I have repeated measures for a large number of variables and about a hundred individuals.
These measures are repeated to assure reproducibility and are not longitudinal time points.
I want to provide summaries and/or plots for these variables, but any calculation across the whole column (even weighted on the number of measures per individual) would lose the important information of the intra-individual variance.
On the other hand, presenting grouped data for this many individuals is not realistic.
Here is a simulation on 9 individuals of the unsatisfying plots I have so far. Both are not scalable with a lot of individuals.
library(tidyverse) N1=9 #individuals N2=25 #measures #for each N1 individuals, take N2 values based on a specific mean and variance (both from a normal distribution) df = expand.grid(individual=factor(1:N1), measure=LETTERS[1:N2]) %>% arrange(individual) %>% group_by(individual) %>% mutate( base_mean = rnorm(1, 0, 50), base_var = abs(rnorm(1, 0, 10)), value = rnorm(n(), base_mean, base_var), ) %>% identity() #draw 1 boxplot with individuals as colors ggplot(df, aes(x="x", y=value)) + geom_boxplot() + geom_jitter(aes(color=individual), width=0.1, alpha=0.9)
#draw 1 boxplot per individual ggplot(df, aes(x=individual, y=value)) + geom_boxplot()
Created on 2021-09-18 by the reprex package (v2.0.0)
Is there a way to visualize or summarise the data on both intra- and inter-individual levels?