We covered how to visualize aggregate longitudinal
data here.
When there are not too many unique units, it can also be helpful to
view the individual growth curves. This technique can also be used
with larger datasets by taking random or targetted (e.g., for
unusual start or end values) subsets and plotting those).

We will use the ggplot2 package for the graphs and the
dataset on drug tolerance from the book Applied Longitudinal Data Analysis.

## load ggplot2require(ggplot2)

## Loading required package: ggplot2

## Loading required package: methods

## read in data set (tolerance data from the ALDA book)toldat<-read.table("https://stats.idre.ucla.edu/stat/r/examples/alda/data/tolerance1_pp.txt",sep=",",header=TRUE)## change id and male to factor variablestoldat<-within(toldat,{id<-factor(id)male<-factor(male,levels=0:1,labels=c("female","male"))})## view the first few lines of the datahead(toldat)

##   id age tolerance   male exposure time
## 1  9  11      2.23 female     1.54    0
## 2  9  12      1.79 female     1.54    1
## 3  9  13      1.90 female     1.54    2
## 4  9  14      2.12 female     1.54    3
## 5  9  15      2.66 female     1.54    4
## 6 45  11      1.12   male     1.16    0

Creating individual plots is very similar to the group level, but
facetted by the ID variable.

ggplot(data=toldat,aes(x=time,y=tolerance))+geom_line()+facet_wrap(~id)

Right now, points are simply connected to make lines. The levels
of the facetting variable (id) are displayed at the top of each
facet. We could also be interested in looking at the linear growth
of each individual. For this, we could plot the points and add the
line of best fit. To accomplish this, we just
switch geom_point() for geom_line() and add a linear
smooth by specifying method = “lm” to stat_smooth().
Smooths in ggplot2 are discussed in more detail here.

ggplot(data=toldat,aes(x=time,y=tolerance))+geom_point()+stat_smooth(method="lm",se=FALSE)+facet_wrap(~id)

Unless the facetting variable is inherently meaningful, we may want
to change the ordering. In this case, the IDs are not meaningfully
ordered, and we might instead want to order the facets from lowest
to highest value at time 0. We can do this with a bit of data
manipulation.

The code is a bit complex so we will break down the logic. The
outermost function is with this tells R that
all the computations should be done inside the environment of the
dataset, “tolerance”. The reorder function reorders the
level of a factor (here id) by sorting on something else. The
second part of the function is the variable that will be used to
reorder the ids, tolerance (the variable tolerance in the dataset,
tolerance). However, we do not want to order by all values of
tolerance for each id (what would have happened by default), we
only want to use the values of tolerance where time is equal to 0.
We can subset the variable using logical indexing. However, the
ordering variable needs to be as long as the id variable, so we
cannot actually subset the data. Instead, what we do is create a
vector that is TRUE if time is equal to 0 and otherwise is NA which
is missing. When a missing value is used to index a vector, the
result is missing no matter what is in the original vector. So
when we index by TRUE and missing, we will get the original vector
values when the indexing vector is TRUE and just missing
otherwise. Now we can take the mean indexed tolerance value for
each id, adding the na.rm = TRUE to ignore the missing
values (actually everything but time 0), and that is what the ids
are reordered by.

toldat$id<-with(toldat,reorder(id,tolerance[ifelse(time ==
    0,TRUE,NA)],FUN=mean,na.rm=TRUE))## print the id variable to see the new ordertoldat$id

##  [1] 9    9    9    9    9    45   45   45   45   45   268  268  268  268 
## [15] 268  314  314  314  314  314  442  442  442  442  442  514  514  514 
## [29] 514  514  569  569  569  569  569  624  624  624  624  624  723  723 
## [43] 723  723  723  918  918  918  918  918  949  949  949  949  949  978 
## [57] 978  978  978  978  1105 1105 1105 1105 1105 1542 1542 1542 1542 1542
## [71] 1552 1552 1552 1552 1552 1653 1653 1653 1653 1653
## attr(,"scores")
##    9   45  268  314  442  514  569  624  723  918  949  978 1105 1542 1552 
## 2.23 1.12 1.45 1.22 1.45 1.34 1.79 1.12 1.22 1.00 1.99 1.22 1.34 1.22 1.00 
## 1653 
## 1.11 
## 16 Levels: 918 1552 1653 45 624 314 723 978 1542 514 1105 268 442 ... 9

Now we can use the same code as before to create the graph, just
using the new, reordered data.

ggplot(data=toldat,aes(x=time,y=tolerance))+geom_point()+stat_smooth(method="lm",se=FALSE)+facet_wrap(~id)

We could use a similar logic to order the graphs by any variable we
wanted to, by the time 4 values, by the average of all the values,
etc.

Graphing nested and longitudinal data

We have seen how to graph simple, longitudinal data, but what if
there was a nesting factor also? For example, students nested
within classrooms over time or partners within couples? To
demonstrate this, we will make up a fake couple id for the tolerance
dataset. The details of the code to simulate a couple id are not
central.

toldat2<-toldat[order(toldat$male),]toldat2$coupleid<-factor(rep(unlist(by(toldat2$id,toldat2$male,function(x){ave(as.numeric(unique(x)),FUN=seq_along)})),each=5))

The graphs in ggplot2 are similar to before, but we group by
the regular id and facet by the couple id.

ggplot(data=toldat2,aes(x=time,y=tolerance,group=id))+geom_point()+stat_smooth(method="lm",se=FALSE)+facet_wrap(~coupleid)

We may also want to use different shapes and linetypes for males and
females so we can tell for each couple which partner it is.

ggplot(data=toldat2,aes(x=time,y=tolerance,group=id,shape=male,linetype=male))+geom_point()+stat_smooth(method="lm",se=FALSE)+facet_wrap(~coupleid)

As before, we may want to order the facets by average couple value
on tolerance at time 0. This graph also demonstrates how to
save and reuse plots in ggplot2. The exact same logic we
used to reorder the ids works for reordering the couple ids. This
time, the mean function is actually doing something besides removing
NAs because most couple ids have two partners.

toldat2$coupleid<-with(toldat2,reorder(coupleid,tolerance[ifelse(time ==
    0,TRUE,NA)],FUN=mean,na.rm=TRUE))## print the id variable to see the new ordertoldat2$coupleid

##  [1] 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5 6 6 6 6 6 7 7 7 7 7
## [36] 8 8 8 8 8 9 9 9 9 9 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5
## [71] 6 6 6 6 6 7 7 7 7 7
## attr(,"scores")
##    1    2    3    4    5    6    7    8    9 
## 1.68 1.33 1.40 1.46 1.60 1.11 1.28 1.00 1.11 
## Levels: 8 6 9 7 2 3 4 5 1

p<-ggplot(data=toldat2,aes(x=time,y=tolerance,group=id))+geom_line(aes(linetype=male),size=1)+facet_wrap(~coupleid)print(p)

Finally we demonstrate a few common customizations used
in ggplot2. Note we reuse the plot object from the previous graph.

## load the grid package for the unit function to adjust the legend widthrequire(grid)

## Loading required package: grid

p + labs(x = "Time", y = "Drug Tolerance", title = "Couple Drug Tolerance") + 
	theme(legend.key.width = unit(1, "cm")) + theme_bw() + scale_linetype(name = "Sex")

Summary

ggplot2 can easily create individual growth curves. You also
have access to all the power of ggplot2 with them—this means it is
easy to facet, add data summaries, add smooths, or anything else. Some data
manipulation can also help to make the individual curves more useable (e.g.,
sorting by a meaningful value rather than ID).