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.

``` r
library(tidyverse)
N1=9 #individuals
N2=25 #measures
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()

ggplot(df, aes(x="x", y=value)) + 
  geom_boxplot() + 
  geom_jitter(aes(color=individual), width=0.1, alpha=0.9)
```

![](https://i.sstatic.net/KTUDP.png)

``` r


ggplot(df, aes(x=individual, y=value)) + 
  geom_boxplot()
```

![](https://i.sstatic.net/xurED.png)

<sup>Created on 2021-09-18 by the [reprex package](https://reprex.tidyverse.org) (v2.0.0)</sup>


Is there a way to visualize or summarise the data on both intra- and inter-individual level?