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

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


### LOAD DATA ###
DRMP_2016 <- readxl::read_excel('Reeval_Impl_Goals_Linkage_Analysis/Data/Fish/DRMP_2016_CEDENFish.xlsx', sheet=1, guess_max = 30000)
nrow(DRMP_2016) #number of rows should match the Excel file (minus the header row)
## [1] 132
#Load CEDEN StationCodes to look up CoordSystem by StationCode
CedenCoordSys <- readxl::read_excel('Reeval_Impl_Goals_Linkage_Analysis/Data/Fish/0_CEDEN_StationCode_CoordSystem lookup.xlsx', sheet='Worksheet', guess_max = 30000)
CedenCoordSys <- CedenCoordSys %>%
  select(StationCode, Datum) %>%
  rename(CoordSystem = Datum)



### 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','CompositeProgramName','CompositeParentProjectName','CompositeProjectCode','CompositeProjectName','CompositeCompositeID',
               'CompositeStationName','CompositeStationCode','CompositeTargetLatitude','CompositeTargetLongitude','CompositeSampleDate','CompositeCommonName',
               'CompositeFinalID','NumberFishPerComp','CompositeTissueName','Method','Analyte','Unit','Result','ResQualCode','MDL','RL','WeightAvg g','TLMin mm',
               'TLMax mm','TLAvgLength mm','CompositeRowID','SampleID','QACode','BatchVerification','ComplianceCode','ResultComments','LabSubmissionCode'
)

DRMP_2016_new <- DRMP_2016 %>%
  select(keep_cols) %>% #DO NOT CHANGE - selects columns specified above
  rename(
    ProgramName = CompositeProgramName,
    ParentProjectName = CompositeParentProjectName,
    ProjectCode = CompositeProjectCode,
    ProjectName = CompositeProjectName,
    CompositeID = CompositeCompositeID,
    ResultQualCode = ResQualCode,
    StationName = CompositeStationName,
    StationCode = CompositeStationCode,
    TargetLatitude = CompositeTargetLatitude,
    TargetLongitude = CompositeTargetLongitude,
    SampleDate = CompositeSampleDate,
    CommonName = CompositeCommonName,
    TaxonomicName = CompositeFinalID,
    TissueName = CompositeTissueName
  ) %>%
  mutate(
    CitationCode = 'DRMP_2016_CEDEN',
    SampleTime = "00:00:00",
    WBT = 'River/Stream'
  ) %>%
  left_join(
    ., CedenCoordSys, by='StationCode' #adds in CoordSystem column
  )

nrow(DRMP_2016_new)
## [1] 132
# str(DRMP_2016_new) #just to check data class of different columns - e.g., is Date column in POSIX format?
# View(DRMP_2016_new)


### FORMAT COLUMN PARAMETERS ###

  # Standardize WBT (WaterBodyType) Groups - "River/Stream", "Estuary", Drain/Canal", "Wetland", "Spring", "Slough", 
  #                                          "Pond",  "Lake/Reservoir", "Delta", "Forebay/Afterbay", "Not Recorded" #
unique(DRMP_2016_new$WBT) #Identifies OLDNAMES
## [1] "River/Stream"
# Good - all "River/Stream"


  # Standardize TissueName Groups - "Fillet" or "Whole Body" #
unique(DRMP_2016_new$TissueName)
## [1] "fillet"
DRMP_2016_new <- DRMP_2016_new %>%
  mutate(TissueName = recode(TissueName,
                             "fillet" = "Fillet"
                             )
         )
unique(DRMP_2016_new$TissueName)
## [1] "Fillet"
  # Standardize Analyte Groups - "Mercury, Total" (we consider Total Mercury and Methylmercury to be approx equal) #
unique(DRMP_2016_new$Analyte)
## [1] "Mercury, Total"  "Moisture, Total"
DRMP_2016_new <- DRMP_2016_new %>%
  filter( Analyte == "Mercury, Total" )
#Create 'Analyte' column from Analyte & Analyte_part2 columns - then delete Analyte_part2 column##
unique(DRMP_2016_new$Analyte) #New naming structure for Analyte Groupings
## [1] "Mercury, Total"
  # Standardize ResultQualCode Groups - "ND", "DNQ", NA#
unique(DRMP_2016_new$ResultQualCode) #Identifies OLDNAMES
## [1] "="
# Good - all "="



# Format Result Column to Numeric#  
# Check column for text - based on text user needs to decide what to do
if(!is.numeric(DRMP_2016_new$Result)){
  if(all(is.na(DRMP_2016_new$Result))){
    DRMP_2016_new <- DRMP_2016_new %>%
      mutate( #Column is all blanks and will be converted to Numeric
        RL = as.numeric(new)
      )
    cat("'Result' column is all blanks and was converted to numeric format\n")
  }else{
    old <-DRMP_2016_new$Result
    new <-DRMP_2016_new$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"))
    #DRMP_2016_new <- DRMP_2016_new %>%
    #  mutate( #Do stuff to prep column to be converted to Numeric
    #    Result = as.numeric(new)
    #    )
  }
}else{
  cat("'Result' column is in numeric format\n")}
## 'Result' column is in numeric format
  # Format MDL Column to Numeric#
  # Check column for text - based on text user needs to decide what to do
if(!is.numeric(DRMP_2016_new$MDL)){
  if(all(is.na(DRMP_2016_new$MDL))){
    DRMP_2016_new <- DRMP_2016_new %>%
      mutate( #Column is all blanks and will be converted to Numeric
        RL = as.numeric(new)
      )
    cat("'MDL' column is all blanks and was converted to numeric format\n")
  }else{
    old <-DRMP_2016_new$MDL
    new <-DRMP_2016_new$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("'MDL' column should be numeric but some cells contain ", grammaticList(setdiff(old, new)),
               ".\nACTIONS TAKEN:\n",
               "~explain here~.\n"))
    #DRMP_2016_new <- DRMP_2016_new %>%
    #  mutate( #Do stuff to prep column to be converted to Numeric
    #    MDL = as.numeric(new)
    #    )
  }
}else{
  cat("'MDL' column is in numeric format\n")}
## 'MDL' column is in numeric format
  # Format RL Column to Numeric#  
  # Check column for text - based on text user needs to decide what to do
if(!is.numeric(DRMP_2016_new$RL)){
  if(all(is.na(DRMP_2016_new$RL))){
    DRMP_2016_new <- DRMP_2016_new %>%
      mutate( #Column is all blanks and will be converted to Numeric
        RL = as.numeric(new)
      )
    cat("'RL' column is all blanks and was converted to numeric format\n")
  }else{
    old <-DRMP_2016_new$RL
    new <-DRMP_2016_new$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("'RL' column should be numeric but some cells contain ", grammaticList(setdiff(old, new)),
               ".\nACTIONS TAKEN:\n",
               "~explain here~.\n"))
    #DRMP_2016_new <- DRMP_2016_new %>%
    #  mutate( #Do stuff to prep column to be converted to Numeric
    #    RL = as.numeric(new)
    #    )
  }
}else{
  cat("'RL' column is in numeric format\n")}
## 'RL' column is in numeric format
  # Check if Result, MDL, & RL Columns all equal <NA> or 0 - these rows have no useful information
nrow(DRMP_2016_new) #Number rows before
## [1] 66
#CODE BELOW REQUIRES USER TROUBLESHOOTING DEPENDING ON AVAILABLE COLUMNS AND SPREADSHEET SPECIFIC CONDITIONS#
DRMP_2016_new <- DRMP_2016_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 <- DRMP_2016_new %>% #Record rows where Result, MDL, & RL all equal <NA>
  filter( is.na(Result) & is.na(MDL) & is.na(RL) )
# View(na_results)
DRMP_2016_new <- anti_join(DRMP_2016_new, na_results, by='SourceRow') #returns rows from DRMP_2016_new not matching values in no_result
nrow(DRMP_2016_new) #Number rows after
## [1] 66
  # Format Units Column - "mg/Kg ww" or "mg/Kg dw"
unique(DRMP_2016_new$Unit) #Identifies OLDNAMES
## [1] "ug/g ww"
# If more than 1 unit colmn exists (e.g., for RL and MDL columns) see WQP script for example on merging into 1 column
DRMP_2016_new <- DRMP_2016_new %>%
  standardizeUnits(pp = "mass")
unique(DRMP_2016_new$Unit)
## [1] "mg/Kg ww"
  # 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
DRMP_2016_new <- DRMP_2016_new %>%
  #rowise() %>%    # rowise is very slow - so used sapply to make this a rowise operation
  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 = ifelse(!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 ###
# DRMP_2016_new <- DRMP_2016_new %>%
#   select(-one_of(temp_cols)) #Remove temp columns since they are no longer needed
# View(DRMP_2016_new)

## SAVE FORMATTED DATA AS EXCEL FILE ##
writexl::write_xlsx(DRMP_2016_new, path='Reeval_Impl_Goals_Linkage_Analysis/Data/Fish/DRMP_2016_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 10:01:26 PST"