Visualising time series of symptom scores (PANSS)
Having just started a new project relating to clinical state/signatures, I need some quick ways of visualing e.g. PANSS scores measured on multiple time points. Here's the resulting R code in case it's helpful for anyone else with an interest in the same. In case it's not obvious, I like heatmap representations.
Sample Data 1 : CSV of one person's PANSS scores measured over 9 time points.
Sample Data 2 : a second person's PANSS scores
The R code - place this and the example data above in your working directory
You'll need to download the example CSV files, and source the R code in your working directory
# Uncomment and modify according to where you're working directory is
# source('visualisePANSSFunctions.R')
d1 <- read.csv('example_PANSS_data1.csv')
d2 <- read.csv('example_PANSS_data2.csv')| P1 | P2 | P3 | P4 | P5 | P6 | P7 | N1 | N2 | N3 | N4 | N5 | N6 | N7 | G1 | G2 | G3 | G4 | G5 | G6 | G7 | G8 | G9 | G10 | G11 | G12 | G13 | G14 | G15 | G16 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 1 | 1 | 1 | 1 | 4 | 2 | 4 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 3 | 1 | 3 | 1 | 3 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 2 |
| 5 | 3 | 4 | 3 | 5 | 4 | 2 | 4 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 4 | 1 | 2 | 1 | 2 | 1 | 1 | 4 | 1 | 1 | 1 | 2 | 1 | 1 | 2 |
| 3 | 1 | 1 | 2 | 1 | 2 | 1 | 4 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 4 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 |
| 4 | 2 | 1 | 3 | 2 | 3 | 3 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 2 | 4 | 1 | 3 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 3 | 1 | 1 |
| 3 | 1 | 1 | 2 | 1 | 2 | 1 | 3 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 |
| 3 | 1 | 2 | 1 | 4 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 2 |
| 3 | 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 3 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 |
| 3 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 3 | 2 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 1 |
Visualise with Relative Scale
The lowest and highest scores are scaled 'within subject'
p1.rel <- HeatMapPANSS( d1, absoluteScale = FALSE )
(p1.rel)
Visualise with Absolute Scale
In contrast to relative, red always represents highest score (7) and green the lowest (1)
p1.abs <- HeatMapPANSS( d2, absoluteScale = TRUE )
(p1.abs)
The absolute scale has utility when comparing two sets of data
p1.abs <- HeatMapPANSS( d1, absoluteScale = TRUE)
p2.abs <- HeatMapPANSS( d2, absoluteScale = TRUE)
grid.arrange( p1.abs, p2.abs, ncol = 2 )
Visualise Change in Scores
Sometimes, it's useful to see how the scores change over time (essentially, the first derivative of scores). For the two people above, compare side-by-side their changes:
p1.delta <- HeatMapChangePANSS( d1 )$plot
p2.delta <- HeatMapChangePANSS( d2 )$plot
grid.arrange( p1.delta, p2.delta, ncol = 2)