13  Manipulation of Longitudinal Data

flowchart LR
sf[Storage Formats]
sf1[Tree]
sf2[Short and Wide]
sf3[Tall and Thin]
sf3a[Uniform Number<br>of Rows,<br>With NAs]
sf3b[Variable Number<br>of Rows,<br>With Few NAs]
sf --> sf1 & sf2 & sf3
sf3 --> sf3a & sf3b
locf[Last<br>Observation<br>Carried<br>Forward]
crr[Carry<br>Forward<br>by Adding<br>Rows]
fi[Find First<br>Observation<br>Meeting Criteria]
cf[Using Functions<br>That Count<br>Criteria Met]
im[Inexact<br>Matching]
sf3a --> locf
sf3b --> crr & fi & cf & im

The data.table package provides exceptional capabilities for manipulating longitudinal data, especially when performing operations by subject. Before showing a variety of examples that typify common tasks, consider that there are many ways to store longitudinal data, including

13.1 Uniform Number of Rows

Consider first the case where most of the subjects have the same number of rows and NA is used as a placeholder with a certain measurement is not made on a given time. Though highly questionable statistically, last observation carried forward (LOCF) is sometimes used to fill in NAs so that simple analyses can be performed.

LOCF is a form of missing value imputation where imputed values are treated the same as real measurements, resulting in highly deflated estimates of standard errors and much higher than nominal \(\alpha\) in frequentist statistical testing.

data.table has an efficient built-in functions for LOCF (and for last observation carried backward and fill-in using a constant value): nafill and setnafill. Consider a longitudinal data table L with 5 observations per each of two subjects.

require(Hmisc)
require(data.table)
require(ggplot2)
L <- data.table(id  = c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2),
                day = c(1, 2, 3, 4, 5, 1, 2, 3, 4, 5),
                y1  = 1:10,
                y2  = c(NA, 1, NA, NA, 2, 1, 2, 3, 4, NA),
                key = .q(id, day))
# .q(id, day) is the Hmisc version of c('id', 'day')
L
    id day y1 y2
 1:  1   1  1 NA
 2:  1   2  2  1
 3:  1   3  3 NA
 4:  1   4  4 NA
 5:  1   5  5  2
 6:  2   1  6  1
 7:  2   2  7  2
 8:  2   3  8  3
 9:  2   4  9  4
10:  2   5 10 NA
setnafill(L, "locf", cols=.q(y1, y2))
L
    id day y1 y2
 1:  1   1  1 NA
 2:  1   2  2  1
 3:  1   3  3  1
 4:  1   4  4  1
 5:  1   5  5  2
 6:  2   1  6  1
 7:  2   2  7  2
 8:  2   3  8  3
 9:  2   4  9  4
10:  2   5 10  4

setnafill changed the data table in place. Note that y1 is unchanged since it contained no NAs.

13.2 Variable Number of Rows

Consider the somewhat more frequently occuring situation where there is one row per subject per time at which a measurement is made. Consider a different data table L, and create records to fill out the observations, carrying forward to 4 days the subject’s last observation on y if it was assessed earlier than day 4.

L <- data.table(id  = c(2, 2, 2, 3, 3, 3, 3, 4, 4, 5, 5, 5),
                day = c(1, 2, 3, 1, 2, 3, 4, 1, 2, 1, 2, 3),
                y    =  1 : 12, key='id')
w <- copy(L)
u <- w[, .q(seq, maxseq) := .(1 : .N, .N),
       by=id][
       seq == maxseq & day < 4,]
u <- u[, .(day = (day + 1) : 4, y = y), by=id]
u
w <- rbind(L, u, fill=TRUE)
setkey(w, id, day)
w
1
fresh start with no propagation of changes back to L. Only needed if will be using := to compute variables in-place and you don’t want the new variables also added to L. This is related to data.table doing things by reference instead of making copies. w <- L does not create new memory for w.
2
within one subject compute consecutive record numbers 1 : .N and last record number .N
3
separately by subject
4
feed this result directly into a data.table operation to save last records when the last record is on a day before day 4
5
fill out to day 4 by adding extra observations, separately by subject
6
vertically combine observations in L and u, filling variables missing from one of the data tables with missing values (NA)
7
sort by subject id and day within id and set these variables as dataset keys 
   id day  y
1:  2   4  3
2:  4   3  9
3:  4   4  9
4:  5   4 12
    id day  y
 1:  2   1  1
 2:  2   2  2
 3:  2   3  3
 4:  2   4  3
 5:  3   1  4
 6:  3   2  5
 7:  3   3  6
 8:  3   4  7
 9:  4   1  8
10:  4   2  9
11:  4   3  9
12:  4   4  9
13:  5   1 10
14:  5   2 11
15:  5   3 12
16:  5   4 12

Find the first time at which y >= 3 and at which y >= 7.

day[y >= 3] is read as “the value of day when y >= 3”. It is a standard subscripting operation in R for two parallel vectors day and y. Taking the minimum value of day satisfying the condition gives us the first qualifying day.
L[, .(first3 = min(day[y >= 3]),
      first7 = min(day[y >= 7])), by=id]
   id first3 first7
1:  2      3    Inf
2:  3      1      4
3:  4      1      1
4:  5      1      1

Same but instead of resulting in an infinite value if no observations for a subject meet the condition, make the result NA.

mn <- function(x) if(length(x)) min(x) else as.double(NA)
# as.double needed because day is stored as double precision
# (type contents(L) to see this) and data.table requires
# consistent storage types
L[, .(first3 = mn(day[y >= 3]),
      first7 = mn(day[y >= 7])), by=id]
   id first3 first7
1:  2      3     NA
2:  3      1      4
3:  4      1      1
4:  5      1      1

Add a new variable z and compute the first day at which z is above 0.5 for two days in a row for the subject. Note that the logic below looks for consecutive days for which records exist for a subject. To also require the days to be one day apart add the clause day == shift(day) + 1 after shift(z) > 0.5.

set.seed(1)
w <- copy(L)
w[, z := round(runif(.N), 3)]
u <- copy(w)
u
    id day  y     z
 1:  2   1  1 0.266
 2:  2   2  2 0.372
 3:  2   3  3 0.573
 4:  3   1  4 0.908
 5:  3   2  5 0.202
 6:  3   3  6 0.898
 7:  3   4  7 0.945
 8:  4   1  8 0.661
 9:  4   2  9 0.629
10:  5   1 10 0.062
11:  5   2 11 0.206
12:  5   3 12 0.177
mn <- function(x)
  if(! length(x) || all(is.na(x))) as.double(NA) else min(x, na.rm=TRUE)
u[, consecutive := z > 0.5 & shift(z) > 0.5, by=id][, 
    firstday    := mn(day[consecutive]),     by=id]
u
    id day  y     z consecutive firstday
 1:  2   1  1 0.266       FALSE       NA
 2:  2   2  2 0.372       FALSE       NA
 3:  2   3  3 0.573       FALSE       NA
 4:  3   1  4 0.908          NA        4
 5:  3   2  5 0.202       FALSE        4
 6:  3   3  6 0.898       FALSE        4
 7:  3   4  7 0.945        TRUE        4
 8:  4   1  8 0.661          NA        2
 9:  4   2  9 0.629        TRUE        2
10:  5   1 10 0.062       FALSE       NA
11:  5   2 11 0.206       FALSE       NA
12:  5   3 12 0.177       FALSE       NA

In general, using methods that involve counters makes logic more clear, easier to incrementally debug, and easier to extend the condition to any number of consecutive times. Create a function that computes the number of consecutive TRUE values or ones such that whenever the sequence is interrupted by FALSE or 0 the counting starts over. As before we compute the first day at which two consecutive z values exceed 0.5.

nconsec is modified from code found here.
nconsec <- function(x) x * sequence(rle(x)$lengths)
# rle in base R: run length encoding
# Example:
x <- c(0,0,1,1,0,1,1,0,1,1,1,1)
nconsec(x)
# To require that the time gap between measurements must be <= 2 time
# units use the following example
t <- c(1:9, 11, 14, 15)
rbind(t=t, x=x)
nconsec(x & t <= shift(t) + 2)

u <- copy(w)
# nconsec(z > 0.5) = number of consecutive days (counting current
# day) for which the subject had z > 0.5
# u[, firstday := mn(day[nconsec(z > 0.5) == 2]), by=id] 
#                 |  |  |                    |
#           minimum  |  |                    |
#                  day  |                    |
#               such that                    |
#    it's the 2nd consecutive day with z > 0.5
u[,
    firstday :=
                 mn(day
                    [
                     nconsec(z > 0.05)
                     == 2]),
    by=id]
u
1
for all rows in data table u
2
add new column firstday
3
whose value is the minimum day (first day)
4
such that
5
the number of consecutive days (previous or current) for which z > 0.05
6
equals 2
7
run separately by subject id 
 [1] 0 0 1 2 0 1 2 0 1 2 3 4
  [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
t    1    2    3    4    5    6    7    8    9    11    14    15
x    0    0    1    1    0    1    1    0    1     1     1     1
 [1] 0 0 1 2 0 1 2 0 1 2 0 1
    id day  y     z firstday
 1:  2   1  1 0.266        2
 2:  2   2  2 0.372        2
 3:  2   3  3 0.573        2
 4:  3   1  4 0.908        2
 5:  3   2  5 0.202        2
 6:  3   3  6 0.898        2
 7:  3   4  7 0.945        2
 8:  4   1  8 0.661        2
 9:  4   2  9 0.629        2
10:  5   1 10 0.062        2
11:  5   2 11 0.206        2
12:  5   3 12 0.177        2

13.2.1 Summarizing Multiple Baseline Measurements

Suppose that subjects have varying numbers of records, with some having only two measurements, and that at least one measurement occurs before an intervention takes place at day zero. Our goal is to summarize the pre-intervention measurements, adding three new variables to the data and doing away with baseline records but instead carrying forward the three baseline summarizes merged with the post-intervention responses. The three summary measures are

  • the average response measurement over all measurements made between day -7 and day 0
  • the average response over all measurements made earlier than day -7 if there are any (NA otherwise)
  • the slope over all measurements made on day 0 or earlier if there are at least two such measurements that are at least 2 days apart (NA otherwise)

Simulate data, starting with simulating a random number npre of baseline measures from 1-5 and a possibly different random number npost of follow-up measurements from 1-5. Sample days without replacement so that measurements within subject are always on different days. The dataset has a subject-specific variable age that is not used in this analysis added to it. The overall mean time trend is quadratic in time.

set.seed(11)
n <- 40
w <- lapply(1 : n,
            function(i) { npre   <- sample(1:5, 1)
                          npost  <- sample(1:5, 1)
                          day <- sort(c(sample(-21 : 0, npre),
                                        sample(1 : 180, npost)))
                          age <- sample(40:80, 1)
                          y <- round(day + (day - 90)^2 / 20 + 30 * rnorm(npre + npost))
                          data.table(id=i, age, day, y=y)
                        }  )
head(w)
u <- rbindlist(w)
u
# Show distribution of number of points per subject
with(u, table(table(id)))
u[, id := factor(id)]
ggplot(u, aes(x=day, y=y)) +
  geom_line(aes(col=id, alpha=I(0.4)), data=u) +
  geom_point(aes(col=id), data=u) +
  guides(color='none')
1
separately for integers 1, 2, 3, …, n runs a function on that integer with the function producing a list that is stored in an element of a larger list; response y is normal with mean being a quadratic function of day
2
argument i is the subject number; for that subject simulate the number of observations then simulate data using that number
3
stack all the little data tables into one tall data table u
4
ggplot2 needs a variable mapped to the color aesthetic to be factor so it treats subject id as categorical and doesn’t try to create a continuous color gradient 

[[1]]
   id age day   y
1:  1  51  -6 427
2:  1  51  -5 486
3:  1  51  37 196
4:  1  51  60 104

[[2]]
   id age day   y
1:  2  42 -20 572
2:  2  42 -15 511
3:  2  42 -14 538
4:  2  42  -9 443
5:  2  42  -5 399
6:  2  42   6 335
7:  2  42  39 181
8:  2  42  47 172

[[3]]
   id age day   y
1:  3  66 -15 508
2:  3  66 126 211
3:  3  66 133 178
4:  3  66 156 348
5:  3  66 165 461

[[4]]
   id age day   y
1:  4  77 -16 573
2:  4  77  -6 429
3:  4  77  -1 440
4:  4  77   0 395
5:  4  77   6 293
6:  4  77  14 329
7:  4  77  21 281

[[5]]
   id age day   y
1:  5  42 -11 544
2:  5  42 -10 492
3:  5  42  -5 421
4:  5  42  -1 483
5:  5  42   8 341
6:  5  42  18 219
7:  5  42 161 429

[[6]]
   id age day   y
1:  6  58 -20 659
2:  6  58 112 141
3:  6  58 136 257
4:  6  58 138 255

     id age day   y
  1:  1  51  -6 427
  2:  1  51  -5 486
  3:  1  51  37 196
  4:  1  51  60 104
  5:  2  42 -20 572
 ---               
218: 39  42 -20 589
219: 39  42 -18 613
220: 39  42 139 222
221: 40  48  -2 414
222: 40  48  66  92

2 3 4 5 6 7 8 9 
4 3 4 9 5 9 3 3

For one subject write a function to compute all of the three summary measures for which the data needed are available. Don’t try to estimate the slope if some times are not at least 3 days apart. By returning a list the g function causes data.table to create three variables.

g <- function(x, y) {
  j <- x <= 0 & ! is.na(y)
  x <- x[j]; y <- y[j]
  n <- length(x)
  if(n == 0) return(list(y0 = NA_real_, ym8 = NA_real_, slope = NA_real_))
  pre0  <- x >= -7
  prem8 <- x <  -7
  list(y0    = if(any(pre0))     mean(y[pre0])                else NA_real_,
       ym8   = if(any(prem8))    mean(y[prem8])               else NA_real_,
       slope = if(length(x) > 1 && diff(range(x)) >= 3)
                                 unname(coef(lsfit(x, y))[2]) else NA_real_)
}
1
Analyze observations with non-missing y that are measured pre-intervention
2
data.table requires all variables being created to have the same type for every observation; this includes the type of NA
3
range(x) computes a 2-vector with the min and max x, and diff subtracts the min from the max. The && and operator causes diff(...) to not even be evaluated unless there are at least two points. lsfit is a simple function to fit a linear regression. coef extracts the vector of regression coefficients from the fit, and we keep the second coefficient, which is the slope. The slope carries an element name of X (created by lsfit) which is removed by unname().

Check the function.

g(numeric(0), numeric(0))
$y0
[1] NA

$ym8
[1] NA

$slope
[1] NA
g(-10, 1)
$y0
[1] NA

$ym8
[1] 1

$slope
[1] NA
g(c(-10, -9), c(1, 2))
$y0
[1] NA

$ym8
[1] 1.5

$slope
[1] NA
g(c(-10, -3), c(1, 2))
$y0
[1] 2

$ym8
[1] 1

$slope
[1] 0.1428571

Now run the function separately on each subject. Then drop the pre-intervention records from the main data table u and merge the new baseline variables with the follow-up records.

base <- u[, g(day, y), by = .(id)]
head(base)
   id       y0 ym8      slope
1:  1 456.5000  NA         NA
2:  2 399.0000 516 -11.900901
3:  3       NA 508         NA
4:  4 421.3333 573 -10.073095
5:  5 452.0000 518  -6.131274
6:  6       NA 659         NA
dim(u)
[1] 222   4
u <- u[day > 0]   # Keep only post-intervention (follow-up) records
dim(u)
[1] 115   4
u <- Merge(base, u, id = ~ id)     # Merge is in Hmisc
       Vars Obs Unique IDs IDs in #1 IDs not in #1
base      4  40         40        NA            NA
u         4 115         40        40             0
Merged    7 115         40        40             0

Number of unique IDs in any data frame : 40 
Number of unique IDs in all data frames: 40 
u
     id    y0 ym8     slope age day   y
  1:  1 456.5  NA        NA  51  37 196
  2:  1 456.5  NA        NA  51  60 104
  3:  2 399.0 516 -11.90090  42   6 335
  4:  2 399.0 516 -11.90090  42  39 181
  5:  2 399.0 516 -11.90090  42  47 172
 ---                                   
111: 38 407.0 498 -10.05983  57  62  69
112: 38 407.0 498 -10.05983  57  67 114
113: 38 407.0 498 -10.05983  57  96  42
114: 39    NA 601        NA  42 139 222
115: 40 414.0  NA        NA  48  66  92

13.2.2 Interpolation/Extrapolation to a Specific Time

Suppose that subjects have varying numbers of records, with some having only one measurement. Our goal is to summarize each subject with a single measurement targeted to estimate the subject’s response at 90 days. Our strategy is as follows:

  • If the subject has a measurement at exactly 90 days, use it.
  • If the subject has only one measurement, use that measurement to estimate that subject’s intercept (vertical shift), and use the time-response curve estimated from all subjects to extrapolate an estimate at 90 days.
  • If the subject has two or more measurements, use the same approach but average all vertical shifts from the overall loess curve to adjust the loess “intercept” in estimating y at the target time.

Use the data table u created above.

timeresp <- u[! is.na(day + y), approxfun(lowess(day, y))]
w <- data.table(day=1:180, y=timeresp(1:180))
ggplot(u, aes(x=day, y=y)) +
  geom_line(aes(col=id, alpha=I(0.4)), data=u) +
  geom_point(aes(col=id), data=u) +
  geom_line(aes(x=day, y, size=I(1.5)), data=w) +
  guides(color='none')
1
lowess runs the loess nonparametric smoother and creates a list with vectors x and y; approxfun translates this list into a function that linearly interpolates while doing the table look-up into the lowess result. timeresp is then a function to estimate the all-subjects-combined time-response curve. lowess doesn’t handle NAs properly so we excluded points that are missing on either variable.
2
evaluates the whole time-response curve for day 1, 2, 3, …, 180

As done previously, we follow the good practice of perfecting a function that summarizes the data for one subject, then let data.table run that function separately by subject ID. The method adjusts the loess curve so that it perfectly fits one observed point or best fits all observed points on the average, then we use that adjusted curve to estimate the response at day 90. This is an empirical approach that trusts the raw data to provide the intercept, resulting in interpolated or extrapolated estimates that are as noisy as the real data. Function f also returns the number of days from 90 to the closest day used for estimation of day 90’s response.

f <- function(x, y, id=character(0), target=90) {
  j <- ! is.na(x + y)
    if(! any(j)) {
      cat('no non-missing x, y pairs for subject', id, '\n')
        return(list(day = target, distance=NA_real_, yest=NA_real_))
        }
  x <- x[! is.na(x)]; y <- y[! is.na(y)]
  distance <- abs(x - target)
  i        <- which(distance == 0)
  if(length(i)) return(list(day=target, distance=0., yest=mean(y[i])))
  # estimate mean y at observed x - mean loess at observed x to adjust
  z <- timeresp(c(target, x))
  list(day      = target,
       distance = min(distance),
       yest     = z[1] + mean(y) - mean(z[-1]) )
  }

Test the function under a variety of situations.

# n=1, y is on the loess curve so projected y at x=90 should be also
f(25, 253.0339)
$day
[1] 90

$distance
[1] 65

$yest
[1] 115.017
timeresp(c(25, 90))
[1] 260.3086 122.2918
# n=1, x=90 exactly
f(90, 111.11)
$day
[1] 90

$distance
[1] 0

$yest
[1] 111.11
# n=3, two x=90
f(c(1, 2, 90, 90), c(37, 3, 33, 34))
$day
[1] 90

$distance
[1] 0

$yest
[1] 33.5
# n=1, not on loess curve
f(25, timeresp(25) - 100)
$day
[1] 90

$distance
[1] 65

$yest
[1] 22.29177
timeresp(90) - 100
[1] 22.29177
# n=2, interpolate to 90 guided by loess
f(c(80, 100), c(100, 200))
$day
[1] 90

$distance
[1] 10

$yest
[1] 146.486
# n=2, extrapolate to 90
f(c(70, 80), c(100, 200))
$day
[1] 90

$distance
[1] 10

$yest
[1] 146.9803

Now apply the function to each subject. Add the new points to the original plot. Keep the age variable in the one subject per record data table z.

z <- u[, c(list(age=age), f(day, y, id)), by=id]   # just use u[, f(day, y, id), ...] if don't need age
# Show distribution of distance between 90 and closest observed day
z[, table(distance)]
distance
 1  2  3  5  6  8  9 10 12 13 14 15 17 18 22 24 26 30 32 36 40 43 47 49 52 53 
 3  4  1 10  5 10  5 10  8  4  3  4  5  2  3  1  3  2  5  4  1  5  1  1  5  1 
54 58 69 70 71 
 1  1  3  1  3 
ggplot(u, aes(x=day, y=y)) +
  geom_line(aes(col=id), data=u) +
  geom_point(aes(col=id), data=u) +
  geom_line(aes(x=day, y, size=I(1.5)), data=w) +
  geom_point(aes(x=day, y=yest, col=id, shape=I('x'), size=I(3)), data=z) +
  guides(color='none')

13.2.3 Linear Interpolation to a Vector of Times

Suppose that 10 subjects have varying numbers of records, corresponding to assessment times t ranging from 0 to 2 years. We want to estimate the values of variable y over a regular sequence of values 0.25 years apart, for all subjects having at least two records at distinct times. Use linear interpolation, or linear extrapolation if one of the target times is outside the range of a subject’s data. It is advisable to specify the time sequence as consisting of points interior to [0, 2] so that the estimates at the end have a good chance to be symmetrically interpolated/extrapolated. For any value of t in the grid, set the calculated interpolated/extrapolated value to NA if there is no measurement within 0.25 years of t.

Create the test dataset and a function to do the calculations on a single subject.

set.seed(15)
n <- 10
w <- lapply(1 : n,
            function(i) { m <- sample(1 : 20, 1)
                          t <- sort(runif(m, 0, 2))
                          y <- abs(t - 1) + 0.5 * rnorm(m)
                          data.table(id=as.character(i), t, y)})
u <- rbindlist(w)
# Show distribution of number of points per subject
with(u, table(table(id)))
ggplot(u, aes(x=t, y=y)) +
  geom_line(aes(col=id, alpha=I(0.4)), data=u) +
  geom_point(aes(col=id), data=u) +
  guides(color='none')
1
Number of assessments for a subject is a random number between 1 and 20
2
Uniformly distributed assessment times in ascending order
3
True responses have means that are V-shaped in time
4
Setting id to a character variable will keep ggplot2 from trying to treat id as a continuous variable
5
Using a transparency of 0.4 makes it easier to read spaghetti plots
6
Prevent ggplot2 from creating a legend for id 


 1  5  6  8  9 12 16 17 
 1  1  1  1  1  2  1  2 
# Set target times to regularize to
times <- seq(0.25, 1.75, by=0.25)
g <- function(t, y) {
  i <- ! is.na(y)
  if(any(! i)) {
    t <- t[i]
    y <- y[i]
  }
  if(uniqueN(t) < 2) return(list(t=times, y=rep(NA_real_, length(times))))
  z <- approxExtrap(t, y, xout=times)$y
  # For each target time compute the number of measurements within 0.25
  # years of the target
  nclose <- sapply(times, function(x) sum(abs(t - x) <= 0.25))
  z[nclose < 1] <- NA_real_
  list(t=times, y=z)
}
1
g is a function of two vectors (all values for one subject) that returns a list, which makes data.table place the output into two variables with names given in list() (t and y)
2
uniqueN is in data.table and computes the number of distinct values of its argument. Remember that when you need NA placeholders in building a data.table you need to declare the storage mode for the NA. Here real means double precision floating point (16 digits of precision, the standard R non-integer numeric).
3
approxExtrap is in the Hmisc package. It extends the built-in R approx function to do linear extrapolation.

Test the function.

g(1, 1)
$t
[1] 0.25 0.50 0.75 1.00 1.25 1.50 1.75

$y
[1] NA NA NA NA NA NA NA
g(1:2, 11:12)
$t
[1] 0.25 0.50 0.75 1.00 1.25 1.50 1.75

$y
[1]    NA    NA 10.75 11.00 11.25    NA 11.75

Create a data table with the new variables, and plot the derived estimates along with the raw data. X’s are regularized y-values. X’s that are not touching lines represent extrapolations, and those on lines are interpolations.

z <- u[, g(t, y), by=id]
ggplot(u, aes(x=t, y=y)) +
  geom_line(aes(col=id), data=u) +
  geom_point(aes(col=id), data=u) +
  geom_point(aes(x=t, y=y, col=id, shape=I('x'), size=I(4)), data=z) +
  guides(color='none')

Look at raw data for the two Xs on the plot that are not touching lines at t=0.25 (id=7) and 0.5 (id=4). For id=4 the X marks an extrapolation to the left of a steep drop over the first two points. For id=7 there is also extrapolation to the left.

u[id %in% c(4,7)]
    id         t            y
 1:  4 0.5470506  0.651103767
 2:  4 0.6435926 -0.008838958
 3:  4 0.6618349  0.664723930
 4:  4 0.8096265  0.804353411
 5:  4 1.0037501  0.490364169
 6:  4 1.3605564  1.030634292
 7:  4 1.3618222  0.891685917
 8:  4 1.3620511  0.780965231
 9:  4 1.4502774 -0.676070873
10:  4 1.5483572 -0.117616105
11:  4 1.6356089  0.081489276
12:  4 1.6660358  0.372573268
13:  4 1.6981980  0.201474172
14:  4 1.8238231  1.344406932
15:  4 1.8530522  0.211624201
16:  4 1.8928997  0.920978736
17:  4 1.9928758  1.671926932
18:  7 0.3260752  0.938380892
19:  7 1.0025397  0.189611500
20:  7 1.1225979  0.635890259
21:  7 1.1586452  0.946132952
22:  7 1.3950600  0.753206290
23:  7 1.5650036  1.063503814
24:  7 1.8467943  1.307851168
25:  7 1.9586770  0.756841672
26:  7 1.9804584  0.825144644
    id         t            y

Let’s summarize the regularized data by computing the interpolated y at t=1, Gini’s mean difference, and mean absolute consecutive y difference for each subject. Also compute the number of non-missing regularized values.

g <- function(y) {
  n <- sum(! is.na(y))
  if(n < 2) return(list(n=n, y1=NA_real_, gmd=NA_real_, consec=NA_real_))
  list(n      = n,
       y1     = y[abs(times - 1.) < 0.001],
       gmd    = GiniMd(y, na.rm=TRUE),
       consec = mean(abs(y - Lag(y)), na.rm=TRUE))
}
g(c(1, 2, NA, 5, 8, 9, 10))
w <- z[, g(y), by=id]
w
1
Checking against integers such as times = 1 will not have a problem with code such as y[times == 1] but to work in general when values being checked for equality may not be exactly represented in base 2 it’s best to allow for a tolerance such as 0.001.
2
Lag is in the Hmisc package; by default it shifts the y vector back one observation 
$n
[1] 6

$y1
[1] 5

$gmd
[1] 4.6

$consec
[1] 1.5

    id n         y1       gmd    consec
 1:  1 6         NA 0.6785255 0.4052871
 2:  2 7  0.4282241 0.1502275 0.1544205
 3:  3 0         NA        NA        NA
 4:  4 6  0.4964298 0.5336070 0.6330032
 5:  5 7 -0.5053917 0.6432752 0.4290084
 6:  6 7  0.4744770 0.3438133 0.2189778
 7:  7 6  0.1924227 0.4040584 0.3270544
 8:  8 7 -0.1416566 0.5904388 0.5696528
 9:  9 7  0.1374026 0.4416730 0.3067097
10: 10 7 -0.1504149 0.6846669 0.5107083

13.3 Overlap Joins and Non-equi Merges

Section 12.2 covered simple inexact matching/merging. Now consider more complex tasks.

The foverlaps function in data.table provides an amazingly fast way to do complex overlap joins. Our first example is modified from an example in the help file for foverlaps. An annotation column is added to describe what happened.

d1 <- data.table(w     =.q(a, a, b, b, b),
                 start = c( 5, 10, 1, 25, 50),
                 end   = c(11, 20, 4, 52, 60))
d2 <- data.table(w     =.q(a, a, b),
                 start = c(1, 15,  1),
                 end   = c(4, 18, 55),
                 name  = .q(dog, cat, giraffe),
                 key   = .q(w, start, end))
f <- foverlaps(d1, d2, type="any")
ann <- c('no a overlap with d1 5-11 & d2 interval',
         'a 10-20 overlaps with a 16-18',
         'b 1-4 overlaps with b 1-55',
         'b 25-62 overlaps with b 1-55',
         'b 50-60 overlaps with b 1-55')
f[, annotation := ann]
f
   w start end    name i.start i.end                              annotation
1: a    NA  NA    <NA>       5    11 no a overlap with d1 5-11 & d2 interval
2: a    15  18     cat      10    20           a 10-20 overlaps with a 16-18
3: b     1  55 giraffe       1     4              b 1-4 overlaps with b 1-55
4: b     1  55 giraffe      25    52            b 25-62 overlaps with b 1-55
5: b     1  55 giraffe      50    60            b 50-60 overlaps with b 1-55
# Don't include record for non-match
foverlaps(d1, d2, type='any', nomatch=NULL)
   w start end    name i.start i.end
1: a    15  18     cat      10    20
2: b     1  55 giraffe       1     4
3: b     1  55 giraffe      25    52
4: b     1  55 giraffe      50    60
# Require the d1 interval to be within the d2 interval
foverlaps(d1, d2, type="within")
   w start end    name i.start i.end
1: a    NA  NA    <NA>       5    11
2: a    NA  NA    <NA>      10    20
3: b     1  55 giraffe       1     4
4: b     1  55 giraffe      25    52
5: b    NA  NA    <NA>      50    60
# Require the intervals to have the same starting point
foverlaps(d1, d2, type="start")
   w start end    name i.start i.end
1: a    NA  NA    <NA>       5    11
2: a    NA  NA    <NA>      10    20
3: b     1  55 giraffe       1     4
4: b    NA  NA    <NA>      25    52
5: b    NA  NA    <NA>      50    60

Now consider an example where there is an “events” dataset e with 0 or more rows per subject containing start (s) and end (e) times and a measurement x representing a daily dose of something given to the subject from s to e. The base dataset b has one record per subject with times c and d. Compute the total dose of drug received between c and d for the subject. This is done by finding all records in e for the subject such that the interval [c,d] has any overlap with the interval [s,e]. For each match compute the number of days in the interval [s,e] that are also in [c,d]. This is given by min(e,d) + 1 - max(c,s). Multiply this duration by x to get the total dose given in [c,d]. For multiple records with intervals touching [c,d] add these products.

base   <- data.table(id    = .q(a,b,c), low=10, hi=20)
events <- data.table(id    = .q(a,b,b,b,k),
                     start = c( 8,  7, 12, 19, 99),
                     end   = c( 9,  8, 14, 88, 99),
                     dose  = c(13, 17, 19, 23, 29))
setkey(base,   id, low,   hi)
setkey(events, id, start, end)
w <- foverlaps(base, events,
               by.x = .q(id, low,   hi),
               by.y = .q(id, start, end ),
               type = 'any', mult='all', nomatch=NA)

w[, elapsed := pmin(end, hi) + 1 - pmax(start, low)]
w[, .(total.dose = sum(dose * elapsed, na.rm=TRUE)), by=id]
   id total.dose
1:  a          0
2:  b        103
3:  c          0

Similar things are can be done with non-equi merges. For those you can require exact subject matches but allow inexact matches on other variables. The following example is modified from here. A medication dataset holds the start and end dates for a patient being on a treatment, and a second dataset visit has one record per subject ID per doctor visit. For each visit look up the drug in effect if there was one.

medication <-
  data.table(ID         = c( 1, 1, 2, 3, 3),
             medication = .q(a, b, a, a, b),
             start      = as.Date(c("2003-03-25","2006-04-27","2008-12-05",
                                    "2004-01-03","2005-09-18")),
             stop       = as.Date(c("2006-04-02","2012-02-03","2011-05-03",
                                    "2005-06-30","2010-07-12")),
             key        = 'ID')
medication
   ID medication      start       stop
1:  1          a 2003-03-25 2006-04-02
2:  1          b 2006-04-27 2012-02-03
3:  2          a 2008-12-05 2011-05-03
4:  3          a 2004-01-03 2005-06-30
5:  3          b 2005-09-18 2010-07-12
set.seed(123)
visit <- data.table(
  ID   = rep(1:3, 4),
  date = sample(seq(as.Date('2003-01-01'), as.Date('2013-01-01'), 1), 12),
  sbp  = round(rnorm(12, 120, 15)),
  key  = c('ID', 'date'))
visit
    ID       date sbp
 1:  1 2004-06-09 113
 2:  1 2006-06-06 147
 3:  1 2008-01-16 126
 4:  1 2009-09-28 127
 5:  2 2003-07-14 138
 6:  2 2006-02-15 122
 7:  2 2006-06-21 127
 8:  2 2009-11-15 101
 9:  3 2005-11-03  91
10:  3 2009-02-04 110
11:  3 2011-03-05 125
12:  3 2012-03-24 112
# Variables named in inequalities need to have variables in
# medication listed first
m <- medication[visit, on = .(ID, start <= date, stop > date)]
m
    ID medication      start       stop sbp
 1:  1          a 2004-06-09 2004-06-09 113
 2:  1          b 2006-06-06 2006-06-06 147
 3:  1          b 2008-01-16 2008-01-16 126
 4:  1          b 2009-09-28 2009-09-28 127
 5:  2       <NA> 2003-07-14 2003-07-14 138
 6:  2       <NA> 2006-02-15 2006-02-15 122
 7:  2       <NA> 2006-06-21 2006-06-21 127
 8:  2          a 2009-11-15 2009-11-15 101
 9:  3          b 2005-11-03 2005-11-03  91
10:  3          b 2009-02-04 2009-02-04 110
11:  3       <NA> 2011-03-05 2011-03-05 125
12:  3       <NA> 2012-03-24 2012-03-24 112
# start and stop dates are replaced with actual date of visit
# drop one of them and rename the other
m[, stop := NULL]
setnames(m, 'start', 'date')
m
    ID medication       date sbp
 1:  1          a 2004-06-09 113
 2:  1          b 2006-06-06 147
 3:  1          b 2008-01-16 126
 4:  1          b 2009-09-28 127
 5:  2       <NA> 2003-07-14 138
 6:  2       <NA> 2006-02-15 122
 7:  2       <NA> 2006-06-21 127
 8:  2          a 2009-11-15 101
 9:  3          b 2005-11-03  91
10:  3          b 2009-02-04 110
11:  3       <NA> 2011-03-05 125
12:  3       <NA> 2012-03-24 112