This report is automatically generated with the R
package knitr
(version 1.40
)
.
source("R Functions/functions_QA data.R") ### LOAD DATA ### AmericanRiver <- readxl::read_excel('Reeval_Impl_Goals_Linkage_Analysis/Data/Aqueous/AmericanRiver.xlsx', sheet='Trib-Res Data112818', guess_max = 30000) nrow(AmericanRiver) #number of rows should match the Excel file (minus the header row)
## [1] 1366
### 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', 'Project Code', 'Station Name - Grouped', 'Project Station Code', 'Sample Date', 'Sample Time','Comments', 'Analyte Name','Fraction Name','Units', 'Result [ND = MDL]','Detection Limit', 'WB Type', 'Weather','lat', 'long', 'Project Result Qual Code', 'Laboratory Qual Code') temp_cols <- c('Result [ND = 1/2 MDL]') #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 rl_pattern <- ".*RL=[[:space:]]?(.*)[[:space:]]ng.*" AmericanRiver_new <- AmericanRiver %>% select( c(keep_cols,temp_cols) ) %>% #DO NOT CHANGE - selects columns specified above; add temp columns if needed mutate( #Add user defined columns CoordSystem = NA_character_, Project = NA_character_, LabBatch = NA_character_, LabSampleID = NA_character_, MatrixName = 'Aqueous', RL = if_else(grepl(rl_pattern, Comments), sub(rl_pattern, '\\1', Comments), NA_character_), MethodName = case_when(grepl('7470A', Comments) ~ 'Method 7470A', TRUE ~ NA_character_), `Result [ND = MDL]` = as.numeric(`Result [ND = MDL]`), `Result [ND = 1/2 MDL]` = as.numeric(`Result [ND = 1/2 MDL]`), # Assume ResultQualCode is '=' except for the following conditions ResultQualCode = case_when(grepl('DNQ',`Project Result Qual Code`) ~ 'DNQ', grepl('EST',`Project Result Qual Code`) ~ 'E', grepl('J',`Project Result Qual Code`) ~ 'DNQ', `Result [ND = 1/2 MDL]` == `Result [ND = MDL]`/2 ~ 'ND', TRUE ~ '='), #grepl('UJ',`Project Result Qual Code`) & grepl(`U`,`Laboratory Qual Code`) ~ 'ND'), #(U code doesn't necessarily mean ND) SampleID = NA_character_, BatchVerification = NA_character_, ComplianceCode = NA_character_, BatchComments = NA_character_, SampleTypeCode = NA_character_ ) %>% rename ( #Rename columns to CEDEN standards #CEDEN 'COLUMNNAME' = AmericanRiver '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_ 'CitationCode' = 'Project Code', 'StationName' = 'Station Name - Grouped', 'StationCode' = 'Project Station Code', 'SampleDate' = 'Sample Date', 'SampleTime' = 'Sample Time', 'WBT' = 'WB Type', 'Analyte' = 'Analyte Name', 'Analyte_part2' = 'Fraction Name', 'Unit' = 'Units', 'Result' = 'Result [ND = MDL]', 'MDL' = 'Detection Limit', 'SampleComments' = 'Weather', 'TargetLatitude' = 'lat', 'TargetLongitude' = 'long', 'QACode' = 'Project Result Qual Code', 'CollectionComments' = 'Laboratory Qual Code', 'ResultsComments' = 'Comments' ) #make ND results blank and move those values over to MDL column ###watch out for the 6 that need correcting! (noted in original spreadsheet) AmericanRiver_new <- AmericanRiver_new %>% mutate( MDL= ifelse(ResultQualCode == 'ND' & is.na(MDL), Result, MDL), Result = ifelse(ResultQualCode == 'ND', NA_real_, Result) ) #no blanks in MDL column have any further detection limit information in QC notes columns (comments, Qual codes, etc.) #flags 'U' and 'J' are not explained in associated lab code tab; assumed that U was non detect and J was do not quanitify, #but looking at the data there were a few combinations of U and J. Assumed if 'Result [ND=1/2 MDL]' column value was #half of the'Result [ND = MDL]' column value, then it is an ND; if there are 'Project Result Qual Code' values = DNQ, EST, J #then those all equal NDQ (except when 'Project Result Qual Code' = UJ and 'Laboratory Qual Code' column is 'U' value, then it is also an ND) #not all data labeled as 'U' code in the original spredsheet's project result qual code were ND - only about half glancing at the data. #so I only labeled NDs from if the 1/2 MDL column was half of the result = MDL column nrow(AmericanRiver_new)
## [1] 1366
#View(AmericanRiver_new) ### FORMAT COLUMN PARAMETERS ### # Standardize Analyte Groups # unique(AmericanRiver_new$Analyte)
## [1] "Mercury, Total" "Methylmercury"
unique(AmericanRiver_new$Analyte_part2)
## [1] "Unfiltered" "Filtered"
unique(paste(AmericanRiver_new$Analyte, AmericanRiver_new$Analyte_part2, sep=', '))
## [1] "Mercury, Total, Unfiltered" "Methylmercury, Unfiltered" "Mercury, Total, Filtered" ## [4] "Methylmercury, Filtered"
AmericanRiver_new <- AmericanRiver_new %>% mutate(Analyte = case_when(Analyte == "Mercury, Total" & Analyte_part2 == "Filtered" ~ "Mercury, Dissolved", Analyte == "Methylmercury" & Analyte_part2 == "Filtered" ~ "Methylmercury, Dissolved", Analyte == "Methylmercury" & Analyte_part2 == "Unfiltered" ~ "Methylmercury, Total", TRUE ~ Analyte) )%>% select(-Analyte_part2) #Remove 'Analyte_part2' column unique(AmericanRiver_new$Analyte)
## [1] "Mercury, Total" "Methylmercury, Total" "Mercury, Dissolved" ## [4] "Methylmercury, Dissolved"
# Format MDL Column to Numeric# # Check column for text - based on text user needs to decide what to do if(any(grepl('[a-df-zA-DF-Z]',AmericanRiver_new$MDL))){ old <-AmericanRiver_new$MDL new <-AmericanRiver_new$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")) #AmericanRiver_new <- AmericanRiver_new %>% # mutate( #Due stuff to prep column to be converted to Numeric # MDL = as.numeric(new) # ) } else { cat("'MDL' column converted to numeric format\n") AmericanRiver_new$MDL <- as.numeric(AmericanRiver_new$MDL)}
## 'MDL' column converted to numeric format
# Format RL 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]',AmericanRiver_new$RL))){ old <-AmericanRiver_new$RL new <-AmericanRiver_new$RL new[grepl('[a-df-zA-DF-Z]', new)] <- NA #skip 'e' for exponential notation e.g., "8e-005" #Print what text was found and what is being done cat(paste0("'RL' column should be numeric but some cells contain ", grammaticList(setdiff(old, new)), ".\nACTIONS TAKEN:\n", "~explain here~.\n")) #AmericanRiver_new <- AmericanRiver_new %>% # mutate( #Due stuff to prep column to be converted to Numeric # RL = as.numeric(new) # ) } else { cat("'RL' column converted to numeric format\n") AmericanRiver_new$RL <- as.numeric(AmericanRiver_new$RL)}
## 'RL' column converted to numeric format
# Check if Result, MDL, & RL Columns all equal <NA> or 0 - these rows have no useful information nrow(AmericanRiver_new) #Number rows before
## [1] 1366
#CODE BELOW REQUIRES USER TROUBLESHOOTING DEPENDING ON AVAILABLE COLUMNS AND SPREADSHEET SPECIFIC CONDITIONS# AmericanRiver_new <- AmericanRiver_new %>% #Set 0 & negative values as blank mutate(Result = ifelse(Result <= 0, NA_real_, Result), MDL = ifelse(MDL <= 0, NA_real_, MDL), RL = ifelse(RL <= 0, NA_real_, RL)) na_results <- AmericanRiver_new %>% #Record rows where Result, MDL, & RL all equal <NA> or 0 filter( is.na(Result) & is.na(MDL) & is.na(RL) ) #View(na_results) AmericanRiver_new <- anti_join(AmericanRiver_new, na_results, by='SourceRow') #returns rows from AmericanRiver_new not matching values in no_result nrow(AmericanRiver_new) #Number rows after - if number rows is the same as before this section can be deleted
## [1] 1366
# Format Units Column - "ng/L", "mg/Kg" unique(AmericanRiver_new$Unit) #Identifies OLDNAMES
## [1] "ng/l" "ng/L"
# If more than 1 unit colmn exists (e.g., for RL and MDL columns) see XXX script for example on merging into 1 column AmericanRiver_new <- AmericanRiver_new %>% standardizeUnits unique(AmericanRiver_new$Unit) #New naming structure for ResultQualCode 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 AmericanRiver_new <- AmericanRiver_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 ### AmericanRiver_new <- AmericanRiver_new %>% select(-one_of(temp_cols)) #Remove temp columns since they are no longer needed #View(AmericanRiver_new) ## SAVE FORMATTED DATA AS EXCEL FILE ## writexl::write_xlsx(AmericanRiver_new, path='Reeval_Impl_Goals_Linkage_Analysis/Data/Aqueous/AmericanRiver_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 13:12:15 PST"