Line plots of longitudinal summary data in R using ggplot2

July 9, 2015
By

(This article was first published on A HopStat and Jump Away » Rbloggers, and kindly contributed to R-bloggers)

I recently had an email for a colleague asking me to make a figure like this in ggplot2 or trellis in R:

plot of chunk final_plot

As I know more about how to do things in ggplot2, I chose to use that package (if it wasn't obvious from the plot or other posts).

Starting Point

Cookbook R/) has a great starting point for making this graph. The solution there is not sufficient for the desired graph, but that may not be clear why that is. I will go through most of the steps of customization on how to get the desired plot.

Creating Data

To illustrate this, I will create some sample dataset:

N <- 30
id <- as.character(1:N) # create ids
sexes = c("male", "female")
sex <- sample(sexes, size = N/2, replace = TRUE) # create a sample of sex
diseases = c("low", "med", "high")
disease <- rep(diseases, each = N/3) # disease severity 
times = c("Pre", "0", "30", "60")
time <- rep(times, times = N) # times measured 
t <- 0:3
ntimes = length(t)
y1 <- c(replicate(N/2, rnorm(ntimes, mean = 10+2*t)), 
        replicate(N/2, rnorm(ntimes, mean = 10+4*t)))
y2 <- c(replicate(N/2, rnorm(ntimes, mean = 10-2*t)), 
        replicate(N/2, rnorm(ntimes, mean = 10-4*t)))
y3 <- c(replicate(N/2, rnorm(ntimes, mean = 10+t^2)), 
        replicate(N/2, rnorm(ntimes, mean = 10-t^2)))

data <- data.frame(id=rep(id, each=ntimes), sex=rep(sex, each=ntimes), 
                   severity=rep(disease, each=ntimes), time=time, 
                   Y1=c(y1), Y2=c(y2), Y3=c(y3)) # create data.frame
#### factor the variables so in correct order
data$sex = factor(data$sex, levels = sexes)
data$time = factor(data$time, levels = times)
data$severity = factor(data$severity, levels = diseases)
head(data)
  id    sex severity time        Y1        Y2        Y3
1  1 female      low  Pre  9.262417 11.510636  9.047127
2  1 female      low    0 10.223988  8.592833 11.570381
3  1 female      low   30 13.650680  5.696405 13.954316
4  1 female      low   60 15.528288  5.313968 18.631744
5  2 female      low  Pre  9.734716 11.190081 10.086104
6  2 female      low    0 12.892207  7.897296  9.794494

We have a longitudinal dataset with 30 different people/units with different ID. Each ID has a single sex and disease severity. Each ID has 4 replicates, measuring 3 separate variables (Y1, Y2, and Y3) at each time point. The 4 time points are previous (Pre)/baseline, time 0, 30, and 60, which represent follow-up.

Reformatting Data

In ggplot2, if you want to plot all 3 Y variables, you must have them in the same column, with another column indicating which variable you want plot. Essentially, I need to make the data “longer”. For this, I will reshape the data using the reshape2 package and the function melt.

library(reshape2)
long = melt(data, measure.vars = c("Y1", "Y2", "Y3") )
head(long)
  id    sex severity time variable     value
1  1 female      low  Pre       Y1  9.262417
2  1 female      low    0       Y1 10.223988
3  1 female      low   30       Y1 13.650680
4  1 female      low   60       Y1 15.528288
5  2 female      low  Pre       Y1  9.734716
6  2 female      low    0       Y1 12.892207

It may not be clear what has been reshaped, but reordering the data.frame can illustrate that each Y variable is now a separate row:

head(long[ order(long$id, long$time, long$variable),], 10)
    id    sex severity time variable     value
1    1 female      low  Pre       Y1  9.262417
121  1 female      low  Pre       Y2 11.510636
241  1 female      low  Pre       Y3  9.047127
2    1 female      low    0       Y1 10.223988
122  1 female      low    0       Y2  8.592833
242  1 female      low    0       Y3 11.570381
3    1 female      low   30       Y1 13.650680
123  1 female      low   30       Y2  5.696405
243  1 female      low   30       Y3 13.954316
4    1 female      low   60       Y1 15.528288

Creating Summarized data frame

We will make a data.frame with the means and standard deviations for each group, for each sex, for each Y variable, for separate time points. I will use plyr to create this data.frame, using ddply (first d representing I'm putting in a data.frame, and the second d representing I want data.frame out):

library(plyr)
agg = ddply(long, .(severity, sex, variable, time), function(x){
  c(mean=mean(x$value), sd = sd(x$value))
})
head(agg)
  severity  sex variable time      mean        sd
1      low male       Y1  Pre  9.691420 1.1268324
2      low male       Y1    0 12.145178 1.1218897
3      low male       Y1   30 14.304611 0.3342055
4      low male       Y1   60 15.885740 1.7616423
5      low male       Y2  Pre  9.653853 0.7404102
6      low male       Y2    0  7.652401 0.7751223

There is nothing special about means/standard deviations. It could be any summary measures you are interested in visualizing.

We will also create the Mean + 1 standard deviation. We could have done standard error or a confidence interval, etc.

agg$lower = agg$mean + agg$sd
agg$upper = agg$mean - agg$sd

Now, agg contains the data we wish to plot.

Time is not on your side

Time as a factor

If you look at the plot we wish to make, we want the lines to be connected for times 0, 30, 60, but not for the previous data. Let's try using the time variable, which is a factor. We create pd, which will be a ggplot2 object, which tells that I wish to plot the means + error bars slightly next to each other.

class(agg$time)
[1] "factor"
pd <- position_dodge(width = 0.2) # move them .2 to the left and right

gbase  = ggplot(agg, aes(y=mean, colour=severity)) + 
  geom_errorbar(aes(ymin=lower, ymax=upper), width=.3, position=pd) +
  geom_point(position=pd) + facet_grid(variable ~ sex)
gline = gbase + geom_line(position=pd) 
print(gline + aes(x=time))

plot of chunk gbase

None of the lines are connected! This is because time is a factor. We will use gbase and gline with different times to show how the end result can be achieved.

Time as a numeric

We can make time a numeric variable, and simply replace Pre with -1 so that it can be plotted as well.

agg$num_time = as.numeric(as.character(agg$time))
agg$num_time[ is.na(agg$num_time) ] = -1
unique(agg$num_time)
[1] -1  0 30 60

In a previous post, I have discussed as an aside of creating a plot in ggplot2 and then creating adding data to the data.frame. You must use the %+% to update the data in the object.

gline = gline %+% agg
print(gline + aes(x=num_time))

plot of chunk plus

If you look closely, you can see that Pre and time 0 are very close and not labeled, but also connected. As the scale on the x-axis has changed, the width of the error bar (set to 0.3), now is too small and should be changed if using this solution.

Although there can be a discussion if the Pre data should be even on the same plot or the same timeframe, I will leave that for you to dispute. I don't think it's a terrible idea, and I think the plot works because the Pre and 0 time point data are not connected. There was nothign special about -1, and here we use -30 to make it evenly spaced:

agg$num_time[ agg$num_time == -1 ] = -30
gline = gline %+% agg
print(gline + aes(x=num_time))

plot of chunk create_time_neg

That looks similar to what we want. Again, Pre is connected to the data, but we also now have a labeling problem with the x-axis somewhat. We still must change the width of the error bar in this scenario as well.

Time as a numeric, but not the actual time point

In the next case, we simply use as.numeric to the factor to create a variable new_time that will be 1 for the first level of time (in this case Pre) to the number of time points, in this case 4.

agg$new_time = as.numeric(agg$time)
unique(agg$new_time)
[1] 1 2 3 4
gline = gline %+% agg
print(gline + aes(x = new_time))

plot of chunk new_time

Here we have something similar with the spacing, but now the labels are not what we want. Also, Pre is still connected. The width of the error bars is now on a scale from 1-4, so they look appropriate.

Creating a Separate data.frame

Here, we will create a separate data.frame for the data that we want to connect the points. We want the times 0-60 to be connected and the Pre time point to be separate.

sub_no_pre = agg[ agg$time != "Pre",]

Mulitple data sets in plot function

Note, previously we did:

gline = gbase + geom_line(position=pd) 

This assumes that geom_line uses the same data.frame as the rest of the plot (agg). We can fully specify the arguments in geom_line so that the line is only for the non-Pre data:

gbase = gbase %+% agg
gline = gbase + geom_line(data = sub_no_pre, position=pd, 
                          aes(x = new_time, y = mean, colour=severity)) 
print(gline + aes(x = new_time))

plot of chunk non_conn
Note, the arguments in aes should match the rest of the plot for this to work smoothly and correctly.

Relabeling the axes

Now, we simply need to re-label the x-axis so that it corresponds to the correct times:

g_final = gline + aes(x=new_time) +
  scale_x_continuous(breaks=c(1:4), labels=c("Pre", "0", "30", "60"))

We could be more robust in this code, using the levels of the factor:

time_levs = levels(agg$time)
g_final = gline + aes(x=new_time) +
  scale_x_continuous(
    breaks= 1:length(time_levs), 
    labels = time_levs)
print(g_final)

plot of chunk relabel2

Give me a break

My colleague also wanted to separate the panels a bit. We will use the panel.margin arguments and use the unit function from the grid package to define how far apart we want the axes.

library(grid)
g_final = g_final + theme(panel.margin.x = unit(1, "lines"), 
                          panel.margin.y = unit(0.5, "lines"))
print(g_final)

plot of chunk final

Additional options and conclusoin

I believe legends should be inside a plot for many reasons (I may write about that). Colors can be changed (see scale_colour_manual). Axis labels should be changed, and the Y should be labeled to what they are (this is a toy example).

Overall, this plot seems to be what they wanted and the default options work okay. I hope this illustrates how to customize a ggplot to your needs and how you may need to use multiple data.frames to achieve your desired result.

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