require(grid)
require(lattice)
require(latticeExtra)
require(HH)
data(ProfChal) ## List of matrices. See below for discussion of the dataset.
##
## ProfChal[[2]]
##
## The as.matrix.listOfNamedMatrices display is easier to read when the
## rownames are very long, as in this example. Because the default
## print of the matrix repeats the rownames several times, with only a
## few columns of the data shown in each repetition, the actual matrix
## structure of the data values is obscured.
##
as.matrix.listOfNamedMatrices(ProfChal[2])
## Count plot
plot.likert(ProfChal[[2]],
main='Is your job professionally challenging?',
sub="This plot looks better in a 9in x 4in window.")
## Percent plot calculated automatically from Count data
plot.likert(ProfChal[[2]], as.percent=TRUE,
main='Is your job professionally challenging?',
sub="This plot looks better in a 9in x 4in window.")
## Examples with higher-dimensional arrays.
tmp2 <- array(1:12, dim=c(3,4), dimnames=list(B=LETTERS[3:5], C=letters[6:9]))
tmp3 <- array(1:24, dim=c(2,3,4),
dimnames=list(A=letters[1:2], B=LETTERS[3:5], C=letters[6:9]))
tmp4 <- array(1:120, dim=5:2,
dimnames=list(
W=LETTERS[10:14],
X=letters[6:9],
Y=LETTERS[3:5],
Z=letters[1:2]))
## positive.order=FALSE is the default. With arrays
## the rownames within each item of an array are identical.
plot.likert(tmp2)
## plot.likert(tmp3)
plot.likert(tmp3, layout=c(1,4), auto.key=list(columns=3))
plot.likert(tmp4, layout=c(3,2), auto.key=list(columns=4))
plot.likert(tmp4, layout=c(3,2),
auto.key=list(columns=4),
rightAxis=TRUE, between=list(x=c(4,4)))
plot.likert(tmp4, layout=c(3,2),
auto.key=list(columns=4, border=TRUE, padding.text=4, height=.5))
plot.likert(tmp4, layout=c(3,2), auto.key=list(columns=4), strip.left=FALSE)
plot.likert(tmp4, layout=c(3,2), strip.left=FALSE,
auto.key=list(columns=4, border=TRUE, padding.text=4, height=.5))
## plot.likert interprets vectors as single-row matrices.
## http://survey.cvent.com/blog/customer-insights-2/box-scores-are-not-just-for-baseball
Responses <- c(15, 13, 12, 25, 35)
names(Responses) <- c("Strongly Disagree", "Disagree", "No Opinion",
"Agree", "Strongly Agree")
plot.likert(Responses, main="Retail-R-Us offers the best everyday prices.",
sub="This plot looks better in a 9in x 2.6in window.")
## plot.likert will plot a single-column matrix as if it were entirely
## "No Opinion".
plot.likert(as.matrix(Responses),
main="This is silly, but it doesn't give an error")
## Vertical diverging stacked barcharts are possible.
## They usually do not look good and are not recommended.
plot.likert(Responses, horizontal=FALSE,
aspect=1.5,
main="Retail-R-Us offers the best everyday prices.")
##
## reverse=TRUE is needed for a single-column key with
## horizontal=FALSE and with space="right"
plot.likert(Responses, horizontal=FALSE,
aspect=1.5,
main="Retail-R-Us offers the best everyday prices.",
auto.key=list(space="right", columns=1,
reverse=TRUE, padding.text=2),
sub="This plot looks better in a 4in x 3in window.")
plot.likert(Responses, horizontal=FALSE,
aspect=1.5,
main="Retail-R-Us offers the best everyday prices.",
auto.key=list(size=3, columns=5, between=.5,
between.columns=2, just=.5),
sub="This plot looks better in a 7in x 3in window.")
plot.likert(ProfChal[[2]],
horizontal=FALSE,
scales=list(x=list(rot=90)),
auto.key=list(columns=1, space="right", reverse=TRUE),
main="Vertical diverging stacked barcharts are possible.
They usually do not look good and are not recommended.",
sub="This plot looks less bad in a 8.5in x 7in window.")
## Diverging stacked bar charts have many uses in addition to plotting
## rating scales. The AudiencePercent example, provided by Naomi
## B. Robbins, illustrates the age distribution of users of different
## brands of a product. The original source for this example used
## multiple pie charts.
data(AudiencePercent)
AudiencePercent
plot.likert(AudiencePercent,
auto.key=list(columns=4, between=1, between.columns=2),
xlab="Percentage of audience younger than 35 (left of zero) and older than 35 (right of zero)",
main="Target Audience",
sub="This plot looks better in a 7in x 3.5in window.")
## Color palettes from RColorBrewer can be named.
##
## Since age is always positive and increases in a single direction,
## this example uses colors from a sequential palette for the age
## groups. In this example we do not use a diverging palette that is
## appropriate when groups are defined by a characteristic, such as
## strength of agreement or disagreement, that can increase in two directions.
## Initially we use the "Blues" palette in the BrewerPaletteName argument.
plot.likert(AudiencePercent,
auto.key=list(columns=4, between=1, between.columns=2),
xlab="Percentage of audience younger than 35 (left of zero) and older than 35 (right of zero)",
main="Target Audience",
BrewerPaletteName="Blues",
sub="This plot looks better in a 7in x 3.5in window.")
## The really light colors in the previous example are too light.
## Therefore we use the col argument directly. We chose to use an
## intermediate set of Blue colors selected from the "Blues" palette.
plot.likert(AudiencePercent,
auto.key=list(columns=4, between=1, between.columns=2),
xlab="Percentage of audience younger than 35 (left of zero) and older than 35 (right of zero)",
main="Target Audience",
col=brewer.pal(9, "Blues")[4:7],
sub="This plot looks better in a 7in x 3.5in window.")
## Professional Challenges example.
##
## The data for this example is a list of related likert scales, with
## each item in the list consisting of differently named rows. The data
## is from a questionnaire analyzed in a recent Amstat News article.
## The study population was partitioned in several ways. Data from one
## of the partitions (Employment sector) was used in the first example
## in this help file. The examples here show various options for
## displaying all partitions on the same plot.
##
data(ProfChal)
## ProfChal
as.matrix.listOfNamedMatrices(ProfChal, minlength=6)
## 1. Plot counts with rows in each panel sorted by positive counts.
##
## Strip labels on left (the default for plot.likert).
## positive.order=TRUE is the recommended (but not default) setting for
## lists because the rownames within each item of the list are usually
## different.
##
ProfChalCountsPlot <-
plot.likert(ProfChal, box.width=unit(.4,"cm"),
strip.left.par=list(cex=.7, lines=5),
main='Is your job professionally challenging?',
main.middle=.65,
xlab="Counts",
positive.order=TRUE,
rightAxis=TRUE, ## display row count totals
sub="This plot looks better in a 10in x 7in window.")
ProfChalCountsPlot
## Strip labels on top (the default for most lattice plots).
## Change line spacing
names(ProfChal)[6] <- "Attitude toward Professional Recognition"
plot.likert(ProfChal, box.width=unit(.3,"cm"),
strip.left=FALSE,
main='Is your job professionally challenging?',
main.middle=.65,
xlab="Counts",
positive.order=TRUE)
## Restore original line spacing
names(ProfChal)[6] <- "Attitude
toward
Professional
Recognition"
## 2. Plot percents with rows in each panel sorted by positive percents.
## This is a different sequence than the counts. Row Counts are
## displayed on the right axis.
ProfChalPctPlot <-
plot.likert(ProfChal,
as.percent=TRUE, ## implies display Row Count Totals
box.width=unit(.4,"cm"),
strip.left.par=list(cex=.7, lines=5),
main='Is your job professionally challenging?',
main.middle=.65,
positive.order=TRUE,
sub="This plot looks better in a 10in x 7in window.")
ProfChalPctPlot
## 3. Putting both percents and counts on the same plot requires
## coordination The easiest way is to order the original tables of
## counts by the percents.
## Manually construct RowTotals and Tables of Row percents
rowTotals <- sapply(ProfChal, rowSums, simplify=FALSE)
rowTotals
ProfChalPct <- sapply(ProfChal, function(x) x / rowSums(x) * 100,
simplify=FALSE)
as.matrix.listOfNamedMatrices(ProfChalPct, minlength=6)
## Find sort order for each matrix in the list
PctOrders <- sapply(ProfChalPct,
function(x) attr(as.likert(x), "positive.order"),
simplify=FALSE)
PctOrders
## Original data on Count scale, but ordered as for Percents
ProfChalOrdered <- mapply(FUN=function(L,O) L[order(O),,drop=FALSE],
ProfChal, PctOrders,
SIMPLIFY=FALSE)
as.matrix.listOfNamedMatrices(ProfChalOrdered, minlength=6)
## Counts plot in Pct order
CountsPlot <-
plot.likert(ProfChalOrdered,
box.width=unit(.4,"cm"),
strip.left.par=list(cex=.7, lines=5),
main='Is your job professionally challenging?',
main.middle=.65,
xlab="Counts", ylab.right="Row Count Totals",
rightAxis=TRUE,
sub="This plot looks better in a 10in x 7in window.")
CountsPlot
## Pct plot in Pct order
PctPlot <-
plot.likert(ProfChalOrdered,
as.percent="noRightAxis", ## suppress right axis
box.width=unit(.4,"cm"),
strip.left.par=list(cex=.7, lines=5),
main='Is your job professionally challenging?',
main.middle=.65,
xlab="Percent",
sub="This plot looks better in a 10in x 7in window.")
PctPlot
## Remove main title, left axis tick labels, left strip, bottom legend
## from plot and keep the vertical spacing allocated to those items.
CountsPlotRevised <- emptyMainLeftAxisLeftStripBottomLegend(CountsPlot)
CountsPlotRevised
## Manual stretching of the graph window may be needed to get the
## alignment right. For the standard 7x7 window, this aligns well
## (using either the pdf device or the windows device).
##
PctPlotUpdate <-
update(PctPlot,
sub="The 'position' arguments are set for a 7in x 7in window")
print(PctPlotUpdate, position=c(0,0,.83,1), more=TRUE)
print(CountsPlotRevised, position=c(.76,0,1,1), more=FALSE)
## This graph looks better in a wider window.
## Manual stretching of graph window may be needed to get the alignment right.
## The x position values (.8 and .75) are by trial and error.
## They depend on the current configuration of the plotting window.
print(PctPlot, position=c(0,0,.8,1), more=TRUE)
print(CountsPlotRevised, position=c(.75,0,1,1), more=FALSE)
## The ProfChal data is done again with explicit use of ResizeEtc
## in ?HH:::ResizeEtcRun the code above in your browser using DataLab