x, ...)

Arguments

Details

This is like the function graphics::pairs(), but only the upper triangle is produced.

Value

None.

See Also

graphics::pairs()

Examples

v <- rbind(c(1,0.5,0.2),c(0.5,1,0.9),c(0.2,0.9,1))
x <- rmvn(500, rep(5,3), v)
mypairs(x, col=sample(c("blue","red"), 500, repl=TRUE))

Description

Round a number, preserving extra 0's.

Usage

myround(x, digits = 1)

Arguments

Details

Uses base::sprintf() to round a number, keeping extra 0's.

Value

A vector of character strings.

See Also

base::round(), base::sprintf()

Examples

myround(51.01, 3)
myround(0.199, 2)

Description

Quantile normalizes two vectors or a matrix.

Usage

normalize(x, y = NULL)

Arguments

Details

We sort the columns, take averages across rows, and then plug the averages back into the respective positions. The marginal distributions in the columns are thus forced to be the same. Missing values, which can result in differing numbers of observed values per column, are dealt with by linear interpolation.

Value

If two vectors, x and y, are provided, the output is a matrix with two columns, with the quantile normalized versions of x and y. If y is missing, x should be a matrix, in which case the output is a matrix of the same dimensions with the columns quantile normalized with respect to each other.

Examples

z <- rmvn(10000, mu=c(0,5,10), V = rbind(c(1,0.5,0.5),c(0.5,1,0.5),c(0.5,0.5,1)))
z[sample(prod(dim(z)), 1500)] <- NA
pairs(z)
br <- seq(min(z, na.rm=TRUE), max(z, na.rm=TRUE), length=200)
par(mfrow=c(3,1))
for(i in 1:3)
  hist(z[,i], xlab="z", main=i, breaks=br)
zn <- normalize(z)
br <- seq(min(zn, na.rm=TRUE), max(zn, na.rm=TRUE), length=200)
for(i in 1:3)
  hist(zn[,i], xlab="normalized z", main=i, breaks=br)
pairs(zn)

Description

The numbers 1-20 spelled out in English, for use in reports.

Format

A vector of character strings

Details

• numbers - lower case

• Numbers - Capitalized

Examples

numbers[5]
Numbers[5]

Description

Calculate the sizes of all of the objects in one's workspace.

Usage

objectsizes(obj = NULL, sortbysize = TRUE)

Arguments

Details

Calls utils::object.size() repeated to get the size of a list of objects.

Value

A data frame with the only column being the size of each object in megabytes (MB). The row names are the names of the objects.

See Also

utils::object.size(), base::objects()

Examples

print(output <- objectsizes())
## Not run: sum(output)

Description

Open a file using [base::system() and "open" (well, actually "start" on Linux).

Usage

openfile(file)

Arguments

Details

I'd thought that to open a file you'd use open in MacOS and start in Windows, but system("start myfile.pdf") doesn't work in Windows, and rather system("open myfile.pdf") does, so here we're just using open, except on Linux where at least on my system, you can use "start".

Value

None.

Examples

## Not run: openfile("myplot.pdf")

Description

Calculates a p-value for a paired t-test via permutations.

Usage

paired.perm.test(d, n.perm = NULL, pval = TRUE)

Arguments

Details

This calls the function stats::t.test() to calculate a t-statistic comparing the mean of d to 0. Permutations are perfomed to give an exact or approximate conditional p-value.

Value

If pval=TRUE, the output is a single number: the P-value testing for the symmetry about 0 of the distribution of the population from which d was drawn. If pval=FALSE, the output is a vector of the t statistics from the permutations. An attributed "tobs" contains the t statistic with the observed data.

See Also

stats::t.test(), perm.test()

Examples

x <- c(43.3, 57.1, 35.0, 50.0, 38.2, 31.2)
y <- c(51.9, 95.1, 90.0, 49.7, 101.5, 74.1)
paired.perm.test(x-y)

Description

Calls base::paste() with sep=".".

Usage

paste.(...)

Arguments

Details

There's not much to this function. It just is base::paste() with sep="", 'cause I'm lazy.

Value

A character string or vector of character strings.

See Also

base::paste(), base::paste0(), paste00(), paste..(), paste0.(), paste.0()

Examples

x <- 3
y <- 4
paste.(x, y)

Description

Call base::paste() with sep="." or sep="" and collapse="" or collapse=".".

Usage

paste00(...)

Arguments

Details

There's not much to these functions. paste00(...) is like paste(..., sep="", collapse="") paste..(...) is like paste(..., sep=".", collapse=".") paste0.(...) is like paste(..., sep="", collapse=".") paste.0(...) is like paste(..., sep=".", collapse="")

Value

A character string or vector of character strings.

See Also

base::paste(), base::paste0(), paste.()

Examples

x <- c(3, 4)
y <- c(5, 6)
paste00(x, y)
paste..(x, y)
paste0.(x, y)
paste.0(x, y)

Description

Calculates a p-value for a t-test via permutations.

Usage

perm.test(x, y, n.perm = NULL, var.equal = TRUE, pval = TRUE)

Arguments

Details

This calls the function stats::t.test() to calculate a t-statistic comparing the vectors x and y. Permutations are perfomed to give an exact or approximate conditional p-value.

Value

If pval=TRUE, the output is a single number: the P-value testing for a difference in the distributions of the populations from which x and y were drawn. If pval=FALSE, the output is a vector of the t statistics from the permutations. An attributed "tobs" contains the t statistic with the observed data.

See Also

stats::t.test(), paired.perm.test()

Examples

x <- c(43.3, 57.1, 35.0, 50.0, 38.2, 61.2)
y <- c(51.9, 95.1, 90.0, 49.7, 101.5, 74.1)
perm.test(x,y)

Description

Align two vectors of numbers by their names and then pick a single value from each, favoring the more precise one. If the two values differ by more than round-off error, treat the value as missing.

Usage

pick_more_precise(x, y, tol = 0.000001)

Arguments

Details

Okay, this is a bit weird. But suppose you have two columns of numbers that have been subjected to different quirky rounding patterns. We align the vectors using their names and then for each element we pick between the two choices, favoring the more-precise one. If one is missing, choose the non-missing value. If the two differ by more than the round-off error, treat it as missing.

Value

A vector of combined values

Description

Creates a plot of the crayon colors in brocolors()

Usage

plot_crayons(
  method2order = c("hsv", "cluster"),
  cex = 0.6,
  mar = rep(0.1, 4),
  bg = "white",
  fg = "black",
  border = FALSE
)

Arguments

Value

None

References

https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors

See Also

brocolors()

Examples

plot_crayons()

Description

Adds a line to a quantile-quantile plot for two datasets, from stats::qqplot(). (The available stats::qqline() function works mainly for stats::qqnorm(), with one sample being theoretical quantiles.)

Usage

qqline2(x, y, probs = c(0.25, 0.75), qtype = 7, ...)

Arguments

Value

Intercept and slope of the line.

See Also

stats::qqline(), stats::qqplot()

Examples

x <- rchisq(500, 3)
y <- rgamma(730, 3, 1/2)
qqplot(x, y)
qqline2(x, y)

Description

Computes the QR decomposition of a matrix.

Usage

qr2(x, tol = 0.0000001)

Arguments

Details

Calls the function base::qr() and returns less compact but more understandable output.

Value

A list of two matrices: Q and R.

See Also

base::qr()

Examples

hilbert <- function(n) { i <- 1:n; 1/outer(i-1,i,"+") }
h5 <- hilbert(5);
qr2(h5)

Description

Calculate sample quantiles and their estimated standard errors.

Usage

quantileSE(x, p = 0.95, bw = NULL, na.rm = TRUE, names = TRUE)

Arguments

Details

The sample quantiles are calculated with the function stats::quantile(). Standard errors are obtained by the asymptotic approximation described in Cox and Hinkley (1974). Density values are estimated using a kernel density estimate with the function stats::density().

Value

A matrix of size 2 x length(p). The first row contains the estimated quantiles; the second row contains the corresponding estimated standard errors.

See Also

stats::quantile(), stats::density()

Examples

quantileSE(rchisq(1000,4), c(0.9,0.95))

Description

Calls grDevices::gray() then base::rev()

Usage

revgray(n = 256, ...)

Arguments

Details

There's not much to this. It's just ⁠gray((n:0)/n))⁠

Value

Vector of colors, from white to black

See Also

grDevices::gray()

Examples

x <- matrix(rnorm(100), ncol=10)
image(x, col=revgray())

Description

Calls grDevices::rainbow() then base::rev()

Usage

revrainbow(n = 256, ...)

Arguments

Details

There's not much to this. It's just rev(rainbow(start=0, end=2/3, ...)).

Value

Vector of colors, from blue to red.

See Also

base::rev(), grDevices::rainbow()

Examples

x <- matrix(rnorm(100), ncol=10)
image(x, col=revrainbow())

Description

Simulate from a multivariate normal distribution.

Usage

rmvn(n, mu = 0, V = matrix(1))

Arguments

Details

Uses the Cholesky decomposition of the matrix V, obtained by base::chol().

Value

A matrix of size n x length(mu). Each row corresponds to a separate replicate.

See Also

stats::rnorm()

Examples

x <- rmvn(100, c(1,2),matrix(c(1,1,1,4),ncol=2))

Description

Calculates a running mean, sum or median with a specified window.

Usage

runningmean(
  pos,
  value,
  at = NULL,
  window = 1000,
  what = c("mean", "sum", "median", "sd")
)

Arguments

Value

A vector with the same length as the input at (or pos, if at is NULL), containing the running statistic.

Author(s)

Karl W Broman [email protected]

See Also

runningratio()

Examples

x <- 1:10000
y <- rnorm(length(x))
plot(x,y, xaxs="i", yaxs="i")
lines(x, runningmean(x, y, window=100, what="mean"),
      col="blue", lwd=2)
lines(x, runningmean(x, y, window=100, what="median"),
      col="red", lwd=2)
lines(x, runningmean(x, y, window=100, what="sd"),
      col="green", lwd=2)

Description

Calculates a running ratio; a ratio sum(top)/sum(bottom) in a sliding window.

Usage

runningratio(pos, numerator, denominator, at = NULL, window = 1000)

Arguments

Value

A vector with the same length as the input at (or pos, if at is NULL), containing the running ratio.

Author(s)

Karl W Broman [email protected]

See Also

runningmean()

Examples

x <- 1:1000
y <- runif(1000, 1, 5)
z <- runif(1000, 1, 5)
plot(x, runningratio(x, y, z, window=5), type="l", lwd=2)
lines(x, runningratio(x, y, z, window=50), lwd=2, col="blue")
lines(x, runningratio(x, y, z, window=100), lwd=2, col="red")

Description

Set random number generation to L'Ecuyer-CMRG, for use in parallel calculations.

Usage

setRNGparallel()

unsetRNGparallel()

Details

I can never remember the command RNGkind("L'Ecuyer-CMRG"); this is a shortcut. unsetRNG4parallel sets the random number generator back to the default type.

Examples

RNGkind()
setRNGparallel()
RNGkind()
unsetRNGparallel()
RNGkind()

Description

Perform numerical integration by Simpson's rule or the trapezoidal rule.

Usage

simp(f, a, b, tol = 0.00000001, max.step = 1000, ...)

Arguments

Details

Iterately doubles the number of grid points for the numerical integral, stopping when the integral decreases by less than tol.

Value

The integral of f from a to b.

See Also

stats::integrate()

Examples

f <- function(x) x*x*(1-x)*sin(x*x)
I1 <- trap(f,0,2)
I2 <- simp(f,0,2)

Description

Spell out an integer as a word, for use in reports/papers.

Usage

spell_out(number, capitalize = FALSE, max_value = 9)

Arguments

Value

Character string (or vector of character strings) with numbers spelled out, or as numerals if large.

Examples

spell_out(9)
spell_out(9, cap=TRUE)
spell_out(9, max_value=5)

Description

Convert stringwidth units to number of (margin) lines

Usage

strwidth2lines(s, ...)

Arguments

Value

Maximum string width in units of margin lines

Author(s)

Aimee Teo Broman

Examples

p <- par(no.readonly = TRUE)
string <- sapply(sample(1:20,15,replace=TRUE),
         function(a) paste(LETTERS[1:a], collapse=""))
nlines <- strwidth2lines(string)
mar <- par("mar")
par(mar=c(mar[1],nlines+0.1,mar[3:4]))
  plot(1:length(string),1:length(string),yaxt="n", ylab="")
  axis(side=2, at=seq_along(string), lab=string, las=1)
par(p)
nlines <- strwidth2lines(string,cex=1.5)
par(mar=c(mar[1:3],nlines+0.1))
  plot(1:length(string),1:length(string),ylab="")
  mgp <- par("mgp")
  axis(side = 4, at=seq_along(string),
    labels = string ,las=1, hadj=1,
       mgp=c(mgp[1],nlines,mgp[3]),cex.axis=1.5)
par(p)

Description

Calculates the x-axis limits when adding (long) labels to a plot

Usage

strwidth2xlim(x, xstring, pos = 4, offset = 0.5, ...)

Arguments

Details

See text for details on pos and offset.

Value

Minimum and maximum x-axis limits for adding horizontal text

Author(s)

Aimee Teo Broman

See Also

graphics::text()

Examples

x <- runif(15,-1,1)*10
xlabs <- sapply(sample(1:20,15,replace=TRUE),
         function(a) paste(LETTERS[1:a], collapse=""))
## Labels to the left ##
xlims <- strwidth2xlim(x,xlabs,pos=2)
plot(x,1:length(x),xlim=xlims)
text(x,1:length(x),xlabs,pos=2)
## Labels to the right ##
xlims <- strwidth2xlim(x,xlabs,pos=4,cex=0.7)
plot(x,1:length(x),xlim=xlims)
text(x,1:length(x),xlabs,pos=4,cex=0.7)

Description

Vectorized version of base::switch(): just loops over input and calls base::switch().

Usage

switchv(EXPR, ...)

Arguments

Value

Vector of returned values.

Examples

switchv(c("horse", "fish", "cat", "bug"),
        horse="fast",
        cat="cute",
        "what?")

Description

Karl's ggplot2 theme: black border and no ticks

Usage

theme_karl(base_size = 12, base_family = "", ...)

karl_theme(base_size = 12, base_family = "", ...)

Arguments

Value

An object as returned by ggplot2::theme()

See Also

ggplot2::theme()

Examples

library(ggplot2)
mtcars$cyl <- factor(mtcars$cyl)
ggplot(mtcars, aes(y=mpg, x=disp, color=cyl)) +
    geom_point() + theme_karl()

Description

Set up a time-based axis for base graphics

Usage

time_axis(times, n = 8, scale = NULL, format = NULL)

Arguments

Value

A data frame with the numeric values to plot plus labels to use.

See Also

timeplot()

Examples

n <- 100
y <- rnorm(n)

# labels as days
x <- seq(as.POSIXct("2024-05-01 11:23"), as.POSIXct("2024-05-07 14:50"), length.out=n)
xax <- time_axis(x)
grayplot(x, y, xat=NA, vlines=xax$x)
axis(side=1, at=xax$x, labels=xax$label, mgp=c(2.1, 0.5, 0), tick=FALSE)

# labels as HH:MM
x <- seq(as.POSIXct("2024-05-01 11:23"), as.POSIXct("2024-05-01 14:50"), length.out=n)
xax <- time_axis(x)
grayplot(x, y, xat=NA, vlines=xax$x)
axis(side=1, at=xax$x, labels=xax$label, mgp=c(2.1, 0.5, 0), tick=FALSE)

# labels as seconds
x <- seq(as.POSIXct("2024-05-01 11:23:05.3"), as.POSIXct("2024-05-01 11:23:55.7"), length.out=n)
xax <- time_axis(x)
grayplot(x, y, xat=NA, vlines=xax$x)
axis(side=1, at=xax$x, labels=xax$label, mgp=c(2.1, 0.5, 0), tick=FALSE)

# custom time format
xax <- time_axis(x, format="%H:%M:%S")
grayplot(x, y, xat=NA, vlines=xax$x)
axis(side=1, at=xax$x, labels=xax$label, mgp=c(2.1, 0.5, 0), tick=FALSE)

Description

Like the grayplot() function, but with the x-axis having date/times

Usage

timeplot(x, y, ..., n = 5, scale = NULL, format = NULL)

Arguments

Value

None.

See Also

time_axis(), grayplot(), dotplot()

Examples

n <- 100
y <- rnorm(n)
x <- seq(as.POSIXct("2024-05-01 11:23"), as.POSIXct("2024-05-01 14:50"), length.out=n)
timeplot(x, y)

Description

Plot an arrow within a Holmans triangle (an equilateral triangle used to depict trinomial distributions).

Usage

triarrow(x, ...)

Arguments

Details

Plot of an equilateral triangle, in order to depict trinomial distributions. A trinomial distribution (that is, a trio of non-negative numbers that add to 1) is equated to a point in the triangle through the distances to the three sides. This makes use of the fact that for any point in an equilateral triangle, the sum of the distances to the three sides is constant. First use triplot() to first plot the equilateral triangle.

Value

The (x,y) coordinates of the endpoints of the arrows plotted.

See Also

triplot(), tripoints(), trilines(), tritext()

Examples

triplot()
x <- cbind(c(0.9, 0.05, 0.05), c(0.8, 0.1, 0.1), c(0.1, 0.9, 0), c(0, 0.9, 0.1))
tripoints(x, lwd=2, col=c("black","blue","red","green"), pch=16)
trilines(x, lwd=2, col="orange")
y <- cbind(c(0.05, 0.05, 0.9), c(0.25, 0.25, 0.5))
triarrow(y, col="blue", lwd=2, len=0.1)

Description

Add grid lines to a ternary plot with triplot()

Usage

trigrid(
  n = 1,
  col = "white",
  lty = 1,
  lwd = 1,
  outer_col = "black",
  outer_lwd = 2,
  ...
)

Arguments

See Also

triplot(), trilines()

Examples

triplot(c("A","H","B"), gridlines=1, grid_lwd=2)
trigrid(3, lty=2, lwd=2)

Description

Plot lines within a Holmans triangle (an equilateral triangle used to depict trinomial distributions).

Usage

trilines(x, ...)

Arguments

Details

Plot of an equilateral triangle, in order to depict trinomial distributions. A trinomial distribution (that is, a trio of non-negative numbers that add to 1) is equated to a point in the triangle through the distances to the three sides. This makes use of the fact that for any point in an equilateral triangle, the sum of the distances to the three sides is constant. First use triplot() to first plot the equilateral triangle.

Value

The (x,y) coordinates of the endpoints of the lines plotted.

See Also

triplot(), tripoints(), triarrow(), tritext()

Examples

triplot()
x <- cbind(c(0.9, 0.05, 0.05), c(0.8, 0.1, 0.1), c(0.1, 0.9, 0), c(0, 0.9, 0.1))
tripoints(x, lwd=2, col=c("black","blue","red","green"), pch=16)
trilines(x, lwd=2, col="orange")
y <- cbind(c(0.05, 0.05, 0.9), c(0.25, 0.25, 0.5))
triarrow(y, col="blue", lwd=2, len=0.1)

Description

Plot Holmans triangle (an equilateral triangle used to depict trinomial distributions).

Usage

triplot(
  labels = c("(1,0,0)", "(0,1,0)", "(0,0,1)"),
  col = "black",
  lwd = 2,
  bgcolor = "gray90",
  gridlines = 0,
  grid_col = "white",
  grid_lty = 1,
  grid_lwd = 1,
  ...
)

Arguments

Details

Plot of an equilateral triangle, in order to depict trinomial distributions. A trinomial distribution (that is, a trio of non-negative numbers that add to 1) is equated to a point in the triangle through the distances to the three sides. This makes use of the fact that for any point in an equilateral triangle, the sum of the distances to the three sides is constant. The triplot function creates an empty triangle for use with the related functions tripoints(), trilines(), triarrow().

Value

The (x,y) coordinates of the points plotted, if any.

See Also

tripoints(), trilines(), triarrow(), tritext()

Examples

triplot()
x <- cbind(c(0.9, 0.05, 0.05), c(0.8, 0.1, 0.1), c(0.1, 0.9, 0), c(0, 0.9, 0.1))
tripoints(x, lwd=2, col=c("black","blue","red","green"), pch=16)
trilines(x, lwd=2, col="orange")
y <- cbind(c(0.05, 0.05, 0.9), c(0.25, 0.25, 0.5))
triarrow(y, col="blue", lwd=2, len=0.1)

Description

Plot points within a Holmans triangle (an equilateral triangle used to depict trinomial distributions).

Usage

tripoints(x, ...)

Arguments

Details

Plot of an equilateral triangle, in order to depict trinomial distributions. A trinomial distribution (that is, a trio of non-negative numbers that add to 1) is equated to a point in the triangle through the distances to the three sides. This makes use of the fact that for any point in an equilateral triangle, the sum of the distances to the three sides is constant. First use triplot() to first plot the equilateral triangle.

Value

The (x,y) coordinates of the points plotted.

See Also

triplot(), trilines(), triarrow(), tritext()

Examples

triplot()
x <- cbind(c(0.9, 0.05, 0.05), c(0.8, 0.1, 0.1), c(0.1, 0.9, 0), c(0, 0.9, 0.1))
tripoints(x, lwd=2, col=c("black","blue","red","green"), pch=16)
trilines(x, lwd=2, col="orange")
y <- cbind(c(0.05, 0.05, 0.9), c(0.25, 0.25, 0.5))
triarrow(y, col="blue", lwd=2, len=0.1)

Description

Plot text within a Holmans triangle (an equilateral triangle used to depict trinomial distributions).

Usage

tritext(x, labels, ...)

Arguments

Details

Plot of an equilateral triangle, in order to depict trinomial distributions. A trinomial distribution (that is, a trio of non-negative numbers that add to 1) is equated to a point in the triangle through the distances to the three sides. This makes use of the fact that for any point in an equilateral triangle, the sum of the distances to the three sides is constant. First use triplot() to first plot the equilateral triangle.

Value

Text is plotted at the (x,y) coordinates of the points.

See Also

triplot(), trilines(), triarrow(), tripoints()

Examples

triplot()
x <- cbind(c(0.25, 0.5, 0.25), c(1/3, 1/3, 1/3))
tripoints(x, lwd=2, pch=21, bg="lightblue")
xp <- x + c(0.02, 0, -0.02)
tritext(xp, c("(1/4,1/2,1/4)", "(1/3,1/3,1/3)"), adj=c(0, 0.5))

Description

Create a two-color palette from one color to another through some third color

Usage

twocolorpal(colors = c("slateblue", "white", "violetred"), n = 256, ...)

Arguments

Value

Vector of colors, from blue to white to red

See Also

revgray()

Examples

x <- matrix(rnorm(100, 0.5), ncol=10)
mxabs <- max(abs(x))
image(x, col=twocolorpal(), zlim=c(-mxabs, mxabs))

Description

Turn a vector into a single character string with the items separated by commas and an "and".

Usage

vec2string(x, conjunction = "and")

Arguments

Examples

vec2string(letters[1:2])
vec2string(letters[1:4])
vec2string(letters[1:4], "or")

Description

Plot a Venn diagram (with two groups), to scale, either with circles or with squares.

Usage

venn(
  setA = 50,
  setB = 50,
  both = 25,
  method = c("circle", "square"),
  labels = c("A", "B"),
  col = c("blue", "red")
)

Arguments

Details

Plots a to-scale Venn diagram with two sets, so that the relative areas of the two sets and their intersection are exact.

Value

None.

Examples

venn(setA=86, setB=1622, both=10)
venn(setA=86, setB=1622, both=10, method="square")

Description

For a numeric vector, move values below and above the q and 1-q quantiles to those quantiles.

Usage

winsorize(x, q = 0.006)

Arguments

Value

A vector like the input x, but with extreme values moved in to the q and 1-q quantiles.

Examples

x <- sample(c(1:10, rep(NA, 10), 21:30))
winsorize(x, 0.2)

Description

Calculates the x-axis limits when adding (long) labels to a plot

Usage

xlimlabel(x, xlabels, pos = 4, offset = 0.5, ...)

Arguments

Details

See graphics::text() for details on pos and offset.

Value

Minimum and maximum x-axis limits for adding horizontal text

Author(s)

Aimee Teo Broman

See Also

graphics::text()

Examples

x <- runif(15, -1, 1)*10
xlabs <- sapply(sample(1:20, 15, replace=TRUE),
                function(a) paste(LETTERS[1:a], collapse=""))
par(mfrow=c(2,1), las=1)
## Labels to the left ##
xlims <- xlimlabel(x, xlabs, pos=2)
plot(x, 1:length(x), xlim=xlims, ylab="Index")
text(x, 1:length(x), xlabs, pos=2)
## Labels to the right ##
xlims <- xlimlabel(x, xlabs, pos=4, cex=0.7)
plot(x, 1:length(x), xlim=xlims, ylab="Index")
text(x, 1:length(x), xlabs, pos=4, cex=0.7)