dotchartpl {Hmisc} | R Documentation |
This function produces a plotly
interactive graphic and accepts
a different format of data input than the other dotchart
x
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 group
s to
vanish the finely stratified estimates.
dotchartpl(x, major, minor=NULL, group=NULL, mult=NULL, big=NULL, htext=NULL, num=NULL, denom=NULL, xlim=NULL, xlab='Proportion', width=800, col=colorspace::rainbow_hcl)
x |
a numeric vector used for values on the |
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
|
big |
omit if all levels of |
htext |
additional hover text per point |
num |
if |
denom |
like |
xlim |
|
xlab |
|
col |
a function or vector of colors to assign to |
width |
width of plot in pixels |
a plotly
object
Frank Harrell
## 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)) with(d, dotchartpl(x, major, minor, group, city, big=city==0, num=num, denom=denom)) 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)