Just a short note to help me remember the melt()-function.
Lets create some messy data:
ID <- 1:10 T1 <- runif(10,10,20) T2 <- runif(10,20,30) T3 <- runif(10,30,40) df <- data.frame(ID=ID, T1=T1, T2=T2, T3=T3)
This is heavily inspired by a practical problem a student came to us with. There is 10 different patients, at time = T1, there is a certain value measured on the patient. At time = T2 the same value is measured. And again at time = T3, where T1<T2<T3.
We would now like to plot the development of those values as a function of time (or just T1,T2 and T3).
How to do that?
Using reshape2, and making sure we have the tidyverse packages loaded:
clean <- melt(df, id.vars=c("ID"), value.names=c("T1", "T2", "T3")) head(clean)
## ID variable value ## 1 1 T1 13.93662 ## 2 2 T1 10.30468 ## 3 3 T1 18.14351 ## 4 4 T1 17.58294 ## 5 5 T1 18.13877 ## 6 6 T1 10.21993
Nice, we now have tidy data.
Note that melt() also takes a variable “variable.name”, in case we have a different sort of mess.
Now it is easy to plot:
plot <- ggplot(clean, aes(x=variable, y=value, group=ID)) + geom_line() plot