This report is automatically generated with the R
package knitr
(version 1.40
)
.
source("R Functions/functions_QA data.R") ###there are 4 field duplicates: #R5AQ7102 #R5AQ7132 #R5AQ7185 #R5AQ7277 #that need to be matched with an original environmental sample ###the rest are field duplicates that have alredy been averaged. this is noted in the columns: # 'Sample Type Code' (avg of field duplicates) # and 'Sample Comments' which lists the values of the 2 duplicates averaged ###some ND duplicates may have assumed a value of half the MDL to average ### LOAD DATA ### R5AQ <- readxl::read_excel('Reeval_Impl_Goals_Linkage_Analysis/Data/Aqueous/R5AQ.xlsx', sheet='Aqueous Hg data Rivers R5 only', guess_max = 30000) nrow(R5AQ) #number of rows should match the Excel file (minus the header row)
## [1] 7290
### 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','Source','Project','StationName','StationCode','SampleDate','CollectionTime','LabBatch','LabSampleID','MatrixName', 'WBT','MethodName','Analyte','Unit','Result','MDL','RL','ResultQualCode','SampleID','SampleComments','TargetLatitude', 'TargetLongitude','QACode','BatchVerification','ComplianceCode','CollectionComments','ResultsComments','BatchComments','SampleTypeCode' ) temp_cols <- c('DataType') #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 R5AQ_new <- R5AQ %>% select( c(keep_cols,temp_cols) ) %>% #DO NOT CHANGE - selects columns specified above mutate( #Add user defined columns CoordSystem = NA ) %>% rename( 'SampleTime' = 'CollectionTime', 'CitationCode' = 'Source' ) %>% filter( DataType == 'Reported' | DataType == 'Field dup', #only keep rows where DataType is 'Reported' or 'Field dup' - these values are not part of the lab QA/QC WBT != 'Not Applicable', #appears to be QC data - (only 2 occurrences when DataType == 'Reported') WBT != 'Well' #Well data does not concern lowland rivers or delta - visually checked that data was not applicable ) nrow(R5AQ_new)
## [1] 5689
#str(R5AQ_new) #View(R5AQ_new) ### FORMAT COLUMN PARAMETERS ### # Standardize MatrixName Groups - "Water", "Sediment", "Soil" # unique(R5AQ_new$MatrixName) #Identifies OLDNAMES
## [1] "samplewater"
R5AQ_new <- R5AQ_new %>% mutate( MatrixName = recode(MatrixName, "samplewater" = "Aqueous") ) unique(R5AQ_new$MatrixName) #New naming structure should now be listed
## [1] "Aqueous"
# Standardize WBT (WaterBodyType) Groups - "River/Stream", "Drain/Canal", "Wetland", "Spring", "Slough", # "Pond", "Lake/Reservoir", "Delta", "Forebay/Afterbay", "Not Recorded" # unique(R5AQ_new$WBT) #Identifies OLDNAMES
## [1] "Creek" "Drain/Canal" "Slough" "River" ## [5] "Spring" "Pond" "Wetland" "Marsh" ## [9] "Not Recorded" "Lake/Reservoir" "Canal" "Gulch" ## [13] "Delta" "Forebay/Afterbay" "Tributary"
R5AQ_new <- R5AQ_new %>% mutate(WBT = recode(WBT, "Creek" = "River/Stream", "River" = "River/Stream", "Marsh" = "Wetland", "Canal" = "Drain/Canal", "Gulch" = "River/Stream", #only applies to Harley Gulch - will be filtered out in GIS since not within lowland river or delta scope "Tributary" = "River/Stream"), #EXAMPLE FOR WHEN "OLDNAME" is 'NA' but we want a NEWNAME - if this example is deleted, also delete the comma after "Not Recorded" above WBT = case_when(is.na(WBT) ~ "Not Recorded", #Use "Not Recorded" when WBT value is NA TRUE ~ WBT) #Keep original WBT value in all other cases ) unique(paste(R5AQ_new$MatrixName, R5AQ_new$WBT, sep='; ')) #New naming structure for Matrix Name & WBT Groupings
## [1] "Aqueous; River/Stream" "Aqueous; Drain/Canal" "Aqueous; Slough" ## [4] "Aqueous; Spring" "Aqueous; Pond" "Aqueous; Wetland" ## [7] "Aqueous; Not Recorded" "Aqueous; Lake/Reservoir" "Aqueous; Delta" ## [10] "Aqueous; Forebay/Afterbay"
# Standardize ResultQualCode Groups - "ND", "DNQ", NA# unique(R5AQ_new$ResultQualCode) #Identifies OLDNAMES
## [1] "=" "ND" "DNQ"
#[1] "=" "ND" "DNQ" - no changes necessary # 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]',R5AQ_new$Result))){ old <-R5AQ_new$Result new <-R5AQ_new$Result new[grepl('<|[a-df-zA-DF-Z]', new)] <- NA #skip 'e' for exponential notation e.g., "8e-005" #Print what text was found and what is being done cat(paste0("'Result' column should be numeric but some cells contain ", grammaticList(setdiff(old, new)), ".\nACTIONS TAKEN:\n", "Numeric MDL value from SampleComments column was placed in MDL column when MDL column = '-88'.\n", "Result value of '<MDL' replaced with 'NA'.\n") ) mdl_pattern <- ".*MDL=[[:space:]](.*)[[:space:]]ng.*" #regex simplified: capture any character group between "MDL= " and " ng"; #Regex explained: ".*"=any characters before; "MDL=[[:space:]]"=the string 'MDL= '; "(.*)"=capturing any character group ; "[[:space:]]ng"=characters ' ng'; ".*"=any characters after R5AQ_new <- R5AQ_new %>% mutate( MDL = as.character(MDL), #temporarily convert MDL column to character to insert the numeric text string from SampleComments column MDL = case_when(Result == "<MDL" & MDL == "-88" & grepl("ng/L", SampleComments) ~ sub(mdl_pattern, '\\1', SampleComments), #"\\1" returns 1st pattern group; returns original string if no match TRUE ~ MDL), MDL = as.numeric(MDL), # revert MDL column to numeric Result = case_when(Result == "<MDL" ~ NA_character_, TRUE ~ Result), Result = as.numeric(new) ) } else { cat("'Result' column converted to numeric format\n") R5AQ_new$Result <- as.numeric(R5AQ_new$Result) }
## 'Result' column should be numeric but some cells contain <MDL. ## ACTIONS TAKEN: ## Numeric MDL value from SampleComments column was placed in MDL column when MDL column = '-88'. ## Result value of '<MDL' replaced with 'NA'.
#View(R5AQ_new) # Check if Result, MDL, & RL Columns all equal <NA> or 0 - these rows have no useful information nrow(R5AQ_new) #Number rows before
## [1] 5689
#CODE BELOW REQUIRES USER TROUBLESHOOTING DEPENDING ON AVAILABLE COLUMNS AND SPREADSHEET SPECIFIC CONDITIONS# R5AQ_new <- R5AQ_new %>% #Set 0 & negative values as blank mutate(Result = ifelse(Result <= 0, NA_real_, Result), MDL = ifelse(MDL <= 0, NA_real_, MDL), RL = ifelse(RL <= 0, NA_real_, RL)) na_results <- R5AQ_new %>% #Record rows where Result, MDL, & RL all equal <NA> filter( is.na(Result) & is.na(MDL) & is.na(RL) ) nrow(na_results)
## [1] 27
R5AQ_new <- anti_join(R5AQ_new, na_results, by='SourceRow') #returns rows from R5AQ_new not matching values in no_result nrow(R5AQ_new) #Number rows after
## [1] 5662
# Format Units Column - "ng/L", "mg/Kg" unique(R5AQ_new$Unit) #Identifies OLDNAMES
## [1] "ng/L"
R5AQ_new <- R5AQ_new %>% standardizeUnits unique(R5AQ_new$Unit)
## [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 R5AQ_new <- R5AQ_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 ### R5AQ_new <- R5AQ_new %>% select(-one_of(temp_cols)) #Remove temp columns since they are no longer needed #View(R5AQ_new) ## SAVE FORMATTED DATA AS EXCEL FILE ## writexl::write_xlsx(R5AQ_new, path='Reeval_Impl_Goals_Linkage_Analysis/Data/Aqueous/R5AQ_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 15:59:54 PST"