Package 'caroline'

Description

This function loops through all of the factor columns in a dataframe and adds new.levs to the factor levels list.

Usage

addFactLevs(x, new.levs=NA)

Arguments

See Also

factor, levels

Description

Finding the an extreme record for each group within a dataset is a more challenging routine task in R and SQL. This function provides a easy interface to that functionality either using R (fast for small data frames) or SQL (fastest for large data)

Usage

bestBy(df, by, best, clmns=names(df), inverse=FALSE, sql=FALSE)

Arguments

Value

A data frame of 'best' records from each factor level

Author(s)

David Schruth

See Also

groupBy

Examples

blast.results <- data.frame(score=c(1,2,34,4,5,3,23), 
                            query=c('z','x','y','z','x','y','z'), 
                            target=c('a','b','c','d','e','f','g')
                            )
best.hits.R <- bestBy(blast.results, by='query', best='score', inverse=TRUE)
best.hits.R
## or using SQLite
best.hits.sql <- bestBy(blast.results, by='query', best='score', inverse=TRUE, sql=TRUE)
best.hits.sql

Description

This is wrapper for dbWriteTable written with the the primary improvements focusing on database import into an existing table definition schema. The function matches and rearranges columns of the dataframe to database feilds and additionally performs checks for NA's in required variables, overlength strings, and type mismatches. There also exists support for updating of the PostgreSQL specific sequence for tables with auto incrementing primary keys.

Usage

dbWriteTable2(con, table.name, df, fill.null = TRUE, add.id=TRUE, 
                   row.names=FALSE, pg.update.seq=FALSE, ...)

Arguments

Value

If successful, the ids of the newly added database records (invisible)

See Also

dbWriteTable

Description

Plot the raw distribution of points, like a series of horizontal box plots.

Usage

distro.dots(x, jit.f=1, add=FALSE, pd=0,  vv=names(x), vvlabs=NULL,
                  xlim=range(unlist(x)), ...)

Arguments

Value

a 'distro dot plot' of variable distributions

Examples

n <- rnorm(130, 10, 3)
p <- rpois(110, 4)
u <- runif(300, 0, 20)
l <- rlnorm(130, log(2))
g <- rgamma(140, 3)

X <- list(a=u, random=n, array=p,of=l, variable=u, spreads=g)
distro.dots(x=X, jit.f=3)

Description

Takes the ellipsis function parameter (of optional named arguments) and compares with a internal (named list) of defaults for any non-conflicting user specified inputs.

Usage

ellipsis.defaults(x, nl)

Arguments

Value

a modified ellipsis variable (a named list of optional parameter values)

Examples

domy.xyplot <- function(data, x.name, y.name, ...){

 ellipsis <- ellipsis.defaults(x=list(...), nl=list(ylab=y.name,xlab=x.name))
 do.call( plot,  c(list(x=data[,x.name],y=data[,y.name]),  ellipsis))

}

domy.xyplot(data=mtcars, y.name='mpg', x.name='wt', xlab='weight')

Description

This function takes a fit multivariate regression model as input and converts the normal tabular output into a single line using repeated "+"or"-" symbols for significance

Usage

fit.1ln.rprt(fit, method=c('std.dev','p-value')[1], decimal.places=3, 
             name.char.len=6, print.inline=TRUE, rtrn.line=FALSE, R2=TRUE,mn='')

Arguments

Value

A character string of the form "++var1 +var5 var3 | -var2 –var4" indicating signifcance and direction of regression results

Examples

# Motor Trend car data
fit.1ln.rprt(lm('mpg ~ cyl + hp + wt + gear + disp', data=mtcars))

# Tooth Growth data
fit.1ln.rprt(lm('len ~ dose + supp', data=ToothGrowth))

# Earthquake attenuation data
fit.1ln.rprt(lm('accel ~ dist + mag', data=attenu))

# Fertility data
fit.1ln.rprt(lm('parity ~ age  + education + spontaneous + induced', data=infert), name.char.len=18)

Description

A trivial one-line function for exp(mean(log()))

Usage

geomean(x)

Arguments

Value

the geometric mean (a scalar value)

See Also

geometric.mean

Examples

geomean(rnorm(20,5))

Description

The R equvalent of a SQL 'group by' call.

Usage

groupBy(df, by, aggregation,  clmns=names(df), collapse=',',
                distinct=FALSE, sql=FALSE, full.names=FALSE, ...)

Arguments

Value

an summary/aggregate dataframe

See Also

aggregate, bestBy

Examples

df <- data.frame(a=runif(12),b=c(runif(11),NA), 
                 z=rep(letters[13:18],2),w=rep(letters[20:23],3))

groupBy(df=df, by='w', clmns=c(rep(c('a','b'),3),'z','w'), 
 aggregation=c('sum','mean','var','sd','min','max','paste','length'), 
 full.names=TRUE, na.rm=TRUE)
# or using SQLite
groupBy(df=df, by='w', clmns=c(rep(c('a','b'),2),'z','w'), 
        aggregation=c('sum','mean','min','max','paste','length'), 
        full.names=TRUE, sql=TRUE)


## passing a custom function
meantop <- function(x,n=2, ...)
  mean(x[order(x, decreasing=TRUE)][1:n], ...)
  
groupBy(df, by='w', aggregation=rep(c('mean','max','meantop'),2), 
                    clmns=rep(c('a','b'),3), na.rm=TRUE)

Description

This is a very simplified heatmap function: basically a convienent wrapper around the 'image' function.

Usage

heatmatrix(x, values=TRUE, clp=c('bottom','top'), rlp=c('left','right'), 
                             xadj=.02, yadj=.3, ylab.cntr=FALSE, cex=1, cex.axis=1, ...)

Arguments

Value

a heatmap plot

See Also

image, heatmap, heatmap.2

Examples

data(mtcars)
x  <- as.matrix(mtcars)

heatmatrix(x)

Description

This simple function makes R scatter plots interactive by creating an image and wrapping HTML around it: creating a hyperlinked hyperplot. Hover over the points to see what each is. Click to connect to a table below that will tell you more about each point (if browse ==TRUE).

Usage

hyperplot(x, y = NULL, annout = 1:length(x), 
                 name ="hyperplot.imagemap", w = 72 * 8, h = 72 * 6, 
                 link ="internal", browse = TRUE, cex = 1, ...)

Arguments

Value

HTML page with annotation mapped image

See Also

browseURL

Examples

if(capabilities()["png"] && interactive()) {

main.hov <- 'Hover over a point to see the name'
main.subsets <- '(annotated subset in red only)'
main.click.in <- 'click on points to visit table'
main.click.out <-  'click on points to visit external site'
cols <- c('black','red')
ext.url <- 'http://cran.r-project.org'

################################
## x and y as numeric vectors ##
################################
x.out <- nv(rnorm(13,2,sd=5),toupper(letters)[1:13])
x.in <- nv(rnorm(13,1,sd=.5),toupper(letters)[14:26])
y.out <- nv(rnorm(13,2,sd=5),toupper(letters)[1:13])
y.in <- nv(rnorm(13,1,sd=.5),toupper(letters)[14:26])
x <- c(x.out, x.in)
y <- c(y.out, y.in)

## simplest version
hyperplot(x,y, main=main.hov)

## same but with annotations being supplied as a paramter (instead of names on x)
names(x) <- NULL
hyperplot(x,y, annout=toupper(letters), main=main.hov)

## annotate only a subset 
hyperplot(x,y, annout=1:13, col=cols[rep(c(T,F), each=13)+1],
  main=paste(main.hov,main.subsets,sep='\n'))


########################
## annout as dataframe #
########################
## x and y as vectors 
x <- nv(x,toupper(letters)) # reinstate the names of x
df <- data.frame(ab=rep(c('a','b'),13),row.names=toupper(letters))
hyperplot(x,y, annout=df, 
                main=paste(main.hov, main.click.in, sep='\n'))

## x and y as names of columns in df
df <- cbind.data.frame(data.frame(x=x, y=y), df)
hyperplot(x='x',y='y', annout=df, 
                main=paste(main.hov, main.click.in, sep='\n'))

## using 'link' column name parameter to specify external links
df <- cbind.data.frame(df, 
            data.frame(url=ext.url, stringsAsFactors=FALSE))
hyperplot(x='x',y='y', annout=df, link='url', 
                main=paste(main.hov, main.click.out,sep='\n'))

## using reserved column name 'out' as a way to annotate a subset
df <- cbind.data.frame(df, data.frame(out=rep(c(TRUE,FALSE), each=13)))
hyperplot(x='x',y='y', annout=df, col=cols[df$out+1], 
                main=paste(main.hov, main.click.in, main.subsets,sep='\n'))



}

Description

This funciton will recursively search the package archives backwards in time until it finds a version that installs successfully. This function is useful for installing or troubleshooting package dependency trees where one or more packages "require" the very most recent version of R. Rather than upgrading your base R installation, you can use this function to search back in time until you find a version of the package that works with your existing version of R.

Usage

install.prev.pkg(pkg.nm, version=NULL, 
  repo.url='https://cran.r-project.org/src/contrib/Archive/')

Arguments

Value

NULL

Examples

#install.prev.pkg('mnormt')
#install.prev.pkg('mvtnorm')

Description

This function is a wrapper for segments which trigonometrically shortens the lines that are near the "1" end so as not to cluter or overplot the text label it is attached to.

Usage

labsegs(x0, y0, x1, y1, buf=.3, ...)

Arguments

See Also

segments

Examples

x <- rnorm(1000,0,.5)
y <- rnorm(1000,-.3,.15)

labdb <- data.frame(x=seq(-.5,.5,by=.5), y=rep(.85,3))
xlims <- c(-1,1)
ylims <- c(-.5,1)

x0.lbd <- x[rev(order(y))][1:3]
y0.lbd <- y[rev(order(y))][1:3]

par(mfrow=c(1,2))
plot(x,y, xlim=xlims, ylim=ylims, main='segments')
segments(x0=x0.lbd, y0=y0.lbd, x1=labdb$x, y1=labdb$y, col=rainbow(3), lwd=3)
text(x=labdb$x, y=labdb$y, labels=letters[1:3], cex=3, col=rainbow(3))

plot(x,y, xlim=xlims, ylim=ylims, main='labsegs')
labsegs(x0=x0.lbd, y0=y0.lbd, x1=labdb$x, y1=labdb$y, col=rainbow(3), lwd=3, buf=.07)
text(x=labdb$x, y=labdb$y, labels=letters[1:3], cex=3, col=rainbow(3))

Description

This function assesses four corners of an x vs y plot to find the one with the fewest points. It does so by iterating counting from outward to the middle, performing a sparseness-weighted average across corners.

Usage

legend.position(x,y,xlim=NULL,ylim=NULL,start=.05, end=.5, incr=.01)

Arguments

Value

one of four values ('bottom'/'top' + 'right'/'left') corresponding to the optimal [most white space] position of the legend.

See Also

Hmisc's largest.empty function

Examples

par(mfrow=c(3,3))

for(i in 1:9){
x <- rnorm(50)
y <- rlnorm(50)
clrs <- c('red','blue')
plot(x,y, col=clrs)
legend(x=legend.position(x,y), legend = c('x','y'), pch=1, col=clrs, inset=.1) 
}

Description

'leghead' is part 'head' and part 'summary'. It works best on a sorted dataframe where all you are interestesd in only the most (or least) abundant rows. An ideal place to use it is in a legend for ~lognormally distributed data. Additionally, an optional row-wise color coding column is added (the color 'gray' is used for missing row names).

Usage

leghead(x, n=7, tabulate=FALSE, colors=TRUE, na.name='NA', 
        na.col = "white", other.col = "gray",  na.last = TRUE)

Arguments

Value

A truncated dataframe with a new bottom row summarizing all the truncated ones.

See Also

summary, head, sstable

Examples

e <- data.frame(a=runif(12),b=runif(12), z=rep(letters[13:18],2),w=rep(letters[20:23],3))
tab <- sstable(e, idx.clmns=c('z'), ct.clmns=c('a','b'))
lh <- leghead(tab)
plot(x=lh$a, y=lh$b, cex=lh$sum*3, col=lh$color, pch=20)
legend('topleft',legend=rownames(lh), col=lh$color, pch=20)

Description

A grep/sub-like function that returns one or more back-referenced pattern matches in the form of a vector or as columns in a dataframe (respectively). Unlike sub, this function is more geared towards data extraction rather than data cleaning. The name is derived from the popular PERL regular expression 'match' operator function 'm' (eg. 'extraction =~ m/sought_text/').

Usage

m(pattern, vect, names="V", types="character", mismatch=NA, ...)

Arguments

Value

Either a vector or a dataframe depending on the number of backreferences in the pattern.

See Also

sub, gsub, regexpr, grep, gregexpr.

Examples

## single vector output examples
m(pattern="asdf.([A-Z]{4}).", 
  vect=c('asdf.AS.fds','asdf.ABCD.asdf', '12.ASDF.asdf','asdf.REWQ.123'))


Rurls <- c('http://www.r-project.org',    'http://cran.r-project.org',
           'http://journal.r-project.org','http://developer.r-project.org')
m(pattern="http://([a-z]+).r-project.org", vect=Rurls)


# dataframe output examples

data(mtcars)
m(pattern="^([A-Za-z]+) ?(.*)$", 
  vect=rownames(mtcars), names=c('make','model'), types=rep('character',2))

Description

Create x & y coordinates for an elipse from parameters. save.

Usage

makeElipseCoords(x0 = 0, y0 = 0, b = 1, a = 1, alpha = 0, pct.range = c(0,1), len = 50)

Arguments

Value

a 2 column (x and y) dataframe with coordinates for drawing an elipse

Examples

makeElipseCoords(x0 = 0, y0 = 0, b = 1, a = 2, alpha = 0)

Description

There is no easy way to move point labels around interactively on an a plot in R. This function allows a point and click way to select (using identify) and move (using locator) points by modifying the underlying dataframe.

Usage

mvlabs(df, n=nrow(df), x='x', y='y', l='lab', cols=colors()[grep("dark",colors())], ...)

Arguments

Value

a series of violin plots

See Also

locator,identify,labsegs

Examples

x <- rnorm(20); y <- rnorm(20)
  df <- data.frame(x,y, lab=as.character(letters[1:20]))
  plot(df$x, df$y, pch=''); text(df$x, df$y, df$lab)
  ## df <- mvlabs(df, 'x','y','lab', n=3)  
  plot(df$x, df$y, pch=''); text(df$x, df$y, df$lab)

Description

This function is a wrapper for merge that supports merging multiple vectors and or dataframes

Usage

nerge(l, ...)

Arguments

See Also

merge

Examples

df <- data.frame(a=c(6,7,8), b=c(9,8,7))
rownames(df) <- c('a','d','c')

l <- list(x=nv(c(1,2),c('a','b')),y=nv(c(2,3),c('b','d')),z=nv(c(4,1),c('c','d') ), w=df)
nerge(l, all=TRUE)


l2 <- list(a=nv(c(1.23, 1.423, 2.343), c('z','y','x')),b=nv(c(6.34,7.34, 12.545),c('z','w','y')))
nerge(l2, all=TRUE)

Description

The '$' or "[," operators for dataframes and tables do not carry along with them the row names. This function provides a solution for this problem. Additionally this function will accept a vector of values and a corresponding vector of value names–an ideal, in-line way for setting named-vectors as default parameters in new functions.)

Usage

nv(x, name)

Arguments

Value

a named vector or factor

Author(s)

David Schruth

See Also

vector,name

Examples

## example 1: pulling a row.named vector out of a dataframe
df <- data.frame(a=c(1,2,34,4,5,3,23), b=c('z','x','y','z','x','n','p'))
rownames(df) <- letters[1:nrow(df)]
nv(df,'a')
nv(df,'b')

## example 2: a naming vectors from scratch
nv(c(1,2,3), c('a','b','c'))
nv(df$a, df$b)

Description

This function helps to pad numbers on the left side with zeros so that they may be used to create strings used in filesystem names (for example).

Usage

pad(vect,np)

Arguments

Value

a (vector of) string(s) with np number of zeros padded on as a prefix

Author(s)

Jeremy Tantrum

Examples

pad(c(1,10,1000,10000), 4)

Description

generic function for parsing delimited lists from BATCH mode argument strings.

Usage

parseArgString(string, delimiter=',', min.param.ct=2, max.param.ct=2, param.range=NULL)

Arguments

Value

a vector or value that has been check for validity

Examples

## passes
parseArgString('apple,banana,pear', param.range=c("apple","banana","pear","pineapple"))
parseArgString('1,2,3', param.range=c(1,4))


## fails
## Not run: 
parseArgString('apple,banana,pear', param.range=c("apple","banana"))
parseArgString('1,2,3', param.range=c(1,2))

## End(Not run)

Description

This function will add extra columns to an existing dataframe. The second argument 'clmn' should specify which column(s) of the dataframe the percentage should be calculated by dividing each column's row-element by it's sum.

Usage

pct(df, clmns)

Arguments

Value

The original dataframe plus extra percentage columns corresponding to original columns in the dataframe.

Examples

df <- data.frame(a=c(1,2,3), b=c('x','y','z'), c=c(5,3,2))
pct(df, c('a','c'))

Description

Plot pie charts in an XY scatterplot. An overhauled wrapper of the original pie plot function. It is currently very slow: a recommened work around is to plot to something other than the default device (aka png, pdf, etc).

Usage

pies(x, show.labels = FALSE, show.slice.labels = FALSE, color.table = NULL, 
		radii = rep(2,length(x)), x0=NULL, y0=NULL, 
		edges = 200,  clockwise = FALSE, 
                init.angle = if (clockwise) 90 else 0, density = NULL, angle = 45, 
                border = NULL, lty = NULL, 
                other.color='gray', na.color='white', ...)

Arguments

Value

Pie charts as points on a plot

See Also

pie

Examples

## these examples are to the default plot window, which can be slow
## try instead to plot to png or pdf for example

## example 1
  pies(
       list(
            a=nv(c(1,2,3),c('one','two','thre')),
            b=nv(c(2,2,3),c('one','two','thre')),
            c=nv(c(1,2,3),c('one','two','thre'))
            ),
       x0=c(0,.5,1),
       y0=c(0,.5,1), radii=6, border=c('gray', 'black', 'red')
       )


## example 2
n <- 200
n.groups <- 10
n.subgroups <-  6

grps <- paste('gene',seq(1,n.groups), sep='')[round(runif(n,1,n.groups))]
subgrps <- paste('species',seq(1,n.subgroups), sep='')[round(runif(n,1,n.subgroups))]
group.df <- cbind.data.frame(grps,subgrps)
subgroup.list <- by(group.df, group.df$grps, function(x) x$subgrps)

pie.list <- lapply(subgroup.list, table)
col.tab <- nv(rainbow(6), unique(subgrps))

pies(x=pie.list, x0=rnorm(n.groups), y0=rnorm(n.groups), 
     radii=10, show.labels=TRUE, show.slice.labels=TRUE, color.table=col.tab)


## example 3  reading from external flat file
## salt.df <- read.delim('/path/to/my/file.tab')
## create a dummy dataset that might live inside the above file
salt.df <- data.frame(salinity=rnorm(25,5), temperature=rnorm(25,25),spec_a=rpois(25,4), 
							   spec_b=rpois(25,4), 
							   spec_c=rpois(25,4), 
							   spec_d=rpois(25,4),
							   spec_e=rpois(25,4)
							   )
## pull out the colnumn names that are specific to pie wedge numbers	
salt.spec.nms <- names(salt.df)[grep('spec',names(salt.df))]
## turn them into a list
pie.list <- lapply(1:nrow(salt.df), 
 function(i) as.table(nv(as.vector(as.matrix(salt.df[i,salt.spec.nms])),salt.spec.nms)))
names(pie.list)<- letters[1:25]
with(salt.df, pies(x=pie.list, x0=salinity, y0=temperature, radii=2))

Description

Plot a grid of x y plots showing how a third confounding variable 'z' changes the slope

Usage

## S3 method for class 'confound.grid'
plot(x, Y='y', X='x', confounder='z', breaks=3, mains='breaks',...)

Arguments

Value

a confound grid plot

Examples

# Swiss fertility confounding example
plot.confound.grid(x=swiss, "Infant.Mortality", "Fertility" , "Education", breaks=4)

Description

Visually compare continuuous univariate distributions using jittered and [progressive levels of] transparent points. This type of diagram plots positions of raw numerical data of comparable univariate distributions with a boxplot overlay indicating quartiles surrounding the central tendency of the underlying points. The distributions are vertically stacked (between) and jittered (within) as well as translucent in order to reduce overlapping points on larger-|N| datasets.

Usage

## S3 method for class 'sparge'
plot(x, f=NULL, out.range=range(unlist(x)), cat.names=names(x), 
           cpd=0, cpw=.4, jit.f=1, horiz=TRUE, add=FALSE, lgnd='auto', zl=FALSE,  
                                        col=1, box.brdrs='gray',alpha=.3, ...)

Arguments

Details

The function can currently take three different forms of input. First, x can be a list of numeric vectors with no need for f. Second, x can be a single vector that is to be split by factor f, of the same length. Third, x can be a dataframe and f specifies a model formula in the form of "outcome ~ control" (simple plot) or "out ~ predictor | control" (two series plot with legend).

Value

a 'sparge' [sprinkler/smear] plot of point distributions

See Also

See also 'boxplot' and 'stripchart' in package 'graphics' as well as 'sina', 'violin', 'bean', 'ridgelines', and 'raincloud' plots.

Examples

x <- lapply(sample(1:5), function(avg) (rnorm(500,avg)))
names(x) <- letters[1:length(x)]
plot.sparge(x, col=rep('blue',length(x)), main='sparge plots:\nfor distributional comparison')

## four random distributrions (from the 'boxplot' examples)
distros <- list(Uni05 = (1:100)/21, Norm = rnorm(100), `5T` = rt(100, df = 5), 
                                               Gam2 = rgamma(100, shape = 2))
plot.sparge(distros, ylab='distribution',xlab='')


# three more random distributions (from the 'sinaplot' examples)
bimodal <- c(rnorm(300, -2, 0.6), rnorm(300, 2, 0.6))
uniform <- runif(500, -4, 4)
normal <- rnorm(800,0,3)
distributions <- list(uniform = uniform, bimodal = bimodal, normal = normal)
plot.sparge(distributions, ylab='distribution',xlab='')


## using 'f' [as a factor] argument as grouping factor on just one treatment

# Orchard spray by treatment (compare with 'strip chart' plot)
OS <- with(OrchardSprays, split(decrease, treatment))
plot.sparge(OS, log = "x", main = "OrchardSprays", xlab='decrease',ylab='treatment')

# Tooth Growth
plot.sparge(x=ToothGrowth$len, f=ToothGrowth$sup, xlab='lenght', ylab='supplement')


# multi-predictor using model-based parsing of 'f' [as a formula] and 'x' as a dataset

# Tooth Growth 
plot.sparge(x=ToothGrowth, f="len ~ dose | supp", xlab='dose',ylab='tooth length', horiz=FALSE)
# or model-based with out the supplement sub-splitting
plot.sparge(x=ToothGrowth, f="len ~ dose",  xlab='dose',ylab='tooth length', horiz=FALSE)

# from the CO2 dataset
plot.sparge(CO2, 'uptake ~ Type | Treatment', horiz=FALSE,
            xlab='Type',ylab='Uptake', main='CO2')

# Joyner-Boore earthquake data (heavily rounded)
attenu$magnitude <- as.factor(round(attenu$mag))
attenu$distance <- as.factor(round(log10(attenu$dist)))
plot.sparge(x=attenu, f="accel ~ distance | magnitude", horiz=FALSE, 
  xlab='log10(distance)',ylab='acceleration', main='earthquake attenuation')

# Motor Trend cars data (rounded)
mtcars$cylinders <- as.factor(mtcars$cyl)
plot.sparge(x=mtcars, f="qsec ~ gear | cylinders", horiz=FALSE, 
         xlab='number of gears', ylab='seconds', main='Motor Trend Cars')

# fertility dataset 
infert$education <- as.factor(infert$education)
infert$ages <- jitter(infert$age, amount=1/2)
plot.sparge(x=infert, f="ages ~ spontaneous | education ", horiz=FALSE, 
       ylab='[jittered] ages, yrs', xlab='spontaneous' , main='fertility')

Description

This function performs a simple scatter plot but also superimposses a linear regression trend (abline) and optionally also the p-value of this line

Usage

## S3 method for class 'xy.ab.p'
plot(x, x.var, y.var, 
fit.line=TRUE, p.value=TRUE, slope=TRUE, p.col=c('red','gray','green'), 
               plot.labels=TRUE, verbose=TRUE, xlab=x.var, ylab=y.var, ...)

Arguments

Value

An x/y scatterplot with regression line

Examples

par(mfrow=c(6,5), mar=c(2,2,0,0), mgp=c(-1,.5,0))

eg.df <- expand.grid(names(swiss), names(swiss))
for(i in 1:nrow(eg.df)){
  print(i)
  xv <- as.character(eg.df[i,1]); print(xv)
  yv <- as.character(eg.df[i,2]); print(yv)
  if(yv != xv)
    plot.xy.ab.p(x=swiss, x.var=xv, y.var=yv, p.value=FALSE, slope=FALSE) 
}

Description

Used to create a clock on a plot as a way to keep track of the additional parameter of time for use in animated movies of multiple plots.

Usage

plotClock(hour, minute, x0 = 0, y0 = 0, r = 1)

Arguments

Value

a plot of a clock

Description

.

Usage

raAddArms(epsilon=.55, start=1, end=6, A.shift=0, R.shift=0, ...)

Arguments

See Also

raPlot

Description

.

Usage

raAddAxLabs(conditions=nv(c('a','b'),c('ref','obs')), normalize=T, add=TRUE, line=2)

Arguments

See Also

raPlot

Description

.

Usage

raAddSigLines(n, end=20, alpha=1e-3, nr=0, A.shift=0, plot=FALSE, ...)

Arguments

See Also

raPlot

Description

A plot which turns two vectors of count data into log scaled fold change ratio and average abundance. The plot derives from a Bland-Altman plot and is also very similar to an MA plot. The RA plot is unique, however, in it's creative inclusion of the vector-unique 'arms' which are artificially introduced into the plot by adding a <1 epsilon factor before the log function is applied. The name RAy comes from the fact that the aforementioned 'uniques' arms addition makes it strongly resemble a geometric ray. Many of the parameters to the function play off of this convenient anatomical analogy.

Usage

raPlot(a, b=NULL, uniques=5, normalize=FALSE,  
        nr=0, alpha = 0.01, jitter=FALSE, jit.wgts=NULL,
        rex=1, flat=TRUE, tail=.5, arms=.5, spine=1, border=NULL, plot=TRUE,  ...)

Arguments

Value

a RAy plot

See Also

limma::plotMA, edgeR::maPlot

Examples

a <- rnbinom(n=10000, mu=5, size=2)
b <- rnbinom(n=10000, mu=5, size=2)

## the alternative 
plot(a,b)
## the raPlot version
raPlot(a, b) 

## highlight the condition unique points in the same way as  edgeR's "maPlot"
RA <- raPlot(a, b, pch='')
cond.unique <- apply(cbind(a,b), 1, function(d) any(d==0))
points(RA$A,RA$R, col=c('black','orange')[cond.unique+1])

## try playing with jittering over plotted points
raPlot(a, b, jitter=.3)

Description

This function is a slight (genome annotation friendly) variant of the built-in read.delim function in R. Two non-standard defaults have been set: stringsAsFactors=TRUE, quote="". An additional parameter "check.row.ct", triggering a count.fields call, has been added to further ensure the integrity of large data files.

Usage

read.tab(file, check.row.ct = TRUE, stringsAsFactors = FALSE, 
               quote = "", header=TRUE, ...)

Arguments

Value

a dataframe.

Description

Used to group a dataframe of numbers by a factor that need not be the same length. Find the a factor in the old df and use it to group by the new trumping factor (NA's allowed)

Usage

regroup(df, old, new, clmns, funcs=rep('sum',length(clmns)), combine=TRUE)

Arguments

Value

a dataframe with number of rows equal to the number of factor levels in 'new'

Examples

df <- data.frame(a=rnorm(20),b=rpois(20,1))

mapping <- data.frame(old=rownames(df), new=rep(c('a','b'),10))
regroup(df, old=mapping$old, new=mapping$new)

Description

Used to easily rename the rows of a dataframe.

Usage

rerowname(df, old='NA', new='unknown')

Arguments

Value

A dataframe with one new rowname

Examples

df <- data.frame(a=c(1,2,3), b=c('x','y','z'), c=c(5,3,2))
rownames(df) <- c('p','q','NA')
rerowname(df)

Description

Spie Chart

Usage

spie(p1, p2, init.angle=pi, multi, col = rainbow(length(x$radii)), bg=col, lwd=2, 
              pie.labs=TRUE, grid=TRUE, grid.labs=TRUE, scale=TRUE, p1.circle=TRUE)

Arguments

Author(s)

Romain Francois [email protected] & David Schruth [email protected]

References

D. G. Feitelson (2003), "Comparing Partitions with Spie Charts". School of Computer Science and Engineering, The Hebrew University of Jerusalem.

Michael Friendly (2022), Spie chart – a comparison of two pie charts.

See Also

pie

Examples

p1  <- c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12)
  p2<- c(0.06, 0.15, 0.52, 0.14, 0.08, 0.05)
  plot(p1, p2, multi=c(.5, 1, 1.5, 2))

Description

A wrapper for the "table()" function that also calculates the row-wise sum and sorts by the new column.

Usage

sstable(x, idx.clmns, ct.clmns = NULL, na.label = "NA")

Arguments

Value

A dataframe sorted by the count columns.

Author(s)

David Schruth

See Also

ledghead, table, order, sort

Examples

e <- data.frame(a=runif(12),b=runif(12), z=rep(letters[13:18],2),w=rep(letters[20:23],3))
  e <- data.frame(a=runif(10),b=runif(10), z=rep(letters[12:16],2),w=rep(letters[20:24],2))
  sstable(e, idx.clmns=c('z','w'), ct.clmns='a')
  sstable(e, idx.clmns=c('z'), ct.clmns=c('a','b'))
  sstable(e, idx.clmns=c('z','w'))
  e <- data.frame(a=10,b=0, z=as.factor(NA))
  sstable(e, 'z', c('a','b'))
  e <- data.frame(a=10,b=0, z=NA, w=NA)
  sstable(e, 'z', c('a','b'))
  e <- data.frame(a=runif(10),b=runif(10),m=rep(c('one','two'),5), 
                  z=factor(rep('z',10), levels=c('z','x')))
  sstable(e, idx.clmns=c('m','z'))

Description

Convert a table to a dataframe while perserving the same number of columns and rows and names of each.

Usage

tab2df(x, ...)

Arguments

Value

a dataframe

See Also

table

Examples

x <- data.frame(a=runif(10),b=runif(10), z=rep(letters[1:5],2))
  as.data.frame(x)
  tab2df(x)
  x <- nv(rnorm(10), letters[1:10])
    as.data.frame(x)
  tab2df(x)
  x <- nv(rnorm(2), c('x.b','y.b'))
    as.data.frame(x)
  tab2df(x)
  x <- nv(rnorm(2), c('b.x','b.y'))
    as.data.frame(x)
  tab2df(x)
  e <- data.frame(a=runif(10),b=runif(10), z=rep(letters[13:17],2))
  x <- as.table(sapply(c('a','b'),function(cc) by(e[,'a'],list(e$z), sum)))
    as.data.frame(x)
  tab2df(x)
  x <- as.table(by(1:10, list(a=rep(1:5,2),b=rep(1:2,5)), sum))
    as.data.frame(x)
  tab2df(x)
  x <- as.table(nv(c(54,34), c('a','b')))
    as.data.frame(x)
  tab2df(x)

  x <- table(a='x',b='y')
  tab2df(x)

Description

Generate a new plot window with just text centered in the middle. This is ideally used in conjunction with the 'layout' command to label columns and rows of the grid.

Usage

textplot(..., x=1, y=1)

Arguments

Value

A new plot window wiht just text

See Also

layout, text

Examples

layout(rbind(c(1,1,1),c(2,3,4), c(5, 6,7)),
       widths=c(5, 10,10) , heights=c(5, 10,10))
textplot('title', cex=2)
textplot('row 1', srt=90, cex=2)
plot(1,2)
hist(c(1,2,34,4,3,2,2))
textplot('row 2', srt=90, cex=2)
pie(c(1,23,3,1,1,2,3,4,54,5))
plot(c(1,2,4,4,23,2), c(1,2,4,3,2,2))

Description

This is a simple function which grabs the current plot dimentions and adjusts them by shrinking them by 4

Usage

usr2lims(adj=.04)

Arguments

Value

A 2 item (x and y) list of 2 item (min and max) vectors for x and y limits of the current plot area

See Also

par

Examples

plot(c(0,1), c(0,1))
usr2lims()

Description

The limma package has great functions for making venn diagrams from a matrix. This function is provides upstream functionality to turn a list of factors into this required input format.

Usage

vennMatrix(l)

Arguments

Value

a matrix with columns for list elements and rows with globally unique factor levels

See Also

venCounts

Examples

l <- list(a=factor(c('x','y','z')), b=factor(c('w','x','v')))

vennMatrix(l)

Description

Use weights to jitter values away fromt their current value.

Usage

wjitter(x, w, amount=.43)

Arguments

Value

A weighted jittered vector of the same length as x

Examples

x <- seq(1,20)
w <- runif(20, 0,1)
plot(x,wjitter(w,x))

Description

A simple wrapper for write.table with the same options as read.delim

Usage

write.delim(df, file, quote = FALSE, row.names = FALSE, sep = "\t", ...)

Arguments

Value

A tab delimited text file

See Also

read.delim

Examples

## Not run: 
x <- data.frame(a = I("a \" quote"), b = pi)
write.delim(x, file = "foo.tab")

## End(Not run)