This report is automatically generated with the R package knitr (version 1.40) .

source("R Functions/functions_QA data.R")


### LOAD DATA ### - need to set WD to aqueous folder for Riginal file
#FIND AND REPLACE (Ctrl+F) 'WORKSHEEET' WITH MORE APPROPRIATE NAME (e.g., 'CALFED_data')
#CHANGE 'FILENAME' & 'SHEETNAME' WITH ACTUAL NAMES
SNIPFISH <- readxl::read_excel('Reeval_Impl_Goals_Linkage_Analysis/Data/Aqueous/SNIP_Riginal.xlsx', sheet='SNIP_MasterDatabase_Final_HgRec', guess_max = 30000)
nrow(SNIPFISH) #number of rows should match the Excel file (minus the header row)
## [1] 39729
### LIST COLUMNS TO BE USED, ADD USER DEFINED COLUMNS, & RENAME COLUMNS TO CEDEN STANDARDS ###
#Use 1.READ ME.xlsx, 'ColumnsForR' to list & identify columns that match corresponding CEDEN Standard columns
keep_cols <- c('SourceID','SourceRow','StationName','StationCode','TargetLatitude','TargetLongitude','SampleDate','CollectionTime','QACode',
               'NumberFishPerComp','Analyte','Unit','Result','MDL','RL','ResultQualCode','Datum','Program','ParentProject','Project',
               'CompositeCommonName','CompositeFinalID','CompositeTissueName','MethodName','Weight','Length', 'SampleID',
               'CompositeCompositeID','WaterBodyType','SampleComments','SubmissionCode', 'BatchVerification', 'ComplianceCode'
               )

temp_cols <- c('SampleYear') #Include columns that do not match CEDEN standards but may be useful (e.g., Unit columns for MDL & RL)
#temp_cols are removed before the data is merged with other datasets

SNIPFISH_new <- SNIPFISH %>%
  select( c(keep_cols,temp_cols) ) %>% #DO NOT CHANGE - selects columns specified above
  rename(
    #Rename SNIPFISH columns to CEDEN format here: CEDEN 'COLUMNNAME' = SNIPFISH 'COLUMNNAME'
    #DELTE COLUMN NAMES THAT DO NOT HAVE AN EQUIVALENT COLUMN IN THE SNIPFISH
    'SampleTime' = 'CollectionTime',
    'CoordSystem' = 'Datum',
    'ProgramName' = 'Program',
    'ParentProjectName' = 'ParentProject',
    'ProjectName' = 'Project',
    'CommonName' = 'CompositeCommonName',
    'TaxonomicName' = 'CompositeFinalID',
    'TissueName' = 'CompositeTissueName',
    'Method' = 'MethodName',
    'WeightAvg g' = 'Weight', #need to make sure column weight units are all in grams
    'TLAvgLength mm' = 'Length', #need to make sure column Lengthunits are all in mm
    'CompositeRowID' = 'CompositeCompositeID',
    'WBT' = 'WaterBodyType',
    'ResultComments' = 'SampleComments',
    'LabSubmissionCode' = 'SubmissionCode'
  ) %>%
  mutate(
    #Create Missing column or modify existing column here: CEDEN COLUMNNAME = 'SPECIFIED VALUE' or FUNCTION
    #DELTE COLUMN NAMES THAT DO NOT NEED TO BE CHANGED
    CitationCode = 'SNIPFISH',
    ProjectCode = NA_character_,
    CompositeID = NA_character_,
    `TLMin mm` = NA_real_,
    `TLMax mm` = NA_real_
  )

nrow(SNIPFISH_new)
## [1] 39729
#str(SNIPFISH_new) #just to check data class of different columns - e.g., is Date column in POSIX format?
#View(SNIPFISH_new)


### FORMAT COLUMN PARAMETERS ###

  # Standardize TissueName Groups - "Fillet" & "Whole Body" #
unique(SNIPFISH_new$TissueName)
##  [1] NA                                           
##  [2] "fillet"                                     
##  [3] "Whole Body"                                 
##  [4] "Fillet"                                     
##  [5] "Organism, whole"                            
##  [6] "Liver"                                      
##  [7] "Gills"                                      
##  [8] "Brain"                                      
##  [9] "Kidney"                                     
## [10] "whole body"                                 
## [11] "Fillet skin-off"                            
## [12] "FILLET"                                     
## [13] "WHOLE BODY"                                 
## [14] "Whole body"                                 
## [15] "Unknown"                                    
## [16] "soft tissue (e.g. clams) with gonads intact"
## [17] "Soft Tissue"                                
## [18] "whole without Head, Tail and Guts"
SNIPFISH_new <- SNIPFISH_new %>%
  mutate(TissueName = recode(TissueName,
                             "fillet" = "Fillet",
                             "FILLEt" = "Fillet",
                             "Fillet skin-off" = "Fillet",
                             "Organism, whole" = "Whole Body",
                             "WHOLE BODY" = "Whole Body",
                             "whole body"  = "Whole Body",
                             "Whole body"  = "Whole Body"
                             )
         ) %>%
  filter(TissueName %in% c('Fillet','Whole Body'))
unique(SNIPFISH_new$TissueName)
## [1] "Fillet"     "Whole Body"
  # Standardize WBT (WaterBodyType) Groups - "River/Stream", "Drain/Canal", "Wetland", "Spring", "Slough", 
  #                                          "Pond",  "Lake/Reservoir", "Delta", "Forebay/Afterbay", "Not Recorded" #
unique(SNIPFISH_new$WBT)
## [1] "Reservoir/Lake" "River/Creek"    "river/Creek"    "Hatchery"       "Delta"
SNIPFISH_new <- SNIPFISH_new %>%
  filter(WBT %are not% c("Hatchery")) %>%
  mutate(WBT = recode(WBT,
                      "river/Creek" = "River/Stream",
                      "River/Creek" = "River/Stream",
                      "Reservoir/Lake" = "Lake/Reservoir" #Jennie confirmed some Lake/Reservoirs are in scope
                      ))
unique(SNIPFISH_new$WBT)
## [1] "Lake/Reservoir" "River/Stream"   "Delta"
  # Filter to include or exclude specified sites with pond or lake in name that are not part of rivers or in delta
includedSites <- c('"old F&G pond",238', "Cachement Basin", "Cachment basin", "Central Pond,222", "Greens Lake,226", "PG&E pond,223", "seasonal flooded pond,245", "Shag Slough") # list of sites to be included that have lake/pond in name but are part of a river or delta
includedSites %in% SNIPFISH_new$StationName  #Test to see if these sites are present
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#^ all false so don't need to include any code to make sure these sites are not excluded based on WBT 
SNIPFISH_new <- SNIPFISH_new %>%
  filter(WBT %in% c("River/Stream", "Delta"))
nrow(SNIPFISH_new)
## [1] 4334
  # Standardize ResultQualCode Groups - "ND", "DNQ", NA#
unique(SNIPFISH_new$ResultQualCode) #Identifies OLDNAMES
## [1] "="   "ND"  "DNQ" "<"
SNIPFISH_new <- SNIPFISH_new %>%
  mutate(ResultQualCode = recode(ResultQualCode,
                                 "<" = "DNQ"
                                 )
         )
unique(SNIPFISH_new$ResultQualCode) #New naming structure for ResultQualCode Groupings
## [1] "="   "ND"  "DNQ"
  # Format Result Column to Numeric#
  # Check column for text - based on text user needs to decide what to do
if(!is.numeric(SNIPFISH_new$Result)){
  old <-SNIPFISH_new$Result
  new <-SNIPFISH_new$Result
  new[grepl('<|[a-df-zA-DF-Z]', new)] <- NA #skip 'e' for exponential notation e.g., "8e-005"
  #Print what text was found and what is being done
  cat(paste0("'Result' column should be numeric but some cells contain ", grammaticList(setdiff(old, new)),
             ".\nACTIONS TAKEN:\n",
             "~explain here~.\n"))
  #SNIPFISH_new <- SNIPFISH_new %>%
  #  mutate( #Due stuff to prep column to be converted to Numeric
  #    Result = as.numeric(new)
  #    )
} else {
  cat("'Result' column converted to numeric format\n")
  SNIPFISH_new$Result <- as.numeric(SNIPFISH_new$Result)}
## 'Result' column converted to numeric format
  # Format MDL Column to Numeric#
  # Check column for text - based on text user needs to decide what to do
if(!is.numeric(SNIPFISH_new$MDL)){
  old <-SNIPFISH_new$MDL
  new <-SNIPFISH_new$MDL
  new[grepl('[a-df-zA-DF-Z]', new)] <- NA #skip 'e' for exponential notation e.g., "8e-005"
  #Print what text was found and what is being done
  cat(paste0("'Result' column should be numeric but some cells contain ", grammaticList(setdiff(old, new)),
             ".\nACTIONS TAKEN:\n",
             "~explain here~.\n"))

  #SNIPFISH_new <- SNIPFISH_new %>%
  #  mutate( #Do stuff to prep column to be converted to Numeric
  #    MDL = as.numeric(new)
  #  )
} else {
  cat("'MDL' column converted to numeric format\n")
  SNIPFISH_new$MDL <- as.numeric(SNIPFISH_new$MDL)}
## 'MDL' column converted to numeric format
if(any(grepl('[a-df-zA-DF-Z]', SNIPFISH_new$RL))){
  old <-SNIPFISH_new$RL
  new <-SNIPFISH_new$RL
  new[grepl('[a-df-zA-DF-Z]', new)] <- NA #skip 'e' for exponential notation e.g., "8e-005"
  #Print what text was found and what is being done
  cat(paste0("'Result' column should be numeric but some cells contain ", grammaticList(setdiff(old, new)),
             ".\nACTIONS TAKEN:\n",
             "~explain here~.\n"))

  #SNIPFISH_new <- SNIPFISH_new %>%
  #  mutate( #Do stuff to prep column to be converted to Numeric
  #    RL = as.numeric(new)
  #  )
} else {
  cat("'RL' column converted to numeric format\n")
  SNIPFISH_new$RL <- as.numeric(SNIPFISH_new$RL)}
## 'RL' column converted to numeric format
#str(SNIPFISH_new)


  # Check if Result, MDL, & RL Columns all equal <NA> or 0 - these rows have no useful information
nrow(SNIPFISH_new) #Number rows before
## [1] 4334
#CODE BELOW REQUIRES USER TROUBLESHOOTING DEPENDING ON AVAILABLE COLUMNS AND SPREADSHEET SPECIFIC CONDITIONS#
SNIPFISH_new <- SNIPFISH_new %>%
  #Set 0 & negative values as blank
  mutate(Result = ifelse(Result <= 0, NA_real_, Result),
         MDL = ifelse(MDL <= 0, NA_real_, MDL),
         RL = ifelse(RL <= 0, NA_real_, RL))
na_results <- SNIPFISH_new %>% #Record rows where Result, MDL, & RL all equal <NA>
  filter( is.na(Result) & is.na(MDL) & is.na(RL) )
#View(na_results)
SNIPFISH_new <- anti_join(SNIPFISH_new, na_results, by='SourceRow') #returns rows from SNIPFISH_new not matching values in no_result
nrow(SNIPFISH_new) #Number rows after
## [1] 4333
  # Standardize Analyte Groups - "Mercury, Total" (we consider Total Mercury and Methylmercury to be approx equal)
unique(SNIPFISH_new$Analyte)
## [1] "Mercury, Total"                                                          
## [2] "Mercury, Methyl"                                                         
## [3] "Mercury"                                                                 
## [4] "Mercury, biota, tissue, recoverable, dry weight, milli"                  
## [5] "Mercury, biota, tissue, recoverable, dry weight, micrograms per gram"    
## [6] "Mercury, biota, tissue, recoverable, wet weight, milligrams per kilogram"
## [7] "MERCURY, TOTAL IN FISH (PPM,WET WEIGHT BASIS); TISSUE, WET WEIGHT; MG/KG"
SNIPFISH_new <- SNIPFISH_new %>%
  mutate( Analyte = recode(Analyte,
                           "Mercury" = "Mercury, Total",
                           "Mercury, biota, tissue, recoverable, wet weight, milligrams per kilogram" = "Mercury, Total",
                           "MERCURY, TOTAL IN FISH (PPM,WET WEIGHT BASIS); TISSUE, WET WEIGHT; MG/KG" = "Mercury, Total",
                           "Mercury, biota, tissue, recoverable, dry weight, micrograms per gram" = "Mercury, Total dw",
                           "Mercury; ng/g dw" = "Mercury, Total dw",
                           "Mercury, biota, tissue, recoverable, dry weight, milli" = "Mercury, Total dw")
  )
unique(SNIPFISH_new$Analyte) #New naming structure for Analyte Groupings; keep "dw" in analyte so it can be added to Unit column later
## [1] "Mercury, Total"    "Mercury, Methyl"   "Mercury, Total dw"
unique(paste(SNIPFISH_new$Analyte, SNIPFISH_new$Unit, sep='; '))
##  [1] "Mercury, Total; µg/g ww"                                                    
##  [2] "Mercury, Methyl; ppb dry wt"                                                
##  [3] "Mercury, Methyl; ppb wet wt"                                                
##  [4] "Mercury, Total; ppm wet wt"                                                 
##  [5] "Mercury, Total; ppb wet wt"                                                 
##  [6] "Mercury, Total dw; mg/kg"                                                   
##  [7] "Mercury, Total; ppm wet wt (adj for whole body analysis 1.62 x orig hg cnc)"
##  [8] "Mercury, Total; ppm dry wt"                                                 
##  [9] "Mercury, Total dw; µg/g dw"                                                 
## [10] "Mercury, Total; mg/kg"                                                      
## [11] "Mercury, Total; µg/g dw"                                                    
## [12] "Mercury, Methyl; ng/g dw"                                                   
## [13] "Mercury, Total; ng/g dw"                                                    
## [14] "Mercury, Methyl; ng/g ww"                                                   
## [15] "Mercury, Total; ng/g ww"                                                    
## [16] "Mercury, Methyl; ppm dry wt"                                                
## [17] "Mercury, Methyl; ppm wet wt"
  # Format Units Column - "mg/Kg ww"
unique(SNIPFISH_new$Unit) #Identifies OLDNAMES
##  [1] "µg/g ww"                                                    
##  [2] "ppb dry wt"                                                 
##  [3] "ppb wet wt"                                                 
##  [4] "ppm wet wt"                                                 
##  [5] "mg/kg"                                                      
##  [6] "ppm wet wt (adj for whole body analysis 1.62 x orig hg cnc)"
##  [7] "ppm dry wt"                                                 
##  [8] "µg/g dw"                                                    
##  [9] "ng/g dw"                                                    
## [10] "ng/g ww"
# If more than 1 unit colmn exists (e.g., for RL and MDL columns) see WQP script for example on merging into 1 column
SNIPFISH_new <- SNIPFISH_new %>%
  standardizeUnits(pp = "mass")
unique(SNIPFISH_new$Unit)
## [1] "mg/Kg ww"                                                     
## [2] "mg/Kg dry wt"                                                 
## [3] "mg/Kg wet wt"                                                 
## [4] "mg/Kg"                                                        
## [5] "mg/Kg wet wt (adj for whole body analysis 1.62 x orig hg cnc)"
## [6] "mg/Kg dw"
# simplify "dry wt" & "wet wt" in Unit name to "dw" and "ww"
SNIPFISH_new <- SNIPFISH_new %>%
  mutate(Unit = case_when(grepl('wet', Unit) ~ sub('[^mg/Kg].*$', ' ww', Unit), #exclude mg/Kg "[^mg/Kg]", include all characters at end ".*$"
                          grepl('dry', Unit) ~ sub('[^mg/Kg].*$', ' dw', Unit),
                          TRUE ~ Unit
                    )
          )
unique(SNIPFISH_new$Unit)
## [1] "mg/Kg ww" "mg/Kg dw" "mg/Kg"
unique(paste(SNIPFISH_new$Analyte, SNIPFISH_new$Unit, sep='; '))
## [1] "Mercury, Total; mg/Kg ww"    "Mercury, Methyl; mg/Kg dw"  
## [3] "Mercury, Methyl; mg/Kg ww"   "Mercury, Total dw; mg/Kg"   
## [5] "Mercury, Total; mg/Kg dw"    "Mercury, Total dw; mg/Kg dw"
## [7] "Mercury, Total; mg/Kg"
#if the analyte is "Mercury, Total dw" append dw onto unit if it's not already there
SNIPFISH_new <- SNIPFISH_new %>%
  mutate(
    Unit = if_else(Analyte == 'Mercury, Total dw' & Unit == 'mg/Kg', paste(Unit, 'dw'), Unit),
    #assume non-specified mg/kg is ww
    Unit = if_else(grepl('dw|ww', Unit), Unit, paste(Unit, 'ww')),
    Analyte = "Mercury, Total"
  )

unique(SNIPFISH_new$Unit) #New naming structure for Units
## [1] "mg/Kg ww" "mg/Kg dw"
unique(paste(SNIPFISH_new$Analyte, SNIPFISH_new$Unit, sep='; '))
## [1] "Mercury, Total; mg/Kg ww" "Mercury, Total; mg/Kg dw"
# Format Date and Time Column # 
# THE EXAMPLE CODE BELOW ASSUMES DATE AND TIME ARE IN SAME COLUMNS - IF TIME IS IN SEPERATE COLUMN LOOK AT AQ LINKAGE DATA TEMPLATE

SNIPFISH_new <- SNIPFISH_new %>%
  mutate(
    SampleDate = ifelse(is.na(SampleDate), paste0('1/1/',SampleYear), SampleDate)
    )
SNIPFISH_new <- SNIPFISH_new %>%
      mutate(
        SampleDate = recode(SampleDate,
                        "Sept-Oct 2002" ="9/15/2002",
                        '2000-2001' = '1/1/2001'
        ))

SNIPFISH_new <- SNIPFISH_new %>%
  mutate(SampleDate = lubridate::parse_date_time(SampleDate, orders = c("mdy")) #had to break this into a seperate mutate for it to work properly
  )
length(SNIPFISH_new$SourceRow[is.na(SNIPFISH_new$SampleDate)]) #this needs to be 0
## [1] 0
##STOP##
length(SNIPFISH_new$SourceRow[is.na(SNIPFISH_new$SampleTime)])
## [1] 0
SNIPFISH_new <- SNIPFISH_new %>%
  mutate(SampleTime = if_else(SampleTime == '9999', NA_character_, SampleTime) ,
         SampleTime = lubridate::parse_date_time(SampleTime, orders = c("HM", "HMS", "mdyHMS"))
  )

SNIPFISH_new<- SNIPFISH_new %>%
  mutate(
    #If SampleDate & CollectioTime are not in Character format by defualt, turn it into a character class so it exports better
    SampleDate = ifelse(sapply(SampleDate, is.character), SampleDate, as.character(as.Date(SampleDate))),
    SampleTime = ifelse(sapply(SampleTime, is.character), SampleTime, format(lubridate::ymd_hms(SampleTime), "%H:%M:%S")),
    #COMBINE DATE AND TIME INTO SampleDateTime COLUMN
    SampleDateTime = if_else(!is.na(SampleTime), paste(SampleDate, SampleTime), paste(SampleDate, '00:00:00')),
    #FORMAT SampleDateTime COLUMN TO DATE FORMAT
    SampleDateTime = lubridate::ymd_hms(SampleDateTime)
  )


### REMOVE TEMPORARY COLUMNS ###
SNIPFISH_new <- SNIPFISH_new %>%
  select(-one_of(temp_cols)) #Remove temp columns since they are no longer needed
#View(SNIPFISH_new)

## SAVE FORMATTED DATA AS EXCEL FILE ##
writexl::write_xlsx(SNIPFISH_new, path='Reeval_Impl_Goals_Linkage_Analysis/Data/Fish/SNIPFISH_ceden_format.xlsx')
# In excel, to convert SampleDate column to Date format
# 1 - Select the date column.
# 2 - Go to the Data-tab and choose "Text to Columns".
# 3 - On the first screen, leave radio button on "delimited" and click Next.
# 4 - Unselect any delimiter boxes (everything blank) and click Next.
# 5 - Under column data format choose Date, select YMD
# 6 - Click Finish.

The R session information (including the OS info, R version and all packages used):

    sessionInfo()
## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22621)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8  LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] lubridate_1.8.0    plotly_4.10.0      readxl_1.4.1       actuar_3.3-0      
##  [5] NADA_1.6-1.1       forcats_0.5.2      stringr_1.4.1      dplyr_1.0.9       
##  [9] purrr_0.3.4        readr_2.1.2        tidyr_1.2.0        tibble_3.1.8      
## [13] ggplot2_3.3.6      tidyverse_1.3.2    fitdistrplus_1.1-8 survival_3.4-0    
## [17] MASS_7.3-58.1     
## 
## loaded via a namespace (and not attached):
##  [1] lattice_0.20-45     assertthat_0.2.1    digest_0.6.29       utf8_1.2.2         
##  [5] R6_2.5.1            cellranger_1.1.0    backports_1.4.1     reprex_2.0.2       
##  [9] evaluate_0.16       highr_0.9           httr_1.4.4          pillar_1.8.1       
## [13] rlang_1.0.5         lazyeval_0.2.2      googlesheets4_1.0.1 rstudioapi_0.14    
## [17] data.table_1.14.2   Matrix_1.5-1        splines_4.2.2       googledrive_2.0.0  
## [21] htmlwidgets_1.5.4   munsell_0.5.0       broom_1.0.1         compiler_4.2.2     
## [25] modelr_0.1.9        xfun_0.32           pkgconfig_2.0.3     htmltools_0.5.3    
## [29] tidyselect_1.1.2    fansi_1.0.3         viridisLite_0.4.1   crayon_1.5.1       
## [33] tzdb_0.3.0          dbplyr_2.2.1        withr_2.5.0         grid_4.2.2         
## [37] jsonlite_1.8.0      gtable_0.3.1        lifecycle_1.0.1     DBI_1.1.3          
## [41] magrittr_2.0.3      scales_1.2.1        writexl_1.4.0       cli_3.3.0          
## [45] stringi_1.7.8       fs_1.5.2            xml2_1.3.3          ellipsis_0.3.2     
## [49] generics_0.1.3      vctrs_0.4.1         expint_0.1-7        tools_4.2.2        
## [53] glue_1.6.2          hms_1.1.2           fastmap_1.1.0       colorspace_2.0-3   
## [57] gargle_1.2.0        rvest_1.0.3         knitr_1.40          haven_2.5.1
    Sys.time()
## [1] "2024-01-05 11:03:15 PST"