How does each patient change across repeated measurements, and does the cohort-level trend tell the same story? Spaghetti and trend plots answer those two levels of the longitudinal question without pretending they are the same.
You measured the same thing on the same patients more than once, and you want to see how it moves. That is the case for a spaghetti plot. Each patient becomes one line connecting their repeated measurements over time, and the tangle of lines (the “spaghetti” the name promises) gives you the shape of the cohort: where the values cluster, how much individual patients swing, whether the bundle drifts up or down as the years pass. A serial echo measurement (aortic valve gradient, valve area, regurgitation grade) is the natural candidate.
This chapter covers two related displays. The spaghetti plot, hv_spaghetti(), keeps every patient’s trajectory visible, so you see the spread as well as the trend. Its companion, hv_trends(), removes the individual lines, fits a LOESS curve to the raw observations, and overlays one annual summary point per group. Use it when the population-level pattern matters more than tracing individual patients.
Both return a bare ggplot you finish with scales, labels, and a house theme.
hv_spaghetti() expects long-format data: one row per measurement, with an id column identifying the patient, a time column, and the value being tracked. Optionally, a grouping column lets the smoother compare strata when add_smooth = TRUE. When colour_col is set, the individual trajectories and the smoother both take the group colour. sample_spaghetti_data() generates 150 patients with up to 6 observations each, split by a named proportion vector that stands in for a sex stratification. Build both the plain and the group-aware objects up front so the variants below can reuse them.
dta_sp <- sample_spaghetti_data(
n_patients = 150,
max_obs = 6,
groups = c(Female = 0.45, Male = 0.55),
seed = 42L
)
head(dta_sp) id time value group
1 1 0.44 22.12 Female
2 1 0.67 27.20 Female
3 1 0.91 29.24 Female
4 1 1.29 18.95 Female
5 1 1.96 24.00 Female
6 2 2.04 25.16 Female# Build S3 objects for reuse below
sp <- hv_spaghetti(dta_sp)
sp_col <- hv_spaghetti(dta_sp, colour_col = "group")hv_trends() works the other way: you hand it patient-level data and it computes the annual summary internally, so you do not pre-aggregate. The year_range and groups arguments set the study window and group structure.
Both spaghetti objects use the hv_spaghetti plot method. Inspecting their metadata confirms the patient identifier, observation count, and whether a grouping column was retained for the optional smoother before either object is plotted.
class(sp)[1] "hv_spaghetti" "hv_data" sp$meta$x_col
[1] "time"
$y_col
[1] "value"
$id_col
[1] "id"
$color_col
NULL
$colour_col
NULL
$n_subjects
[1] 150
$n_obs
[1] 594
$n_missing
[1] 0
attr(,"hv_both_spellings")
[1] TRUEsp_col$meta$x_col
[1] "time"
$y_col
[1] "value"
$id_col
[1] "id"
$color_col
[1] "group"
$colour_col
[1] "group"
$n_subjects
[1] 150
$n_obs
[1] 594
$n_missing
[1] 0
attr(,"hv_both_spellings")
[1] TRUEStart from the bare spaghetti panel so you can see what the constructor produced before any styling: one thin trajectory per patient over time, no colour, no axis limits, no theme.
p_sp <- plot(sp)
p_sp
Now set sensible axis limits and breaks with the usual scale layers, then add a theme. The y-axis here covers the observed range of AV mean gradient without compressing the trajectories into the lower half of the panel.
plot(sp) +
scale_x_continuous(breaks = seq(0, 5, 1)) +
scale_y_continuous(breaks = seq(0, 40, 10)) +
coord_cartesian(xlim = c(0, 5), ylim = c(0, 40)) +
labs(x = "Years", y = "AV Mean Gradient (mmHg)") +
theme_hv_manuscript(base_size = 12)
A spaghetti plot is read as a cloud first and individual lines second. Follow-up years are on the x-axis and AV mean gradient is on the y-axis; each line is one patient, so the number of observed trajectories at a time point is the effective denominator. Compare the bundle across time, but do not treat the descriptive paths as a treatment effect or assume that dropout is unrelated to the outcome. Look for:
When the bundle is too dense to read by eye, pass add_smooth = TRUE to overlay a LOESS trend line per group (locally weighted regression, a flexible curve that follows the data without assuming a straight line). The thin trajectories and the heavier smooths share each group’s colour, so the eye pairs every patient with their stratum’s trend.
plot(sp_col, add_smooth = TRUE) +
scale_colour_manual(
values = c(Female = "firebrick", Male = "steelblue"),
name = NULL
) +
guides(fill = "none") +
scale_x_continuous(breaks = seq(0, 5, 1)) +
scale_y_continuous(breaks = seq(0, 40, 10)) +
coord_cartesian(xlim = c(0, 5), ylim = c(0, 40)) +
labs(x = "Years", y = "AV Mean Gradient (mmHg)") +
theme_hv_manuscript(base_size = 12) +
theme(legend.position = "right")
The smoother is fitted separately within each sex stratum. It summarizes the observed trajectories, not within-patient causal change, and it can move when the mix of patients still under observation changes.
A plain spaghetti plot needs no legend because every line represents the same thing. When you add a LOESS overlay, use the constructor arguments to make the patient trajectories light and thin while the smooth carries the comparison. The caption names both layers, while line weight keeps the trend visually separate without adding a legend to a one-group panel.
plot(
sp,
add_smooth = TRUE,
line_colour = "grey70",
line_width = 0.3,
alpha = 0.4,
smooth_width = 1.3
) +
scale_x_continuous(breaks = seq(0, 5, 1)) +
scale_y_continuous(breaks = seq(0, 40, 10)) +
coord_cartesian(xlim = c(0, 5), ylim = c(0, 40)) +
labs(x = "Years", y = "AV Mean Gradient (mmHg)") +
theme_hv_manuscript(base_size = 12)
This layer comparison is carried by line weight and the caption, not colour alone. The LOESS curve is still a descriptive smoother and cannot distinguish true change from selective follow-up.
When the per-patient detail stops being the point, hv_trends() replaces the individual trajectories with a smoother plus one summary point per year. Subset to a single group and fit with group_col = NULL for a single series. The points are annual means of the continuous value column, but the LOESS curve is fitted to the raw rows in tr1$data, not to those means. Set the x-axis to the study window and let the y-axis span both layers (here roughly 20 to 70). As the pitfalls below repeat, setting limits tighter than the data, for instance c(0, 10), silently drops every point and line and leaves a blank panel. The c(0, 80) limits here comfortably contain the series.
dta_tr <- sample_trends_data(
n = 600,
year_range = c(1968L, 2000L),
groups = c("Group I", "Group II", "Group III", "Group IV")
)
head(dta_tr) year value group
1 1975 68.94 Group I
2 1973 58.54 Group I
3 1977 42.45 Group I
4 1994 42.06 Group I
5 1987 35.46 Group II
6 1992 27.59 Group IVone_grp <- dta_tr[dta_tr$group == "Group I", ]
tr1 <- hv_trends(one_grp, group_col = NULL)
class(tr1)[1] "hv_trends" "hv_data" tr1$meta$x_col
[1] "year"
$y_col
[1] "value"
$group_col
NULL
$summary_fn
[1] "mean"
$n_obs
[1] 162
$n_missing
[1] 0
$n_groups
[1] 1head(tr1$tables$summary) year value
1 1968 62.00167
2 1969 55.03000
3 1970 57.68000
4 1971 57.56000
5 1972 58.74167
6 1973 52.24000plot(tr1) +
scale_x_continuous(limits = c(1968, 2000), breaks = seq(1968, 2000, 4)) +
scale_y_continuous(limits = c(0, 80), breaks = seq(0, 80, 20)) +
labs(x = "Year", y = "Outcome") +
theme_hv_manuscript(base_size = 12)
Year is on the x-axis and the outcome is on the y-axis. Each point is the mean of the observations available in that year. The LOESS curve instead fits all raw observations, so years with more rows can exert more influence than years with fewer rows. Both layers describe the observed series; neither shows that calendar time caused the outcome to change.
When group_col is set, the constructor computes per-group annual means for the points and plot() fits a raw-observation smoother within each group. Pairing scale_colour_manual() with the house palette and scale_shape_manual() gives each group both a distinct colour and a distinct marker, so the figure stays readable when it is printed or photocopied in greyscale.
tr <- hv_trends(dta_tr)
tr$meta$x_col
[1] "year"
$y_col
[1] "value"
$group_col
[1] "group"
$summary_fn
[1] "mean"
$n_obs
[1] 600
$n_missing
[1] 0
$n_groups
[1] 4head(tr$tables$summary) year value group
Group I.1 1968 62.00167 Group I
Group I.2 1969 55.03000 Group I
Group I.3 1970 57.68000 Group I
Group I.4 1971 57.56000 Group I
Group I.5 1972 58.74167 Group I
Group I.6 1973 52.24000 Group Iplot(tr) +
scale_colour_manual(values = hv_ppt_palette("light"), name = "Group") +
scale_shape_manual(
values = c("Group I" = 15L, "Group II" = 19L,
"Group III" = 17L, "Group IV" = 18L),
name = "Group"
) +
scale_x_continuous(limits = c(1968, 2000), breaks = seq(1968, 2000, 4)) +
labs(x = "Year", y = "Outcome") +
theme_hv_manuscript(base_size = 12) +
theme(legend.position = "right")
Pass se = TRUE to ask geom_smooth() for its model-based uncertainty band around the LOESS fitted to raw observations, and use alpha to control the band’s opacity. This is not a confidence interval for the annual mean points and does not directly display the number of observations available in each year. Show yearly counts separately when that denominator matters.
plot(tr1, se = TRUE, alpha = 0.2) +
scale_x_continuous(limits = c(1968, 2000), breaks = seq(1968, 2000, 4)) +
labs(x = "Year", y = "Outcome") +
theme_hv_manuscript(base_size = 12)
Take the chosen longitudinal view to Finish and deliver the result for manuscript, poster, or slide output. State the follow-up denominator, summary function, smoother, and handling of dropout in the caption; those details determine what the trend can support.
scale_*_continuous(limits = ...) does not zoom, it filters: any point outside the limits is dropped before plotting. Set the limits tighter than the data, say c(0, 10) on a series that runs to 70, and the trend line silently vanishes, leaving an empty panel with no warning. When a panel comes back blank, check the limits against the data range first. Use coord_cartesian() when you want to zoom without discarding points.alpha, or switch to the trend curve, which is built for exactly this.se = TRUE, the band belongs to the raw-observation LOESS fit. It is not a confidence interval for each annual mean and does not reveal the yearly denominator. Inspect or report annual counts separately.