In bishun945/DAMATO: Data Management Tools

knitr::opts_chunk$set(
    message = FALSE,
    warning = FALSE,
    echo = FALSE,
    fig.width = 4,
    fig.height = 3,
    fig.align = 'center',
    comment = NA
)

# <--input_file-->
load("<replaceme>")

if(!require(dplyr)) {
  install.packages("dplyr")
  library(dplyr)
}

if(!require(reshape2)) {
  install.packages("reshape2")
}

if(!require(ggplot2)) {
  install.packages("ggplot2")
  library(ggplot2)
}

if(!require(FCMm)) {
  remotes::install_github("bishun945/FCMm")
  library(FCMm)
}

theme_set(theme_bw())

options(digits = 3)

The description of [base] parameters

dt_base <- tmp$dt$base
unit    <- tmp$dt$unit
unit_char <- unit$unit %>% setNames(., unit$name)

Water quality parameters

Chla

dt_base %>% 
  select(SampleID, SSD, Chla, PC, TSM, ISM, OSM, TN, TP) %>%
  ggplot(aes(x=Chla, y = PC)) + 
  geom_point() + 
  scale_x_log10() + 
  scale_y_log10() + 
  labs(
    x = sprintf("Chla %s", unit_char["chla"]),
    y = sprintf("PC %s", unit_char["藻蓝蛋白"])
  )

叶绿素a共测量了r length(dt_base$Chla)个样本，均值为r mean(dt_base$Chla, na.rm=TRUE) r unit_char["chla"]，最小值为r min(dt_base$Chla, na.rm=TRUE) r unit_char["chla"]，最大值为r max(dt_base$Chla, na.rm=TRUE) r unit_char["chla"]，方差为r sd(dt_base$Chla, na.rm=TRUE) r unit_char["chla"]。

Table of parameters

dt_base %>% 
  dplyr::select(SSD, Chla, PC, TSM, ISM, OSM, TN, TP) %>%
  apply(., 2, function(x) {
    x <- na.omit(x)
    c(min(x), max(x), mean(x), sd(x))
  }) %>% 
  t() %>% 
  as.data.frame() %>%
  cbind(Parm = rownames(.), .) %>%
  setNames(., c("Parm", "min", "max", "mean", "sd")) %>%
  knitr::kable(row.names = FALSE, format = "html")

Suspended matter concentration

dt_base %>% 
  select(SampleID, SSD, Chla, PC, TSM, ISM, OSM, TN, TP) %>%
  ggplot(aes(x=TSM, y=ISM)) + 
  geom_point() + 
  geom_abline(slope = 1, intercept = 0, linetype = 2) + 
  scale_x_log10() + 
  scale_y_log10() + 
  labs(
    x = sprintf("TSM %s", unit_char["TSM"]),
    y = sprintf("ISM %s", unit_char["ISM"])
  )

dt_base %>% 
  select(SampleID, SSD, Chla, PC, TSM, ISM, OSM, TN, TP) %>%
  ggplot(aes(x=TSM, y=SSD)) + 
  geom_point() + 
  scale_x_log10() + 
  scale_y_log10() + 
  labs(
    x = sprintf("TSM %s", unit_char["TSM"]),
    y = sprintf("SSD %s", unit_char["透明度"])
  )

Spectral analysis

Rrs colored by Chla

tmp$dt$Rrs %>%
  t() %>%
  as.data.frame() %>%
  setNames(., as.numeric(.[1, ])) %>%
  .[-1, ] %>%
  .[, dplyr::between(colnames(.), 400, 800)] %>%
  cbind(SampleID = rownames(.), .) %>%
  dplyr::mutate(SampleID = as.vector(SampleID)) %>%
  dplyr::left_join(., dt_base[, c("SampleID", "Chla")], by = "SampleID") %>%
  reshape2::melt(id = c("SampleID", "Chla")) %>%
  dplyr::mutate(variable = as.vector(variable) %>% as.numeric()) %>%
  ggplot(aes(x = variable, y = value, group = SampleID, color = Chla)) +
  geom_path() + 
  scale_color_viridis_c(trans = "log10") + 
  labs(x = "Wavelength [nm]", y = "Rrs [1/sr]")

Rrs colored by TSM

tmp$dt$Rrs %>%
  t() %>%
  as.data.frame() %>%
  setNames(., as.numeric(.[1, ])) %>%
  .[-1, ] %>%
  .[, dplyr::between(colnames(.), 400, 800)] %>%
  cbind(SampleID = rownames(.), .) %>%
  dplyr::mutate(SampleID = as.vector(SampleID)) %>%
  dplyr::left_join(., dt_base[, c("SampleID", "TSM")], by = "SampleID") %>%
  reshape2::melt(id = c("SampleID", "TSM")) %>%
  dplyr::mutate(variable = as.vector(variable) %>% as.numeric()) %>%
  ggplot(aes(x = variable, y = value, group = SampleID, color = TSM)) +
  geom_path() + 
  scale_color_viridis_c(trans = "log10") + 
  labs(x = "Wavelength [nm]", y = "Rrs [1/sr]")

Blend_result <- DAMATO:::Rrs_and_Chla(tmp)

OWT results

cluster <- Blend_result$Blend_result$res_FCM$cluster
tmp$dt$Rrs %>%
  t() %>%
  as.data.frame() %>%
  setNames(., as.numeric(.[1, ])) %>%
  .[-1, ] %>%
  .[, dplyr::between(colnames(.), 400, 800)] %>%
  FCMm::plot_spec_group(., cluster) + 
  labs(x = "Wavelength [nm]", y = "Rrs [1/sr]", color = "OWT")

tmp$dt$Rrs %>%
  t() %>%
  as.data.frame() %>%
  setNames(., as.numeric(.[1, ])) %>%
  .[-1, ] %>%
  .[, dplyr::between(colnames(.), 400, 800)] %>%
  {./trapz2(.)} %>%
  FCMm::plot_spec_group(., cluster) + 
  labs(x = "Wavelength [nm]", y = "Normalized Rrs", color = "OWT")

Blending Chla result

Blend_result$p_Chla + 
  labs(
    x = sprintf("Measured Chla %s", unit_char["chla"]),
    y = sprintf("Predicted Chla %s", unit_char["chla"])
  )

bishun945/DAMATO documentation built on Dec. 19, 2021, 9:48 a.m.