## ----include=FALSE------------------------------------------------------------
knitr::opts_chunk$set(comment = "", warning = FALSE, message = FALSE)

## -----------------------------------------------------------------------------
library(knitr)
library(ClustIRR)
library(ggplot2)
library(patchwork)
library(ggforce)
library(rBLAST)
theme_set(new = theme_bw(base_size = 10))

## ----include = FALSE----------------------------------------------------------
# only run code if blast+ is installed
run <- has_blast()

## ----echo = FALSE, eval = !run, results='asis'--------------------------------
cat("**Note: BLAST was not installed when this vignette was built!**")

## ----eval=run-----------------------------------------------------------------
# # System requirements
# #  The BLAST+ software needs to be installed on your system. Installation
# # instructions are available in this package's
# # [INSTALL](https://github.com/mhahsler/rBLAST/blob/devel/INSTALL) file and
# # at \url{https://www.ncbi.nlm.nih.gov/books/NBK569861/}.
# 
# # R needs to be able to find the executable. After installing the software,
# # try in R
# Sys.which("blastp")
# 
# # If the command returns "" instead of the path to the executable,
# # then you need to set the environment variable called PATH. In R
# Sys.setenv(PATH = paste(Sys.getenv("PATH"),
#    "path_to_your_BLAST_installation", sep=.Platform$path.sep))

## ----eval=run-----------------------------------------------------------------
# data("D2", package = "ClustIRR")

## ----eval=run-----------------------------------------------------------------
# cl <- clustirr(s = D2, control = list(blast_gmi = 0.8))

## ----eval=run-----------------------------------------------------------------
# gcd <- detect_communities(graph = cl$graph,
#                           algorithm = "leiden",
#                           metric = "average",
#                           resolution = 1,
#                           iterations = 100,
#                           chains = c("CDR3a", "CDR3b"))

## ----eval=run-----------------------------------------------------------------
# dim(gcd$community_occupancy_matrix)

## ----fig.width=8, fig.height=9, eval=run--------------------------------------
# honeycomb <- get_honeycombs(com = gcd$community_occupancy_matrix)
# wrap_plots(honeycomb, nrow = 5, ncol = 3)+
#     plot_annotation(tag_levels = 'A')

## ----eval=run-----------------------------------------------------------------
# d <- dco(community_occupancy_matrix = gcd$community_occupancy_matrix,
#          groups = c(1, 1, 1, 2, 2, 2),
#          mcmc_control = list(mcmc_warmup = 300,
#                              mcmc_iter = 600,
#                              mcmc_chains = 2,
#                              mcmc_cores = 1,
#                              mcmc_algorithm = "NUTS",
#                              adapt_delta = 0.9,
#                              max_treedepth = 10))

## ----fig.width=6, fig.height=3, eval=run--------------------------------------
# ggplot(data = d$posterior_summary$beta)+
#     geom_sina(aes(x = sample, y = mean))|
#     ggplot(data = d$posterior_summary$beta_mu)+
#     geom_sina(aes(x = as.character(g), y = mean))

## ----fig.width=7, fig.height=3, eval=run--------------------------------------
# ggplot(data = d$posterior_summary$epsilon)+
#     facet_wrap(facets = ~contrast, ncol = 2)+
#     geom_errorbar(aes(x = community, y = mean, ymin = L95, ymax = H95),
#                   col = "lightgray", width = 0)+
#     geom_point(aes(x = community, y = mean), shape = 21, fill = "black",
#                stroke = 0.4, col = "white", size = 1.25)+
#     theme(legend.position = "top")+
#     ylab(label = expression(delta))+
#     scale_x_continuous(expand = c(0,0))