Hmisc (version 4.0-2)

dotchartpl: Enhanced Version of dotchart Function for plotly

Description

This function produces a plotly interactive graphic and accepts a different format of data input than the other dotchartx functions. It was written to handle a hierarchical data structure including strata that further subdivide the main classes. Strata, indicated by the mult variable, are shown on the same horizontal line, and if the variable big is FALSE will appear slightly below the main line, using smaller symbols, and having some transparency. This is intended to handle output such as that from the summaryP function when there is a superpositioning variable group and a stratification variable mult, especially when the data have been run through the addMarginal function to create mult categories labelled "All" for which the user will specify big=TRUE to indicate non-stratified estimates (stratified only on group) to emphasize.

When viewing graphics that used mult and big, the user can click on the legends for the small points for groups to vanish the finely stratified estimates.

Usage

dotchartpl(x, major, minor=NULL, group=NULL, mult=NULL, big=NULL, htext=NULL, num=NULL, denom=NULL, lower=NULL, upper=NULL, xlim=NULL, xlab='Proportion', tracename=NULL, limitstracename=NULL, width=800, col=colorspace::rainbow_hcl)

Arguments

x

a numeric vector used for values on the x-axis

major

major vertical category, e.g., variable labels

minor

minor vertical category, e.g. category levels within variables

group

superpositioning variable such as treatment

mult

strata names for further subdivisions without groups

big

omit if all levels of mult are equally important or if mult is omitted. Otherwise denotes major (larger points, right on horizontal lines) vs. minor (smaller, transparent points slightly below the line).

htext

additional hover text per point

num

if x represents proportions, optinally specifies to numerators, to be used in fractions added to hover text. When num is given, x is automatically added to hover text, rounded to 3 digits after the decimal point.

denom

like num but for denominators

lower

lower limits for optional error bars

upper

upper limits for optional error bars

xlim

x-axis limits

xlab

x-axis label

tracename

plotly trace name if group is not used

limitstracename

plotly trace name for lower and upper if group is not used

col

a function or vector of colors to assign to group. If a function it will be evaluated with an argument equal to the number of distinct groups.

width

width of plot in pixels

Value

plotly object

See Also

dotchartp

Examples

## Not run: 
# set.seed(1)
# d <- expand.grid(major=c('Alabama', 'Alaska', 'Arkansas'),
#                  minor=c('East', 'West'),
#                  group=c('Female', 'Male'),
#                  city=0:2)
# n <- nrow(d)
# d$x <- (1 : nrow(d)) + runif(n)
# d$num <- round(100*runif(n))
# d$denom <- d$num + round(100*runif(n))
# d$lower <- d$x - runif(n)
# d$upper <- d$x + runif(n)
# 
# # numerators and denominators are meaningless in this example
# with(d,
#  dotchartpl(x, major, minor, group, city, lower=lower, upper=upper,
#             big=city==0, num=num, denom=denom, xlab='x'))
# 
# n <- 500
# set.seed(1)
# d <- data.frame(
#   race         = sample(c('Asian', 'Black/AA', 'White'), n, TRUE),
#   sex          = sample(c('Female', 'Male'), n, TRUE),
#   treat        = sample(c('A', 'B'), n, TRUE),
#   smoking      = sample(c('Smoker', 'Non-smoker'), n, TRUE),
#   hypertension = sample(c('Hypertensive', 'Non-Hypertensive'), n, TRUE),
#   region       = sample(c('North America','Europe','South America',
#                           'Europe', 'Asia', 'Central America'), n, TRUE))
# 
# d <- upData(d, labels=c(race='Race', sex='Sex'))
# 
# dm <- addMarginal(d, region)
# s <- summaryP(race + sex + smoking + hypertension ~
#                 region + treat,  data=dm)
# 
# s$region <- ifelse(s$region == 'All', 'All Regions', as.character(s$region))
# 
# with(s, 
#  dotchartpl(freq / denom, major=var, minor=val, group=treat, mult=region,
#             big=region == 'All Regions', num=freq, denom=denom)
# )
# 
# s2 <- s[- attr(s, 'rows.to.exclude1'), ]
# with(s2, 
#      dotchartpl(freq / denom, major=var, minor=val, group=treat, mult=region,
#                 big=region == 'All Regions', num=freq, denom=denom)
# )
# # Note these plots can be created by plot.summaryP when options(grType='plotly')
# ## End(Not run)