In leppott/ContDataSumViz: Create Visualizations of Continuous Monitoring Data

`r child_env$p_name`

knitr::opts_chunk$set(echo = FALSE)

# Cannot label chunks in child
# Produces dups when re-use the same file with different parameters
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# DT::datatable and plotly objects can only have one per chunk
# this is why they don't render when generate them in a loop
# OK if use a Parent-Child set of RMD files

# 3. Missing Data Info, by Parameter

# Setup---
i <- ContData.env$myNames.DataFields[child_env$p_num]
i.num <- match(i,myParameters)
myTitle.Sub <- myParameters.Lab[i.num]

i.flag <- paste(ContData.env$myName.Flag, i, sep = ".")

# Title----
cat("### ", myTitle.Sub, sep = "")
cat("\n\n")

# Flags
cap <- "Number of Records, flags"
myTable <- addmargins(table(data.import[, i.flag]
                           , useNA = "ifany")
                     ,1)
print(knitr::kable(t(as.matrix(myTable))
                   , format = "markdown"
                   , caption = cap))
cat("\n\n")

# Num Records, year/month
cap <- "Number of Records, by year and month (with totals)"
myTable <- addmargins(table(data.import[,ContData.env$myName.Yr]
                          ,data.import[,ContData.env$myName.Mo]
                          , useNA = "ifany"))
print(knitr::kable(myTable
                   , format = "markdown"
                   , row.names = TRUE
                   , caption = cap))
cat("\n\n")

# Plot
df_base <- data.import %>%
  dplyr::filter(!!as.name(i.flag) == "P") %>%
  dplyr::group_by(!!as.name(ContData.env$myName.MoDa)
                  , !!as.name(ContData.env$myName.Yr)) %>%
  dplyr::summarise(n = dplyr::n(), .groups = "drop")
# munge
df_base$MonthDay <- sprintf("%04d", df_base$MonthDay)
# change class
df_base$MonthDay <- as.character(df_base$MonthDay)
df_base$Year <- as.character(df_base$Year)

# Plot, heat map
str_title_hm <- paste0("Records with 'P'assing flags, ", mySiteID, ", ", i)
cap <- "Records marked as S, X, or NA will show as missing."
p_hm <- ggplot2::ggplot(df_base
                      , ggplot2::aes(x = !!as.name(ContData.env$myName.Yr)
                                  , y = !!as.name(ContData.env$myName.MoDa)
                                  , fill = n)) +
  ggplot2::geom_tile() +
  ggplot2::scale_fill_gradient(low = "orange", high = "dark green") +
  ggplot2::labs(title = str_title_hm
                , caption = cap) +
  ggplot2::scale_y_discrete(limits = rev)
  # without plotly use
  #scale_y_discrete(limits = rev, guide = ggplot2::guide_axis(check.overlap = TRUE))

# Map, Display ----
cat("Plot is interactive.  Zoom in and y-axis will become visible.\n\n")
plotly::ggplotly(p_hm)
cat("\n\n")

# Dates, Missing Data

cat("The table below has all the sample dates with a number of records different
from the expected (", records.expected, "). 'N' is total number of records for 
the day.")
# table of dates with missing data
df_missing <- df_base %>%
  dplyr::filter(n != records.expected) %>%
  dplyr::select(Year, MonthDay, n) %>%
  dplyr::arrange(Year, MonthDay)
cap <- paste0("Dates with Potential Missing Records (expected = "
            , records.expected
            , ").")
DT::datatable(df_missing
              , filter = "top"
              , caption = cap
              , options = list(scrollX = TRUE
                               , scrollY = TRUE
                               , pageLength = 30
                               , lengthMenu = c(15, 30, 60, 90, 120, 180, 366)
                               , autoWidth = TRUE
                               ))
#~~~~~
# should be ok with just the above
# leave in example code below
#~~~

# 
# 
# cat("Number of records by day and month (with totals)")
# cat("\n")
# myTable <- addmargins(table(data.import[,ContData.env$myName.Day]
#                           ,data.import[,ContData.env$myName.Mo]
#                           , useNA = "ifany"))
# #table, kable (static)
# #print(knitr::kable(myTable, format="markdown", row.names=TRUE))
# 
# # table, DT (interactive)
# DT::datatable(as.data.frame.matrix(myTable)
#             , filter = "top"
#             , caption = "Records by day and month"
#             , options = list(scrollX = TRUE
#                              , scrollY = TRUE
#                              , lengthMenu = c(10, 20, 32)
#                              , autoWidth = TRUE
#                              ))
# 
# 
# 
# cat("\n\n") 
# #

# #
# 
# #~~~~~~~~~~~~
# # New, 20220616
# # OTHER
# 
# 
# # Pivot
# df_plot <- tidyr::pivot_wider(df_base
#                      , names_from = ContData.env$myName.Yr
#                      , values_from = "n")
# 
# cat("\n\n")
# #
# 
# # table, DT (interactive)
# # add color code (https://rstudio.github.io/DT/010-style.html)
# brks <- quantile(df_plot[, -1], probs = seq(.05, .95, .05), na.rm = TRUE)
# clrs <- round(seq(255, 40, length.out = length(brks) + 1), 0) %>%
#               {paste0("rgb(255,", ., ",", ., ")")}
# 
# 
# cat <- "Records by month-day and year"
# print(knitr::kable(df_plot, caption = cat))
# 
# DT::datatable(df_plot
#             , filter = "top"
#             , caption = cat
#             , options = list(scrollX = TRUE
#                              , scrollY = TRUE
#                              , pageLength = 30
#                              , lengthMenu = c(15, 30, 60, 90, 120, 180, 366)
#                              , autoWidth = TRUE
#                              )) 
# # coloring doesn't look right
# # %>%
# #   formatStyle(names(df_plot)[-1]
# #                , backgroundColor = styleInterval(brks, clrs))
# # 
# cat("\n\n")