R/joinStandData.R

In KateMMiller/forestNETN: Package for importing, joining and querying NETN forest database tables

#' @include joinLocEvent.R
#'
#' @importFrom dplyr arrange case_when group_by filter full_join left_join mutate select summarize
#' @importFrom magrittr %>%
#' @importFrom stringr str_pad
#' @importFrom tidyr pivot_wider
#'
#' @title joinStandData: compile stand data
#'
#' @description This function combines stand-level data for each plot, including cover by strata,
#' earthworms presence/absence, plot slope, canopy cover, etc. Must run importData first.
#'
#' @param park Combine data from all parks or one or more parks at a time. Valid inputs:
#' \describe{
#' \item{"all"}{Includes all parks in the network}
#' \item{"ACAD"}{Acadia NP only}
#' \item{"MABI"}{Marsh-Billings-Rockefeller NHP only}
#' \item{"MIMA"}{Minute Man NHP only}
#' \item{"MORR"}{Morristown NHP only}
#' \item{"ROVA"}{Roosevelt-Vanderbilt NHS only}
#' \item{"SAGA"}{Saint-Gaudens NHS only}
#' \item{"SARA"}{Saratoga NHP only}
#' \item{"WEFA"}{Weir Farm NHS only}}
#'
#' @param from Year to start analysis, ranging from 2006 to current year
#' @param to Year to stop analysis, ranging from 2006 to current year
#'
#' @param QAQC Allows you to remove or include QAQC events.
#' \describe{
#' \item{FALSE}{Default. Only returns visits that are not QAQC visits}
#' \item{TRUE}{Returns all visits, including QAQC visits}}
#'
#' @param locType Allows you to only include plots that are part of the GRTS sample design or include all plots, such as deer exclosures
#' \describe{
#' \item{"VS"}{Only include plots that are part of the Vital Signs GRTS sample design}
#' \item{"all"}{Include all plots, such as plots in deer exclosures or test plots.}}
#'
#' @param eventType Allows you to include only complete sampling events or all sampling events
#' \describe{
#' \item{"complete"}{Default. Only include sampling events for a plot that are complete.}
#' \item{"all}{Include all plot events with a record in tblCOMN.Event, including plots missing most of the data
#' associated with that event (eg ACAD-029.2010). This feature is currently hard-coded in the function.}}
#'
#' @param panels Allows you to select individual panels from 1 to 4. Default is all 4 panels (1:4).
#' If more than one panel is selected, specify by c(1, 3), for example.
#'
#' @param output Allows you to return all columns or just the most important columns for analysis. Valid
#' inputs are "short" and "verbose".
#'
#' @param ... Other arguments passed to function.
#'
#' @return returns a dataframe with stand data attached to location and event data. Field names starting with "Pct" are midpoints
#' between cover class ranges (e.g., 62.5 is the midpoint for 50-75%). Field names starting with "Txt" define the cover classes.
#'
#' @examples
#' \dontrun{
#' importData()
#' # import 4 years of MABI stand data
#' stand_df <- joinStandData(park = 'MABI', from = 2015, to = 2019)
#'
#' # import all visits, including QAQC, from 2019 in ACAD. Only return important data fields.
#' acad_stand <- joinStandData(park = ACAD, from = 2019, to = 2019, QAQC = TRUE, output = 'short')
#' }
#'
#' @export
#'
#------------------------
# Join stand data
#------------------------
joinStandData <- function(park = 'all', QAQC = FALSE, locType = c('VS', 'all'), panels = 1:4,
                          from = 2006, to = as.numeric(format(Sys.Date(), "%Y")),
                          eventType = c('complete', 'all'),
                          output = 'verbose', ...){

    # Match args and class
    park <- match.arg(park, several.ok = TRUE,
                      c("all", "ACAD", "MABI", "MIMA", "MORR", "ROVA", "SAGA", "SARA", "WEFA"))
    stopifnot(class(from) == "numeric", from >= 2006)
    stopifnot(class(to) == "numeric", to >= 2006)
    locType <- match.arg(locType)
    eventType <- match.arg(eventType)
    stopifnot(class(QAQC) == 'logical')
    stopifnot(panels %in% c(1, 2, 3, 4))
    output <- match.arg(output, c("short", "verbose"))

    env <- if(exists("VIEWS_NETN")){VIEWS_NETN} else {.GlobalEnv}

    # import the Stand Data views
    tryCatch(standinfo <- subset(get("StandInfoPhotos_NETN", envir = env),
                                 select = c(Plot_Name, PlotID, EventID, CrownClosureCode,
                                            CrownClosureLabel, StandStructureCode, StandStructureLabel,
                                            DeerBrowseCode, WaterPlotCode, WaterPlotLabel, MicrotopographyCode,
                                            EarthwormCode, WeatherLabel, PhotoNotes, StandNotes)),
             error = function(e){stop("StandInfoPhotos_NETN view not found. Please import view.")}
    )

    tryCatch(pstrata <- subset(get("StandPlantCoverStrata_NETN", envir = env),
                               select = c(Plot_Name, PlotID, EventID, StrataCode, CoverClassLabel)),
             error = function(e){stop("StandPlantCoverStrata_NETN view not found. Please import view.")}
    )

    tryCatch(ffloor <- subset(get("StandForestFloor_NETN", envir = env),
                              select = c(Plot_Name, PlotID, EventID, ForestFloorCode, CoverClassLabel)),
             error = function(e){stop("StandForestFloor_NETN view not found. Please import view.")}
    )

    tryCatch(treeht <- subset(get("StandTreeHeights_NETN", envir = env),
                              select = c(Plot_Name, PlotID, EventID, CrownClassCode, CrownClassLabel,
                                         TagCode, Height)),
             error = function(e){stop("StandTreeHeights_NETN view not found. Please import view.")}
    )

    tryCatch(slopes <- subset(get("StandSlopes_NETN", envir = env),
                              select = c(Plot_Name, PlotID, EventID, IsQAQC, SampleYear, PlotSlope)),
             error = function(e){stop("StandSlopes_NETN view not found. Please import view.")}
    )

    # standinfo comes in as 1 row per visit, so don't need to reshape, but need to fix cover midpoints and rename
    # to match previous package names

    standinfo <- standinfo %>% mutate(Stand_Structure = case_when(StandStructureLabel == "PM" ~ "Permanently missing",
                                                                  StandStructureLabel == "EA" ~ "Even-aged",
                                                                  StandStructureLabel == "ES" ~ "Early successional",
                                                                  StandStructureLabel == "M" ~ "Mosaic",
                                                                  StandStructureLabel == "MA" ~ "Multi-aged",
                                                                  StandStructureLabel == "W" ~ "Woodland (ACAD only)",
                                                                  TRUE ~ NA_character_),
                                      Stand_Structure_Code = ifelse(StandStructureCode == "PM",
                                                                    NA, StandStructureCode),
                                      Pct_Crown_Closure = case_when(CrownClosureLabel == "<10%" ~ 5,
                                                                    CrownClosureLabel == "10-25%" ~ 17.5,
                                                                    CrownClosureLabel == "25-50%" ~ 37.5,
                                                                    CrownClosureLabel == "50-75%" ~ 62.5,
                                                                    CrownClosureLabel == "75-100%" ~ 87.5,
                                                                    CrownClosureLabel == "Permanently Missing" ~ NA_real_,
                                                                    TRUE ~ NA_real_),
                                      Deer_Browse_Index = case_when(DeerBrowseCode == "1" ~ 1,
                                                                    DeerBrowseCode == "2" ~ 2,
                                                                    DeerBrowseCode == "3" ~ 3,
                                                                    DeerBrowseCode == "4" ~ 4,
                                                                    DeerBrowseCode %in% c("5") ~ 5,
                                                                    DeerBrowseCode %in% c("NC", "PM") ~ NA_real_,
                                                                    TRUE ~ NA_real_),
                                      Earthworms = ifelse(EarthwormCode == "PM", NA, EarthwormCode),
                                      Microtopography = ifelse(MicrotopographyCode == "PM", NA, MicrotopographyCode)) %>%
                               select(Plot_Name, PlotID, EventID, Stand_Structure, Stand_Structure_Code,
                                      Pct_Crown_Closure, CrownClosureCode, CrownClosureLabel, Deer_Browse_Index, Microtopography,
                                      Earthworms, WaterPlotCode, WaterPlotLabel, WeatherLabel, PhotoNotes, StandNotes)

    old_scol <- c("CrownClosureLabel", "WaterPlotCode", "WaterPlotLabel", "WeatherLabel")
    new_scol <- c("Txt_Crown_Closure", "Water_on_Plot_Code", "Water_on_Plot", "Weather_Photo")

    names(standinfo)[match(old_scol, names(standinfo))] <- new_scol # change col names to match prev. analyses

    # reshape pstrata and ffloor so 1 row per event and add midpoint cover fields
    # pstrata- had to reshape in 2 steps, so PMs didn't get converted to NA in teh txt fields

    pstrata_pct_wide <- pstrata %>% mutate(Pct = case_when(CoverClassLabel == "0%" ~ 0,
                                                           CoverClassLabel == "1-5%" ~ 3,
                                                           CoverClassLabel == "5-25%" ~ 15,
                                                           CoverClassLabel == "25-50%" ~ 37.5,
                                                           CoverClassLabel == "50-75%" ~ 62.5,
                                                           CoverClassLabel == "75-95%" ~ 85,
                                                           CoverClassLabel == "95-100%" ~ 97.5,
                                                           CoverClassLabel == "Permanently Missing" ~ NA_real_,
                                                           TRUE ~ NA_real_)) %>%
                                select(-CoverClassLabel) %>%
                                pivot_wider(names_from = StrataCode, values_from = Pct,
                                            values_fill = NA) %>%
                                select(Plot_Name, PlotID, EventID, G, MU, HU)

    old_pcol <- c("G", "MU", "HU")
    new_pcolp <- c("Pct_Understory_Low", "Pct_Understory_Mid", "Pct_Understory_High")
    names(pstrata_pct_wide)[match(old_pcol, names(pstrata_pct_wide))] <- new_pcolp # change col names to match prev. analyses

    pstrata_txt_wide <- pstrata %>% select(Plot_Name, PlotID, EventID, StrataCode, CoverClassLabel) %>%
                                    pivot_wider(names_from = StrataCode, values_from = CoverClassLabel) %>%
                                    select(Plot_Name:HU)

    new_pcolt <- c("Txt_Understory_Low", "Txt_Understory_Mid", "Txt_Understory_High")
    names(pstrata_txt_wide)[match(old_pcol, names(pstrata_txt_wide))] <- new_pcolt # change col names to match prev. analyses

    pstrata_wide <- full_join(pstrata_pct_wide, pstrata_txt_wide,
                          by = intersect(names(pstrata_pct_wide), names(pstrata_txt_wide)))

    pstrata_wide <- pstrata_wide[,c("Plot_Name", "PlotID", "EventID",
                                    "Pct_Understory_Low", "Txt_Understory_Low",
                                    "Pct_Understory_Mid", "Txt_Understory_Mid",
                                    "Pct_Understory_High", "Txt_Understory_High")]

    # forest floor
    ffloor_pct_wide <- ffloor %>% mutate(Pct = case_when(CoverClassLabel == "0%" ~ 0,
                                                         CoverClassLabel == "1-5%" ~ 3,
                                                         CoverClassLabel == "5-25%" ~ 15,
                                                         CoverClassLabel == "25-50%" ~ 37.5,
                                                         CoverClassLabel == "50-75%" ~ 62.5,
                                                         CoverClassLabel == "75-95%" ~ 85,
                                                         CoverClassLabel == "95-100%" ~ 97.5,
                                                         CoverClassLabel %in% c("Not Collected",
                                                                                "Permanently Missing") ~ NA_real_,
                                                         TRUE ~ NA_real_)) %>%
                                  select(-CoverClassLabel) %>%
                                  pivot_wider(names_from = ForestFloorCode, values_from = Pct,
                                              values_fill = NA) %>%
                                  select(Plot_Name, PlotID, EventID, BS, R, W, `T`, NV, L)

    old_fcol <- c("BS", "R", "W", "T", "NV", "L")
    new_fcolp <- c("Pct_Bare_Soil", "Pct_Rock", "Pct_Water", "Pct_Trampled", "Pct_Bryophyte", "Pct_Lichen")
    names(ffloor_pct_wide)[match(old_fcol, names(ffloor_pct_wide))] <- new_fcolp # change col names to match prev. analyses

    ffloor_txt_wide <- ffloor %>% pivot_wider(names_from = ForestFloorCode, values_from = CoverClassLabel) %>%
                                  select(Plot_Name, PlotID, EventID, BS, R, W, `T`, NV, L)

    new_fcolt <- c("Txt_Bare_Soil", "Txt_Rock", "Txt_Water", "Txt_Trampled", "Txt_Bryophyte", "Txt_Lichen")
    names(ffloor_txt_wide)[match(old_fcol, names(ffloor_txt_wide))] <- new_fcolt # change col names to match prev. analyses

    ffloor_wide <- full_join(ffloor_pct_wide, ffloor_txt_wide,
                         by = intersect(names(ffloor_pct_wide), names(ffloor_txt_wide)))

    ffloor_wide <- ffloor_wide[, c("Plot_Name", "PlotID", "EventID",
                                   "Pct_Bare_Soil", "Txt_Bare_Soil", "Pct_Bryophyte", "Txt_Bryophyte",
                                   "Pct_Lichen", "Txt_Lichen", "Pct_Rock", "Txt_Rock", "Pct_Trampled",
                                   "Txt_Trampled", "Pct_Water", "Txt_Water")]

    # stand height
      # Consider making a new function that pulls in the stand heights and leaves individuals
    treeht_sum <- treeht %>% mutate(crown = ifelse(CrownClassCode == 4, "Inter", "Codom")) %>%
                             group_by(Plot_Name, PlotID, EventID, crown) %>%
                             summarize(avg_ht = mean(Height, na.rm = TRUE), .groups = 'drop') %>%
                             pivot_wider(names_from = crown, values_from = avg_ht,
                                         names_prefix = "Avg_Height_") %>%
                             select(Plot_Name, PlotID, EventID, Avg_Height_Codom, Avg_Height_Inter)

    # slopes- need to pull in slopes for QAQC, like for CWD
    slopes_QAQC1 <- slopes[slopes$IsQAQC == TRUE,
                           c("Plot_Name", "PlotID", "EventID", "SampleYear", "IsQAQC", "PlotSlope")]

    slopes_init <- slopes[slopes$IsQAQC == FALSE,
                          c("Plot_Name", "PlotID", "EventID", "SampleYear", "IsQAQC", "PlotSlope")]

    slopes_QAQC <- left_join(slopes_QAQC1 %>% select(-PlotSlope),
                             slopes %>% filter(IsQAQC == FALSE) %>% select(-PlotID, -EventID, -IsQAQC),
                         by = c("Plot_Name", "SampleYear"))

    slopes_QAQC <- slopes_QAQC[, c("Plot_Name", "PlotID", "EventID", "SampleYear", "IsQAQC", "PlotSlope")]
    slopes_init <- slopes_init[, c("Plot_Name", "PlotID", "EventID", "SampleYear", "IsQAQC", "PlotSlope")]

    slopes_final <- rbind(slopes_init, slopes_QAQC)

    merge_cols <- Reduce(intersect, list(names(standinfo), names(pstrata_wide), names(ffloor_wide),
                                         names(slopes_final), names(treeht_sum)))

    stand_comb <- Reduce(function(x, y) full_join(x = x, y = y, by = merge_cols),
                         list(standinfo, slopes_final, pstrata_wide, ffloor_wide, treeht_sum)) %>% unique()

    #table(complete.cases(stand_comb[, merge_cols])) #All TRUE
    #stand_comb[which(duplicated(stand_comb$EventID)),] #no dups

    plot_events <- force(joinLocEvent(park = park, from = from , to = to, QAQC = QAQC,
                                      panels = panels, locType = locType, eventType = "complete",
                                      abandoned = FALSE, output = 'short', ...)) %>%
                   select(Plot_Name, Network, ParkUnit, ParkSubUnit, PlotTypeCode, PanelCode, PlotCode, PlotID,
                          xCoordinate, yCoordinate, EventID, SampleYear, SampleDate, cycle, IsQAQC, IsStuntedWoodland)

    if(nrow(plot_events) == 0){stop("Function returned 0 rows. Check that park and years specified contain visits.")}

    stand_merge <- left_join(plot_events, stand_comb,
                         intersect(names(plot_events), names(stand_comb))) %>%
                   arrange(PlotCode, SampleYear, IsQAQC)

    stand_final <- if(output == 'short'){
      stand_merge %>% select(Plot_Name, ParkUnit, ParkSubUnit, SampleYear, SampleDate, cycle, IsQAQC,
                             Stand_Structure, Pct_Crown_Closure, Deer_Browse_Index, Microtopography, Earthworms,
                             Water_on_Plot, PlotSlope, Pct_Understory_Low, Pct_Understory_Mid, Pct_Understory_High,
                             Pct_Bare_Soil, Pct_Bryophyte, Pct_Lichen, Pct_Rock, Pct_Trampled, Pct_Water,
                             Avg_Height_Codom, Avg_Height_Inter, IsStuntedWoodland)
      } else if(output == 'verbose'){stand_merge}

   return(data.frame(stand_final))
}