## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)

## -----------------------------------------------------------------------------
library(pcvr)
library(data.table) # for fread
library(ggplot2)
library(patchwork) # for easy ggplot manipulation/combination

## ----class.source="static-code", eval=F---------------------------------------
#  devtools::install_github("joshqsumner/pcvr", build_vignettes = TRUE)
#  library(pcvr)

## ----class.source="static-code", eval=F---------------------------------------
#  complicatedFunction("syntax") # do not run this style

## -----------------------------------------------------------------------------
1 + 1 # run this style

## ----class.source="simulated", eval=T-----------------------------------------
support <- seq(0, 1, 0.0001) # this style is simulated data
plot(support, dbeta(support, 5, 5), type = "l", main = "simulated example")

## ----class.source="simulated", eval = T---------------------------------------
set.seed(123)
d <- growthSim("logistic",
  n = 30, t = 25,
  params = list(
    "A" = c(140, 155, 150, 165, 180, 190, 175, 185, 200, 220, 210, 205),
    "B" = c(13, 11, 12, 11, 10, 11, 12, 13, 14, 12, 12, 13),
    "C" = c(3, 3.25, 3.5, 3.1, 2.9, 3.4, 3.75, 2.9, 3, 3.1, 3.25, 3.3)
  )
)
d$genotype <- ifelse(d$group %in% letters[1:3], "MM",
  ifelse(d$group %in% letters[4:6], "B73",
    ifelse(d$group %in% letters[7:9], "Mo17", "W605S")
  )
)
d$fertilizer <- ifelse(d$group %in% letters[seq(1, 12, 3)], 0,
  ifelse(d$group %in% letters[seq(2, 12, 3)], 50, 100)
)
colnames(d)[c(1, 3, 4)] <- c("barcode", "DAS", "area_cm2")
d$height_cm <- growthSim("monomolecular",
  n = 30, t = 25,
  params = list(
    "A" = c(
      25, 30, 35, 32, 34, 30,
      37, 36, 34, 33, 35, 38
    ),
    "B" = c(
      0.12, 0.08, 0.1, 0.11, 0.1,
      0.12, 0.07, 0.12, 0.11, 0.09, 0.08, 0.09
    )
  )
)$y
d$width_cm <- growthSim("power law",
  n = 30, t = 25,
  params = list(
    "A" = c(
      10, 14, 13, 11, 12, 13,
      10, 13, 14, 11, 14, 14
    ),
    "B" = c(
      1.05, 1.13, 1.17, 1.01, 1.2,
      1, 1.04, 1.07, 1.17, 1.04, 1.16, 1.17
    )
  )
)$y
d$hue_circular_mean_degrees <- growthSim("linear",
  n = 30, t = 25,
  params = list("A" = c(runif(12, 1, 3)))
)$y +
  round(runif(nrow(d), 50, 60))
sv_ag <- d

## ----warning = FALSE, message = FALSE-----------------------------------------
frem(sv_ag,
  des = c("genotype", "fertilizer"),
  phenotypes = c("area_cm2", "height_cm", "width_cm", "hue_circular_mean_degrees"),
  timeCol = "DAS", cor = TRUE, returnData = FALSE, combine = FALSE, markSingular = TRUE, time = NULL
)

## ----warning = FALSE, message = FALSE-----------------------------------------
frem(sv_ag,
  des = c("genotype", "fertilizer"),
  phenotypes = c("area_cm2", "height_cm", "width_cm"),
  timeCol = "DAS", cor = FALSE, returnData = FALSE, combine = FALSE, markSingular = FALSE, time = "all"
)

## -----------------------------------------------------------------------------
ggplot(sv_ag, aes(
  x = DAS, y = area_cm2, group = interaction(genotype, fertilizer, lex.order = TRUE),
  color = genotype
)) +
  facet_wrap(~ factor(fertilizer, levels = c("0", "50", "100"))) +
  geom_smooth(method = "loess", se = TRUE, fill = "gray90") +
  geom_line(aes(group = barcode), linewidth = 0.15) +
  labs(
    y = expression("Area" ~ "(cm"^2 ~ ")"),
    color = "Genotype"
  ) +
  guides(color = guide_legend(override.aes = list(linewidth = 5))) +
  pcv_theme() +
  theme(axis.text.x.bottom = element_text(angle = 0))

## -----------------------------------------------------------------------------
mo17_area <- sv_ag[sv_ag$genotype == "Mo17" & sv_ag$DAS > 18 & sv_ag$fertilizer == 100, "area_cm2"]
b73_area <- sv_ag[sv_ag$genotype == "B73" & sv_ag$DAS > 18 & sv_ag$fertilizer == 100, "area_cm2"]

area_res_t <- conjugate(s1 = mo17_area, s2 = b73_area, method = "t", plot = TRUE, rope_range = c(-5, 5))

## -----------------------------------------------------------------------------
rt <- relativeTolerance(sv_ag,
  phenotypes = c("area_cm2", "height_cm"),
  grouping = c("fertilizer", "genotype", "DAS"), control = "fertilizer", controlGroup = "100"
)

ggplot(
  rt[rt$phenotype == "area_cm2" & rt$DAS %in% c(10:12), ],
  aes(x = DAS, y = mu_rel, fill = interaction(fertilizer, genotype))
) +
  geom_col(position = "dodge") +
  geom_errorbar(aes(ymin = mu_rel - 1.96 * se_rel, ymax = mu_rel + 1.96 * se_rel),
    position = position_dodge(width = 0.9), width = 0.3
  ) +
  pcv_theme() +
  labs(y = "Relative Tolerance", fill = "Fertilizer\nand Genotype")

## -----------------------------------------------------------------------------
pd <- rt[rt$phenotype == "area_cm2" & rt$DAS %in% c(5:19) & rt$fertilizer == "0", ]
pd$upper_2se <- pd$mu_rel + 2 * pd$se_rel
pd$lower_2se <- pd$mu_rel - 2 * pd$se_rel

ggplot(pd, aes(x = DAS, y = mu_rel, fill = genotype)) +
  geom_col(position = "dodge") +
  geom_errorbar(aes(
    ymin = lower_2se, ymax = upper_2se,
    group = genotype
  ), position = "dodge") +
  pcv_theme() +
  labs(y = "Relative Tolerance")

## -----------------------------------------------------------------------------
cp <- cumulativePheno(sv_ag, phenotypes = c("area_cm2", "height_cm"), group = "barcode", timeCol = "DAS")

## -----------------------------------------------------------------------------
ggplot(cp, aes(x = DAS, y = area_cm2_csum, color = genotype, group = barcode)) +
  facet_wrap(~ factor(fertilizer, levels = c("0", "50", "100"))) +
  geom_line() +
  pcv_theme() +
  labs(
    y = expression("Cumulative Sum of Area" ~ "(cm"^2 ~ ")"),
    color = "Genotype"
  )

## ----class.source="simulated", class.output="simulated"-----------------------
simdf <- growthSim("logistic",
  n = 20, t = 25,
  params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
)
l <- ggplot(simdf, aes(time, y, group = interaction(group, id))) +
  geom_line(aes(color = group)) +
  labs(title = "Logistic") +
  theme_minimal() +
  theme(legend.position = "none")

simdf <- growthSim("gompertz",
  n = 20, t = 25,
  params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(0.2, 0.25))
)
g <- ggplot(simdf, aes(time, y, group = interaction(group, id))) +
  geom_line(aes(color = group)) +
  labs(title = "Gompertz") +
  theme_minimal() +
  theme(legend.position = "none")

simdf <- growthSim("monomolecular",
  n = 20, t = 25,
  params = list("A" = c(200, 160), "B" = c(0.08, 0.1))
)
m <- ggplot(simdf, aes(time, y, group = interaction(group, id))) +
  geom_line(aes(color = group)) +
  labs(title = "Monomolecular") +
  theme_minimal() +
  theme(legend.position = "none")

simdf <- growthSim("exponential",
  n = 20, t = 25,
  params = list("A" = c(15, 20), "B" = c(0.095, 0.095))
)
e <- ggplot(simdf, aes(time, y, group = interaction(group, id))) +
  geom_line(aes(color = group)) +
  labs(title = "Exponential") +
  theme_minimal() +
  theme(legend.position = "none")

simdf <- growthSim("linear", n = 20, t = 25, params = list("A" = c(1.1, 0.95)))
ln <- ggplot(simdf, aes(time, y, group = interaction(group, id))) +
  geom_line(aes(color = group)) +
  labs(title = "Linear") +
  theme_minimal() +
  theme(legend.position = "none")

simdf <- growthSim("power law", n = 20, t = 25, params = list("A" = c(16, 11), "B" = c(0.75, 0.7)))
pl <- ggplot(simdf, aes(time, y, group = interaction(group, id))) +
  geom_line(aes(color = group)) +
  labs(title = "Power Law") +
  theme_minimal() +
  theme(legend.position = "none")

(l + g + m) / (e + ln + pl)

## -----------------------------------------------------------------------------
priors <- list("A" = 130, "B" = 10, "C" = 0.2)
priorPlots <- plotPrior(priors)
priorPlots[[1]] / priorPlots[[2]] / priorPlots[[3]]

## -----------------------------------------------------------------------------
twoPriors <- list("A" = c(100, 130), "B" = c(6, 12), "C" = c(0.5, 0.25))
plotPrior(twoPriors, "gompertz", n = 100)[[1]]

## -----------------------------------------------------------------------------
sv_ag$group <- interaction(sv_ag$fertilizer, sv_ag$genotype)

## ----eval=FALSE---------------------------------------------------------------
#  library(brms)
#  library(cmdstanr)
#  cmdstanr::install_cmdstan()

## ----eval=FALSE---------------------------------------------------------------
#  ss <- growthSS(
#    model = "gompertz", form = area_cm2 ~ DAS | barcode / group, sigma = "spline", df = sv_ag,
#    start = list("A" = 130, "B" = 10, "C" = 0.5), type = "brms"
#  )

## -----------------------------------------------------------------------------
ggplot(sv_ag, aes(x = DAS, y = area_cm2, group = barcode, color = group)) +
  geom_line() +
  theme_minimal() +
  labs(
    y = expression("Area" ~ "(cm"^2 ~ ")"),
    color = "Genotype\nand Soil"
  )

## ----class.source="static-code", eval=FALSE-----------------------------------
#  fit <- fitGrowth(ss,
#    iter = 1000, cores = 2, chains = 2, backend = "cmdstanr",
#    control = list(adapt_delta = 0.999, max_treedepth = 20)
#  ) # options to increase performance

## ----eval=FALSE---------------------------------------------------------------
#  brmPlot(fit, form = area_cm2 ~ DAS | barcode / group, df = ss$df) +
#    labs(y = expression("Area" ~ "(cm"^2 ~ ")"))

## ----eval=FALSE---------------------------------------------------------------
#  brmViolin(fit, ss, ".../A_group0.B73 > 1.05") +
#    ggplot2::theme(axis.text.x.bottom = ggplot2::element_text(angle = 90))

## -----------------------------------------------------------------------------
ggplot(sv_ag[sv_ag$DAS == 18, ], aes(
  x = fertilizer, y = hue_circular_mean_degrees,
  fill = as.character(fertilizer)
)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(width = 0.05, size = 0.5) +
  scale_fill_manual(values = c(viridis::viridis(3, 1, 0.1)), breaks = c("0", "50", "100")) +
  pcv_theme() +
  theme(legend.position = "none") +
  facet_wrap(~genotype, scales = "free_x") +
  scale_x_discrete(limits = c("0", "50", "100")) +
  labs(y = "Hue Circular Mean (degrees)", x = "Soil and Genotype")

## -----------------------------------------------------------------------------
set.seed(123)
dists <- stats::setNames(lapply(runif(12, 50, 60), function(i) {
  list(mean = i, sd = 15)
}), rep("rnorm", 12))
d <- mvSim(dists,
  wide = TRUE, n_samples = 5,
  t = 25, model = "linear",
  params = list("A" = runif(12, 1, 3))
)
d$group <- sapply(sub(".*_", "", d$group), function(x) {
  letters[as.numeric(x)]
})
d$genotype <- ifelse(d$group %in% letters[1:3], "MM",
  ifelse(d$group %in% letters[4:6], "B73",
    ifelse(d$group %in% letters[7:9], "Mo17", "W605S")
  )
)
d$fertilizer <- ifelse(d$group %in% letters[seq(1, 12, 3)], 0,
  ifelse(d$group %in% letters[seq(2, 12, 3)], 50, 100)
)
colnames(d)[1] <- "DAS"
colnames(d) <- gsub("sim_", "hue_frequencies_", colnames(d))
hue_wide <- d

## ----message=FALSE------------------------------------------------------------
p <- pcv.joyplot(hue_wide[hue_wide$DAS %in% c(5, 10, 15), ],
  index = "hue_frequencies", group = c("fertilizer", "genotype"),
  y = "DAS", id = NULL
)
p + scale_fill_gradientn(colors = scales::hue_pal(l = 65)(360)) +
  scale_y_discrete(limits = c("5", "10", "15"))

## -----------------------------------------------------------------------------
mo17_sample <- hue_wide[
  hue_wide$genotype == "Mo17" & hue_wide$DAS > 18 & hue_wide$fertilizer == 100,
  grepl("hue_freq", colnames(hue_wide))
]
b73_sample <- hue_wide[
  hue_wide$genotype == "B73" & hue_wide$DAS > 18 & hue_wide$fertilizer == 100,
  grepl("hue_freq", colnames(hue_wide))
]

hue_res_ln <- conjugate(
  s1 = mo17_sample, s2 = b73_sample, method = "lognormal",
  plot = TRUE, rope_range = c(-10, 10), hypothesis = "equal"
)

## -----------------------------------------------------------------------------
pcadf(hue_wide, cols = "hue_frequencies", color = "genotype", returnData = FALSE) +
  facet_wrap(~ factor(fertilizer, levels = c("0", "50", "100")))

## ----class.source="simulated", class.output="simulated"-----------------------
set.seed(123)

simFreqs <- function(vec, group) {
  s1 <- hist(vec, breaks = seq(1, 181, 1), plot = FALSE)$counts
  s1d <- as.data.frame(cbind(data.frame(group), matrix(s1, nrow = 1)))
  colnames(s1d) <- c("group", paste0("sim_", 1:180))
  s1d
}

sim_df <- rbind(
  do.call(rbind, lapply(1:10, function(i) {
    simFreqs(rnorm(200, 50, 10), group = "normal")
  })),
  do.call(rbind, lapply(1:10, function(i) {
    simFreqs(rlnorm(200, log(30), 0.25), group = "lognormal")
  })),
  do.call(rbind, lapply(1:10, function(i) {
    simFreqs(c(rlnorm(125, log(15), 0.25), rnorm(75, 75, 5)), group = "bimodal")
  })),
  do.call(rbind, lapply(1:10, function(i) {
    simFreqs(c(rlnorm(100, log(15), 0.25), rnorm(50, 50, 5), rnorm(50, 90, 5)), group = "trimodal")
  })),
  do.call(rbind, lapply(1:10, function(i) {
    simFreqs(runif(200, 1, 180), group = "uniform")
  }))
)

sim_df_long <- as.data.frame(data.table::melt(data.table::as.data.table(sim_df), id.vars = "group"))
sim_df_long$bin <- as.numeric(sub("sim_", "", sim_df_long$variable))

ggplot(sim_df_long, aes(x = bin, y = value, fill = group), alpha = 0.25) +
  geom_col(position = "identity", show.legend = FALSE) +
  pcv_theme() +
  facet_wrap(~group)

## ----class.source="simulated", class.output="simulated"-----------------------
sim_emd <- pcv.emd(
  df = sim_df, cols = "sim_", reorder = c("group"),
  mat = FALSE, plot = TRUE, parallel = 1, raiseError = TRUE
)
sim_emd$plot

## ----class.source="simulated", class.output="simulated"-----------------------
n <- pcv.net(sim_emd$data, filter = 0.5)
net.plot(n, fill = "group")

## ----class.source="simulated", class.output="simulated"-----------------------
n <- pcv.net(sim_emd$data, filter = "0.5")
net.plot(n, fill = "group")

## ----eval=FALSE---------------------------------------------------------------
#  EMD <- pcv.emd(
#    df = hue_wide[hue_wide$DAS %in% c(5, 12, 19), ], cols = "hue_frequencies",
#    reorder = c("fertilizer", "genotype", "DAS"),
#    mat = FALSE, plot = TRUE, parallel = 12, raiseError = TRUE
#  )

## ----eval=FALSE, include=FALSE------------------------------------------------
#  head(EMD$data)
#  EMD$plot

## -----------------------------------------------------------------------------
hue_ag1 <- mv_ag(df = hue_wide, group = c("DAS", "genotype", "fertilizer"), n_per_group = 2)
dim(hue_ag1)

hue_ag2 <- mv_ag(hue_wide, group = c("DAS", "genotype", "fertilizer"), n_per_group = 1)
dim(hue_ag2)

## ----eval=FALSE---------------------------------------------------------------
#  set.seed(456)
#  net <- pcv.net(EMD$data, meta = c("fertilizer", "genotype", "DAS"), filter = 0.5)

## ----eval=FALSE---------------------------------------------------------------
#  net.plot(net, fill = "DAS", shape = "fertilizer", size = 2)