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"