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R: how to visualize categorical data?

R: How to visualize change in binary/categorical data over time

  • >dput(data) structure(list(ID = c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3), Dx = c(1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1), Month = c(0, 6, 12, 18, 24, 0, 6, 12, 18, 24, 0, 6, 12, 18, 24), score = c(0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0)), .Names = c("ID", "Dx", "Month", "score"), row.names = c(NA, -15L), class = "data.frame") >data ID Dx Month score 1 1 1 0 0 2 1 1 6 0 3 1 1 12 0 4 1 1 18 1 5 1 1 24 1 6 2 1 0 1 7 2 1 6 1 8 2 2 12 1 9 2 2 18 0 10 2 2 24 1 11 3 1 0 0 12 3 1 6 0 13 3 1 12 0 14 3 1 18 0 15 3 1 24 0 Suppose I have the above data.frame. I have 3 patients (ID = 1, 2 or 3). Dx is the diagnosis (Dx = 1 is normal, = 2 is diseased). There is a month variable. And last but not least, is a test score variable. The participants' test score is binary, and it can change from 0 or 1 or revert back from 1 to 0. I am having trouble coming up with a way to visualize this data. I would like an informative graph that looks at: The trend of the participants' test scores over time. How that trend compares to the participants' diagnosis over time In my real dataset I have over 800 participants, so I do not want to construct 800 separate graphs ... I think the test score variable being binary really has me stumped. Any help would be appreciated.

  • Answer:

    Note: A lot of the following data manipulation needs to be done for part 2. Part 1 is less complex, and you can see it fit in below. Uses library(data.table) library(ggplot2) library(reshape2) To Compare First, change the Dx from 1 to 2 to 0 to 1 (Assuming that a 0 in score corresponds to a 1 in Dx) data$Dx <- data$Dx - 1 Now, create a matrix that returns a 1 for a 1 diagnosis with a 0 test, and a -1 for a 1 test with a 0 diagnosis. compare <- matrix(c(0,1,-1,0),ncol = 2,dimnames = list(c(0,1),c(0,1))) > compare 0 1 0 0 -1 1 1 0 Now, lets score every event. This simply looks up the matrix above for every entry in your matrix: data$calc <- diag(compare[as.character(data$Dx),as.character(data$score)]) *Note: This can be sped up for large matrices using matching, but it is a quick fix for smaller sets like yours To allow us to use data.table aggregation: data <- data.table(data) Now we need to create our variables: tograph <- melt(data[, list(ScoreTrend = sum(score)/.N, Type = sum(calc)/length(calc[calc != 0]), Measure = sum(abs(calc))), by = Month], id.vars = c("Month")) ScoreTrend: This calculates the proportion of positive scores in each month. Shows the trend of scores over time Type: Shows the proportion of -1 vs 1 over time. If this returns -1, all events were score = 1, diag = 0. If it returns 1, all events were diag = 1, score = 0. A zero would mean a balance between the two Measure: The raw number of incorrect events. We melt this data frame along month so that we can create a facet graph. If there are no incorrect events, we will get a NaN for Type. To set this to 0: tograph[value == NaN, value := 0] Finally, we can plot ggplot(tograph, aes(x = Month, y = value)) + geom_line() + facet_wrap(~variable, ncol = 1) We can now see, in one plot: The number of positive scores by month The proportion of under vs. over diagnosis The number of incorrect diagnoses.

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With ggplot2 you can make faceted plots with subplots for each patient (see my solution for dealing with the large number of plots below). An example visualization: library(ggplot2) ggplot(data, aes(x=Month, y=score, color=factor(Dx))) + geom_point(size=5) + scale_x_continuous(breaks=c(0,6,12,18,24)) + scale_color_discrete("Diagnosis",labels=c("normal","diseased")) + facet_grid(.~ID) + theme_bw() which gives: Including 800 patients in one plot might be a bit too much as already mentioned in the comments of the question. There are several solutions to this problem: Aggregate the data. Create patient subgroups and make a plot for each subgroup. Filter out all the patients who have never been ill. With regard to the last suggestion, you can do that with the following code (which I adapted from http://stackoverflow.com/a/24715757/2204410 to one of my own questions): deleteable <- with(data, ave(Dx, ID, FUN=function(x) all(x==1))) data2 <- data[deleteable==0,] You can use this as well for creating a new variable identifying patient who have been ill: data$neverill <- with(data, ave(Dx, ID, FUN=function(x) all(x==1))) Then you can for example aggregate the data with the several grouping variables (e.g. Month, neverill).

Jaap

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