This report is automatically generated with the R
package knitr
(version 1.40
)
.
source("R Functions/functions_QA data.R") ### LOAD DATA ### DWR <- readxl::read_excel('Reeval_Impl_Goals_Linkage_Analysis/Data/Aqueous/dwr.xlsx', sheet='WDL WQ Hg Data RWQCB Region 5', guess_max = 30000) nrow(DWR) #number of rows should match the Excel file (minus the header row)
## [1] 2974
### 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', 'Full Station Name', 'WQ Station Code', 'Collection Date', 'Station Type', 'Method Name', 'Constituent', 'Units', 'Result', 'RL', 'Sample Description', 'DDLat (NAD83)', 'DDLong (NAD83)') #temp_cols <- c('COLUMN NAME', 'COLUMN NAME') #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 #not applicable for this dataset DWR_new <- DWR %>% select( c(keep_cols) ) %>% #DO NOT CHANGE - selects columns specified above mutate( #Add user defined columns #COLUMNNAME = 'THE SPECIFIED VALUE' CitationCode = 'DWR2018', CoordSystem = 'NAD83', MatrixName = 'Aqueous', CitationCode = NA, Project = NA, LabBatch = NA, LabSampleID = NA, MDL = NA_real_, SampleID = NA, QACode = NA, BatchVerification = NA, ComplianceCode = NA, CollectionComments = NA, ResultsComments = NA, BatchComments = NA, SampleTypeCode = NA ) %>% rename( #Rename columns to CEDEN standards #CEDEN 'COLUMNNAME' = DWR 'COLUMNNAME' #If a column doesnt match the CEDEN Standard, delete that column below, talk to Robin and discuss if we can add it using other information #If a column below doesn't exist; move COLUMNNAME to Mutate above using format; StationName = NA_character_ 'StationName' = 'Full Station Name', 'StationCode' = 'WQ Station Code', 'SampleDate' = 'Collection Date', 'WBT' = 'Station Type', 'MethodName' = 'Method Name', 'Analyte' = 'Constituent', 'Unit' = 'Units', 'Result' = 'Result', 'RL' = 'RL', 'SampleComments' = 'Sample Description', 'TargetLatitude' = 'DDLat (NAD83)', 'TargetLongitude' = 'DDLong (NAD83)' ) nrow(DWR_new)
## [1] 2974
#View(DWR_new) ### FORMAT COLUMN PARAMETERS ### # Standardize WBT (WaterBodyType) Groups - "River/Stream", "Drain/Canal", "Wetland", "Not Recorded" # unique(DWR_new$WBT) #Identifies OLDNAMES
## [1] "River and Stream" "Broad Water Body" "State Water Project" ## [4] "General (UTM)"
#Look for inconsistent group pairings between MatrixName and WBT (e.g., 'Soil; Stream') unique(paste(DWR_new$MatrixName, DWR_new$WBT, sep='; '))
## [1] "Aqueous; River and Stream" "Aqueous; Broad Water Body" ## [3] "Aqueous; State Water Project" "Aqueous; General (UTM)"
#If an inconsistent grouping exists, add comment to 'CollectionComments' column using code: mutate(CollectionComments = case_when(MatrixName=='CONDITION' ~ 'COMMENT', TRUE ~ MatrixName)), #STANDARD CODE TO CHANGE GROUPING NAMES DWR_new <- DWR_new %>% mutate(WBT = recode(WBT, "River and Stream" = "River/Stream", "State Water Project" = "Drain/Canal", "General (UTM)" = "Not Recorded", "Broad Water Body" = "Not Recorded") ) unique(paste(DWR_new$MatrixName, DWR_new$WBT, sep='; ')) #New naming structure for Matrix Name & WBT Groupings
## [1] "Aqueous; River/Stream" "Aqueous; Not Recorded" "Aqueous; Drain/Canal"
# Standardize Analyte Groups - "Mercury, Total", "Mercury, Dissolved", "Mercury, Suspended", & same for Methylmercury # DWR_new <- DWR_new %>% mutate(Analyte = recode(Analyte, "Total Mercury" = "Mercury, Total", "Dissolved Mercury" = "Mercury, Dissolved" ) ) # Format Result Column to Numeric# # Check column for text - based on text user needs to decide what to do if(any(grepl('<|[a-df-zA-DF-Z]',DWR_new$Result))){ old <-DWR_new$Result new <-DWR_new$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)), ".\n '< R.L' changed to NA and DNQ added to ResultQualCode Column.\n")) DWR_new <- DWR_new %>% mutate( ResultQualCode = if_else(Result=="< R.L.", "DNQ", "="), # checked data spreadsheet that using a default of "=" was ok. Result = if_else(Result=="< R.L.", NA_character_, Result), Result = as.numeric(Result) ) } else { cat("'Result' column converted to numeric format\n") DWR_new$Result <- as.numeric(DWR_new$Result)}
## 'Result' column should be numeric but some cells contain < R.L.. ## '< R.L' changed to NA and DNQ added to ResultQualCode Column.
# Format MDL Column to Numeric# # Check column for text - based on text user needs to decide what to do if(!is.numeric(DWR_new$MDL)){ old <-DWR_new$MDL new <-DWR_new$MDL new[grepl('[a-df-zA-DF-Z]', new)] <- NA #skip 'e' for exponential notation e.g., "8e-005" #Print what text was found and what is being done cat(paste0("'Result' column should be numeric but some cells contain ", grammaticList(setdiff(old, new)), ".\nACTIONS TAKEN:\n", "~explain here~.\n")) #DWR_new <- DWR_new %>% # mutate( #Due stuff to prep column to be converted to Numeric # MDL = as.numeric(new) # ) } else { cat("'MDL' column is in numeric format") DWR_new$MDL <- as.numeric(DWR_new$MDL)}
## 'MDL' column is in numeric format
# Format RL Column to Numeric# # Check column for text - based on text user needs to decide what to do if(!is.numeric(DWR_new$RL)){ old <-DWR_new$RL new <-DWR_new$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")) #DWR_new <- DWR_new %>% # mutate( #Due 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(DWR_new) #Number rows before
## [1] 2974
#CODE BELOW REQUIRES USER TROUBLESHOOTING DEPENDING ON AVAILABLE COLUMNS AND SPREADSHEET SPECIFIC CONDITIONS# DWR_new <- DWR_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 <- DWR_new %>% #Record rows where Result, MDL, & RL all equal <NA> filter( is.na(Result) & is.na(MDL) & is.na(RL) ) nrow(na_results)
## [1] 0
DWR_new <- anti_join(DWR_new, na_results, by='SourceRow') #returns rows from DWR_new not matching values in no_result nrow(DWR_new) #Number rows after
## [1] 2974
# Format Units Column - "ng/L", "mg/Kg" unique(DWR_new$Unit) #Identifies OLDNAMES
## [1] "ng/L" "mg/L"
# If more than 1 unit colmn exists (e.g., for RL and MDL columns) see WQP script for example on merging into 1 column DWR_new <- DWR_new %>% standardizeUnits unique(DWR_new$Unit) #New naming structure for Unit Groupings
## [1] "ng/L"
# Format Date and Time Column # # NEED TO TALK ABOUT HOW WE WANT TO DO THIS - To graph in R we need Date and Time in same column # THE EXAMPLE CODE BELOW ASSUMES DATE AND TIME ARE IN SEPERATE COLUMNS #date and time already in same column DWR_new <- DWR_new %>% mutate( #FORMAT SampleDateTime COLUMN TO DATE FORMAT SampleDateTime = SampleDate, #SampleDate is already in Date & Time format SampleDate = format(lubridate::ymd_hms(SampleDateTime), "%Y-%m-%d"), SampleTime = format(lubridate::ymd_hms(SampleDateTime), "%H:%M:%S") ) #View(DWR_new) ## SAVE FORMATTED DATA AS EXCEL FILE ## writexl::write_xlsx(DWR_new, path='Reeval_Impl_Goals_Linkage_Analysis/Data/Aqueous/DWR_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=C ## [3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C ## [5] LC_TIME=English_United States.utf8 ## system code page: 65001 ## ## attached base packages: ## [1] grid stats graphics grDevices utils datasets methods base ## ## other attached packages: ## [1] mgcv_1.8-41 nlme_3.1-160 lubridate_1.8.0 plotly_4.10.0 ## [5] readxl_1.4.1 actuar_3.3-0 NADA_1.6-1.1 forcats_0.5.2 ## [9] stringr_1.4.1 dplyr_1.0.9 purrr_0.3.4 readr_2.1.2 ## [13] tidyr_1.2.0 tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2 ## [17] fitdistrplus_1.1-8 survival_3.4-0 MASS_7.3-58.1 ## ## loaded via a namespace (and not attached): ## [1] httr_1.4.4 jsonlite_1.8.0 viridisLite_0.4.1 splines_4.2.2 ## [5] modelr_0.1.9 assertthat_0.2.1 highr_0.9 googlesheets4_1.0.1 ## [9] cellranger_1.1.0 yaml_2.3.5 pillar_1.8.1 backports_1.4.1 ## [13] lattice_0.20-45 glue_1.6.2 digest_0.6.29 rvest_1.0.3 ## [17] colorspace_2.0-3 htmltools_0.5.3 Matrix_1.5-1 pkgconfig_2.0.3 ## [21] broom_1.0.1 haven_2.5.1 webshot_0.5.3 scales_1.2.1 ## [25] tzdb_0.3.0 googledrive_2.0.0 generics_0.1.3 ellipsis_0.3.2 ## [29] withr_2.5.0 lazyeval_0.2.2 cli_3.3.0 magrittr_2.0.3 ## [33] crayon_1.5.1 evaluate_0.16 fs_1.5.2 fansi_1.0.3 ## [37] xml2_1.3.3 tools_4.2.2 data.table_1.14.2 hms_1.1.2 ## [41] expint_0.1-7 gargle_1.2.0 lifecycle_1.0.1 munsell_0.5.0 ## [45] reprex_2.0.2 writexl_1.4.0 compiler_4.2.2 rlang_1.0.5 ## [49] rstudioapi_0.14 htmlwidgets_1.5.4 crosstalk_1.2.0 rmarkdown_2.16 ## [53] gtable_0.3.1 DBI_1.1.3 R6_2.5.1 knitr_1.40 ## [57] fastmap_1.1.0 utf8_1.2.2 stringi_1.7.8 vctrs_0.4.1 ## [61] dbplyr_2.2.1 tidyselect_1.1.2 xfun_0.32
Sys.time()
## [1] "2023-12-29 15:05:33 PST"