How to build large time series (thousands of times of administration / dose of medication)?

Question

I'm trying to think about how one drug is prescribed in the hospital. In this dummy database, I have 1000 contacts with the patient after 2017/01/01.

The purpose of the construction is to see the pattern of administration of this drug: is it given more often / with a high dose closer to the time of administration, discharge, or in the middle of the patient’s stay.

#Get_random_dates that we will use multiple times gen_random_dates <- function(N, st, et) { st <- as.POSIXct(as.Date(st)) et <- as.POSIXct(as.Date(et)) dt <- as.numeric(difftime(et,st,unit="sec")) ev <- runif(N, 0, dt) rt <- st + ev return(rt) } #Generate admission and discharge dates admission <- gen_random_dates(1000, "2017/01/01", "2017/01/10") discharge <- gen_random_dates(1000, "2017/01/11", "2017/01/20") patient <- sort(sample(1:1000, 1000)) patient_data <- data.frame(patient_ID = patient, admission_date = admission, discharge_date = discharge) #Grow the database patient_data <- patient_data[sort(sample(1000, 100000, replace=TRUE)), ] #Medication admin date and dose patient_data$admin_date <- gen_random_dates(100000, patient_data$admission_date, patient_data$discharge_date) patient_data$admin_dose <- abs(as.integer(rnorm(100000, 50, 100))) 

I tried this ggplot function but it did not help me render the template.

 ggplot(patient_data, aes(x = admin_date, y = admin_dose)) + xlab("Use of Drug in Patient Encounters") + ylab("Dose (mg)") + geom_jitter() 

ggplot

Answer 1

If the browser is an acceptable target, one option is to try ggplotly , which allows panning / zooming, useful with a timeline next to a lot of data. (Disclaimer, I'm a supporter of plotly.js .) Other than that, there is a regular R API for plotly.js . Plotly has graphs that can render many dots or lines, not only due to zooming / panning, but also in some types of scenes WebGL support, which can be much faster .

Answer 2

I would suggest using faces to view multiple patients at the same time. It does not scale well for thousands of patients, but it can help you look at 10-20 at a time. ggplotly works fine with faces.

 ggplot(patient_data, aes(x = admin_date, y = admin_dose)) + xlab("Use of Drug in Patient Encounters") + ylab("Dose (mg)") + geom_jitter() + facet_wrap(~patient)