An utility function to visualise the output from `repExplore`

.

- .data
Output from

`repExplore`

.- .by
Pass NA if you want to plot samples without grouping.

You can pass a character vector with one or several column names from ".meta" to group your data before plotting. In this case you should provide ".meta".

You can pass a character vector that exactly matches the number of samples in your data, each value should correspond to a sample's property. It will be used to group data based on the values provided. Note that in this case you should pass NA to ".meta".

- .meta
A metadata object. An R dataframe with sample names and their properties, such as age, serostatus or hla.

- .errorbars
A numeric vector of length two with quantiles for error bars on sectors. Disabled if ".errorbars.off" is TRUE.

- .errorbars.off
If TRUE then plot CI bars for distances between each group. Disabled if no group passed to the ".by" argument.

- .points
A logical value defining whether points will be visualised or not.

- .test
A logical vector whether statistical tests should be applied. See "Details" for more information.

- .signif.label.size
An integer value defining the size of text for p-value.

- ...
Not used here.

A ggplot2 object.

If data is grouped, then statistical tests for comparing means of groups will be performed, unless `.test = FALSE`

is supplied.
In case there are only two groups, the Wilcoxon rank sum test (https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test) is performed
(R function `wilcox.test`

with an argument `exact = FALSE`

) for testing if there is a difference in mean rank values between two groups.
In case there more than two groups, the Kruskal-Wallis test (https://en.wikipedia.org/wiki/Kruskal
A significant Kruskal-Wallis test indicates that at least one sample stochastically dominates one other sample.
Adjusted for multiple comparisons P-values are plotted on the top of groups.
P-value adjusting is done using the Holm method (https://en.wikipedia.org/wiki/Holm
You can execute the command `?p.adjust`

in the R console to see more.

```
data(immdata)
repExplore(immdata$data, "volume") %>% vis()
repExplore(immdata$data, "count") %>% vis()
repExplore(immdata$data, "len") %>% vis()
repExplore(immdata$data, "clones") %>% vis()
```