longCatEDA: sorter – R documentation

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sorter

General Sorting Function

Description

A function to sort an longCat object created by longCat. sorter must be used directly when stratified plots of subgroups is desired, or when sorting other than the default sorting is desired. Otherwise, sorter is used internally with the defaults by longCatPlot if lc$sorted=FALSE. If an object has already been sorted (lc$sort=TRUE), sorter will not resort it, but will print a code example of how to use multiple sortings.

Usage

sorter(lc, ascending = TRUE, whichColumns = NULL, num = TRUE,
                 mindur = NULL, igrpt = FALSE, customSort = NULL,
                 initFirst = FALSE, group = NULL, groupLabels = NULL,
                 ggap = NULL)

Arguments

`lc`	an object of class `longCat` created by `longCat`.
`ascending`	logical - should sorting be done ascending. Default is `TRUE`.
`whichColumns`	a numeric list indicating which columns in `lc$y` should be used for sorting (.e.g., `c(1, 5, 7)`). Useful if, for example, an intervention occurs after data collection has started, and the user is not interested in sorting on pre-intervention observations.
`num`	see `makePatterns` for details.
`mindur`	see `makePatterns`.
`igrpt`	should `sorter` (ig)nore (r)e(p)ea(t)ed values for each row in `lc$y` for sorting purposes? See `norpt`.
`customSort`	a vector of the same length as the number of rows in `lc$y` providing a user defined variable on which to sort the data prior to secondarily applying the default sort. If `group` is not `NULL`, group will be sorted on prior to the `customSort` variable. Alternatively, `lc$y` can be sorted without calling `sorter` using `lc$y.sorted <- lc$y[o, ]` where `o` is the `order` (e.g., use `o <- order(customSort)`). The user must also set `lc$sorted <- TRUE` to prevent on-the-fly default sorting from being carried out by `longCatPlot`. Users unfamiliar with sorting in `R` should take care not to confuse `order` with `sort`. Default is `NULL`. If any values on `customSort` are missing, the function will return an error message.
`initFirst`	if `customSort` is not `NULL`, setting `initFirst=TRUE` will sort on initial values prior to the custom sorting variable.
`group`	a vector of the same length as the number of rows in `lc$y` indicating group membership. Default is `NULL`. If `group` is `NA`, corresponding rows in `lc$y` will be deleted prior to completing the sorting, and a warning indicating this has been done is printed to the console. If a large number of cases have missing data on the grouping variable, consider recoding the missings into their own group, e.g., `group[is.na(group)] <- -999` and add a missing label to `groupLabels`, e.g. `groupLabels=c('Missing', 'Group1', 'Group2', 'Etc.')`.
`groupLabels`	a vector of numeric or character labels of the same length as the number of unique values in `group`. Default is `NULL`. If `group` is not `NULL` and `groupLabels` is not provided, then the numeric values in `group` are used as the labels.
`ggap`	a number zero to 1. The proportion of blank rows to be plotted between groups when `group` is specified. The default of `NULL` is set to 0.05 when groups are present, 0.0 when there are no groups.

Value

Returns an object of class longCat where lc$sorted=TRUE. See longCat for values.

Author(s)

Stephen Tueller

References

Tueller, S. J., Van Dorn, R. A., & Bobashev, G. V. (2016). Visualization of categorical longitudinal and times series data (Report No. MR-0033-1602). Research Triangle Park, NC: RTI Press. http://www.rti.org/publication/visualization-categorical-longitudinal-and-times-series-data

Examples

### create a plot like that in Figure 3 from Tueller, Van Dorn, & Bobashev (2016)
par(mfrow=c(1,2), bg='cornsilk3')
times <- c(1,100,200,300,400,500,600)
f3lc <- longCat(example3, times); f3lc$sorted <- TRUE; f3lc$y.sorted <- f3lc$y
longCatPlot(f3lc, main='Unsorted', colScheme='heat', lwd=2, legendBuffer=.2)
f3lc <- longCat(example3, times)
longCatPlot(f3lc, main='Sorted', colScheme='heat', lwd=2, legendBuffer=.2)

### sort with a grouping variable and plot
par(mfrow=c(1,1), bg='cornsilk3', mar=c(5.1, 4.1, 4.1, 9.1), xpd=TRUE)
times <- c(1,100,200,300,400,500,600)
lc <- longCat(example3, times)
group <- sample(1:3, nrow(example3), replace=TRUE)
grouplc <- sorter(lc, group=group, groupLabels=1:3)
cols <- longCatPlot(grouplc, groupBuffer=.15, main='Grouped Data', colScheme='heat', 
                    lwd=2, legendBuffer=0)
legend(610, 130, legend=1:5, col=cols, lty=1, lwd=2)
par(bg='transparent', mar = c(5, 4, 4, 2) + 0.1, xpd=FALSE)

### using the sorted data from the previous plot, repeate using ggplot2 
#   following the example of Figure 4 of bdemarest's answer on 
#   https://stackoverflow.com/questions/11513149/
#   good-ways-to-visualize-longitudinal-categorical-data-in-r/
grouplc.df <- data.frame(id=1:nrow(grouplc$group.sorted), 
                group=grouplc$group.sorted[,1], grouplc$y.sorted)
grouplc.long <- reshape(grouplc.df,
                        varying = names(grouplc$y.sorted),
                        v.names = "score",
                        timevar = "time",
                        times = times[1:ncol(grouplc$y.sorted)],
                        direction = "long")
grouplc.long$score <- factor(grouplc.long$score)
grouplc.long$group <- factor(grouplc.long$group, level=3:1)
# remove NA's introduced using group option in sorter
grouplc.long <- na.omit(grouplc.long) 
library(ggplot2)
ggplot(grouplc.long, aes(x=time, y=id, fill=score)) + 
  geom_tile(colour="transparent") +
  scale_fill_manual(values=cols) +
  facet_grid(group ~ ., space="free_y", scales="free_y")

### sort with a grouping variable and events and plot
times <- c(1,100,200,300,400,500,600)
set.seed(45962)
events <- matrix(sample(1:3, nrow(example3)*2, replace=TRUE), nrow(example3), 2)
set.seed(23498)
event.times <- matrix(sample(min(times):max(times), nrow(example3)*2, replace=TRUE), 
nrow(example3), 2)
labels <- c('Street', 'Drug Tx', 'Jail', 'Prison', 'Unknown')
eventLabels=c('Arrest', 'Drug Test', 'Hearing')
eventlc <- longCat(example3, times=times, Labels=labels,  
              events=events, event.times=event.times, 
              eventLabels=eventLabels)
set.seed(4290)              
groupevent <- sample(1:3, nrow(example3), replace=TRUE)
groupeventlc <- sorter(eventlc, group=groupevent)            
par(mfrow=c(1,1), bg='cornsilk3', mar=c(5.1, 4.1, 4.1, 12.1), xpd=TRUE)
cols <- longCatPlot(groupeventlc, legendBuffer=0, groupBuffer=0.15, 
                    main='Grouping and Events')
legend(610, 130, legend=groupeventlc$Labels, lty=1, col=cols, lwd=2)
legend(610, 60, legend=groupeventlc$eventLabels, 
       pch=1:length(groupeventlc$eventLabels))
par(bg='transparent', mar = c(5, 4, 4, 2) + 0.1, xpd=FALSE)

longCatEDA

Package for Plotting Categorical Longitudinal and Time-Series Data

v0.31

GPL (>= 3)

Authors

Stephen Tueller. Funded by the National Institute on Drug Abuse (NIDA) Award number 1R03DA030850, the National Institute on Alcohol Abuse and Alcoholism (NIAAA) Award Number R03 AA019775, and the National Institute of Justice Award Number 2011-RY-BX-0003.

Initial release

2017-03-28