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LoonDataClean2018_03_09.R
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library(data.table)
library(lubridate)
## Load loon Contaminant data https://doi.org/10.5066/P9TDCH3F
dt1 <- fread("1409727_FINAL_EXCEL_17_Nov_14_1743.csv")
dt2 <- fread("1409729_FINAL_EXCEL_17_Nov_14_1740.csv")
dt3 <- fread("1409731_FINAL_EXCEL_17_Nov_14_1739.csv")
dtAll <- rbind(dt1, dt2, dt3)
head(dtAll)
dtUse <- dtAll[ , list(SAMPLENAME, MATRIX, ANALYTE, Result, UNITS)]
dtUse[ , Year :=
as.numeric(gsub("(\\d{2,4}-\\d{2,6}|No Band)(_| _)(\\d{4}) (\\(.*)",
"\\3",
SAMPLENAME, perl = TRUE))]
dtUse[ , unique(Year)]
dtUse[ , Band :=
gsub("(\\d{2,4}-\\d{2,6}|No Band)(_| _)(\\d{4}) (\\(.*)",
"\\1",
SAMPLENAME, perl = TRUE)]
## Fix data mistake
dtUse[ Band == "1038-994250", Band := "1038-94250"]
dtUse[ , temp :=
gsub("(\\d{2,4}-\\d{2,6}|No Band)(_| _)(\\d{4}) (\\(.*)",
"\\4",
SAMPLENAME, perl = TRUE)]
dtUse[ , Lake := gsub("\\(|\\)|( F.*$)|( M.*$)|( A.*$)|( J.*$)|( U.*$)",
"", temp)]
## Note that Loon Marsh does not have a MN lake ID
dtUse[ 43:46,] # To see Loon Marsh
dtUse[ c(223:240, 493:516),] ## Add in Fox to Lake name
dtUse[ c(223:240, 493:516), Lake := paste(Lake, "Fox")]
dtUse[ , Lake]
dtUse[ , unique(Lake)]
# Remove sample error (unknown adult)
dtUse <- dtUse[-grep("1138-06641", dtUse$Band),]
## CLEAN UP Lake data and names
lakeKey <- fread("lakeSubs.csv")
lakeKey
dtUse[ , unique(Band)]
for(index in 1:dim(lakeKey)[1]){
dtUse[ Lake == lakeKey[ index, old], Lake := lakeKey[ index, new]]
}
dtUse[ Lake == "Stumpf Lake", Lake := "Stump Lake"]
dtUse[, Lake := gsub("( Lake)|( Bay)|(Lake )", "", Lake)]
## Read in Lake ID nums
ld <- fread("LakeIDName.csv")
setnames(ld, c("Lake", "rm1", "LakeID", "rm2"))
ld[ , c("rm1", "rm2") := NULL]
setkey(ld, "Lake")
setkey(dtUse, "Lake")
## Check to make sure all of the dtUse Lakes are in the ld
unique(dtUse)[ , Lake][! unique(dtUse[ , Lake]) %in% ld[ , Lake]]
## See which LDs we did not use
ld[ , Lake][!ld[ , Lake] %in% unique(dtUse[ , Lake])]
dtUse <- copy(ld[ dtUse])
dtUse
dtUse[ is.na(LakeID), unique(Lake)]
## Edit Lake codes
## Big Birch, Mantrap, Monogalia, North/South Tamarac.
dtUse[ grep("Mantrap" , Lake), LakeID := '29015104']
dtUse[ grep("Mantrap" , Lake), Lake := 'Mantrap (West Arm)']
dtUse[ grep("Big Birch" , Lake), LakeID := '77008401']
dtUse[ grep("Big Birch" , Lake), Lake := "Big Birch (NE portion)"]
## Clean up Monogalia Lake (has two sublakes in system)
dtUse[ grep("Monongalia", Lake), LakeID := '34015800']
dtUse
dtUse[ grep("Crow River", SAMPLENAME), Lake := "Monongalia - Middle Fork Crow River"]
dtUse[ grep("Crow River", Lake), LakeID := '34015802']
## Merge in Band info
band <- fread("LoonBands_revised 26jul17.csv")
## Remove loon entires Kevin marked to remove
band <- copy(band[ Remove == 0,])
setnames(band, "Body (mm)", "BodyLength")
bandUse <- band[ , list(Age, Sex, Date, Band, Mass)]
## Change date
bandUse[ , Date := as.POSIXct(strptime(Date, "%d-%b-%y"))]
setkey(dtUse, "Band")
setkey(bandUse, "Band")
## only the no Band is missing info about age and sex
dtBoth <- copy(bandUse[dtUse])
dtBoth[ Band == "No Band",]
dtBoth[ Band == "No Band", Age := "Juvenile"]
dtBoth[ Band == "No Band", Sex := "Unknown"]
dtBoth[ Sex == "Unk", Sex := 'Unknown']
dtBoth[ Age == "Juv", Age := 'Juvenile']
dtBoth[ ANALYTE == "% Solids", ANALYTE := "Solids"]
dtBoth[ , temp := NULL]
dtBoth[ , MATRIX := NULL]
colnames(dtBoth)
head(dtBoth)
dtBoth[ Result == "ND" & ANALYTE == "Selenium", Result := 0.2]
dtBoth[ Result == "ND" & ANALYTE == "Aluminum", Result := 0.93]
dtBoth[ Result == "ND" & ANALYTE == "Cadmium", Result := 0.005]
dtBoth[ Result == "ND" & ANALYTE == "Lead", Result := 0.01]
dtBoth[ Result == "ND" & ANALYTE == "Solids", Result := 0.2 ]
dtBoth[ , Result := as.numeric(Result)]
## Have both Analytes and and Samples merged together
## LOOK AT non-detect data
library(ggplot2)
chems <- ggplot(dtBoth, aes(x = Result)) + geom_histogram() +
facet_grid( . ~ ANALYTE, scales = "free_x" )
chems
ggsave("hist.pdf", chems, width = 10, height = 4)
sink("ND data.txt")
print(dtBoth[ , list(min = min(Result), Zeros = length(which(Result == 0))), by = ANALYTE])
sink()
## Cast data into wide format (end thing to do)
colnames(dtBoth)
dtBoth[ , SAMPLENAME := NULL]
dtBoth[ , UNITS := NULL]
dtWide <- copy(dcast(dtBoth, Year + Lake + LakeID + Age + Sex +
Band + Mass + Date ~ ANALYTE, value.var = 'Result'))
dtWide[ grep("Loon", Lake), LakeID := '111111111']
############################################################
############################################################
############################################################
## load in water quliaty data
dWQ <- fread("WATERQUALITY_ALL_LAKES_20July2017.csv")
## Change loon marsh to have the same dummy ID used elsewhere in code
dWQ[ grep("LOON", LOC_DESC), LOC_ID := '111111111']
## fix wrong loc_id code
dWQ[ LOC_DESC == "POINT", LOC_ID := "34_0193_00_201"]
## Change ALK 1/2 of detection limit
dWQ[ ALK_2320B == "< 10", ALK_2320B := '5']
dWQ[ , ALK_2320B := as.numeric(ALK_2320B)]
## Taking a median of Alk and P across different methods of measuring
dWQ[ , ALK := apply(dWQ[ , grep( "ALK", colnames(dWQ)), with = FALSE],
1, median, na.rm = TRUE)]
dWQ[ , P_3654 := as.numeric(gsub("< ", "", P_3654))]
dWQ[ , Phosp := apply(dWQ[ , grep( "P_", colnames(dWQ)), with = FALSE],
1, median, na.rm = TRUE)]
## Remove CHLA and take median of other measure types
dWQ[ , c( "CHLA_10200H_UNCORR", "CHLA_ASTMD373187_UNCORR") := NULL]
dWQ[ , CHLA := apply(dWQ[ , grep( "CHLA", colnames(dWQ)), with = FALSE],
1, median, na.rm = TRUE)]
dWQ2 <- copy(dWQ[ , list(LOC_ID, LOC_DESC, SAMPLE_DATE,
SECCHI, PH, Phosp, ALK, CHLA, LAKE_AREA, MAX_DEPTH,
TSI)])
## Clean up secchi
dWQ2[ , SECCHI := as.numeric(gsub("> ", "", SECCHI))]
dWQ2 <- copy(dWQ2[ , list(SECCHI = median(SECCHI, na.rm = TRUE), Phosp = median(Phosp, na.rm = TRUE),
PH = median(PH, na.rm = TRUE), ALK = median(ALK, na.rm = TRUE),
CHLA = median(CHLA, na.rm = TRUE), LAKE_AREA =median(LAKE_AREA, na.rm = TRUE),
MAX_DEPTH = median(MAX_DEPTH, na.rm = TRUE), TSI = median(TSI, na.rm = TRUE)), by = list(LOC_ID, LOC_DESC, SAMPLE_DATE)])
## NEXT, convert Date to date formate as well as time, then remove months outsides of
## of May through July
dWQ2[, SAMPLE_DATE := as.POSIXct(SAMPLE_DATE, format = "%m/%d/%Y")]
library(lubridate)
dWQ2[ , Year := year(SAMPLE_DATE)]
dWQ2[ , Month := month(SAMPLE_DATE)]
dWQ3 <- copy(dWQ2)
dWQ3[ , LakeID := as.numeric(gsub( "(\\d{2})_(\\d{4})_(\\d{2})(.*)",
"\\1\\2\\3",
LOC_ID))]
wqLakes <- dWQ3[ , list(LakeID = round( mean(LakeID))), by = LOC_DESC]
loonLakes <- dtWide[ , list(LakeID = round(mean(as.numeric(LakeID)))), by = Lake]
loonLakes[ Lake == "Loon", LakeID := 111111111]
wqLakes[ LOC_DESC == "LOON_MARSH", LakeID := 111111111]
wqLakes[ grepl("MANTRAP", LOC_DESC), LakeID := 29015104]
wqLakes[ LOC_DESC == "WEST_FOX", LakeID := 18029700]
wqLakes[ LOC_DESC == "ANNA", LakeID := 56044800]
wqLakes[ LOC_DESC == "STUMP", LakeID := 73009100]
setnames(wqLakes, 'LOC_DESC', 'WQ_Lakes')
setnames(loonLakes, 'Lake', 'Loon_Lakes')
setkey(loonLakes, "LakeID")
setkey(wqLakes, "LakeID")
wqLakes[ loonLakes, allow.cartesian = TRUE][ order(Loon_Lakes), ]
areaDepth <- copy(dWQ3[ , list(
AREA = mean(LAKE_AREA, na.rm = TRUE),
MAXdepth = mean(MAX_DEPTH, na.rm = TRUE)),
by = list(LOC_DESC, LakeID )])
areaDepth <- areaDepth[ complete.cases(areaDepth),]
## Most Conservative Scenario, pool everything including years
dWQ3[ LOC_DESC == "MANTRAP_WEST_ARM", LakeID := 29015104]
## Extarct out data so only within year
dWQ5a <- copy(dWQ3[ Month %in% 5:7, list(SECCHI = mean(SECCHI, na.rm = TRUE),
PH = mean(PH, na.rm = TRUE),
Phosp = mean(Phosp, na.rm = TRUE),
ALK = mean(ALK, na.rm = TRUE),
CHLA = mean(CHLA, na.rm = TRUE),
TSI = mean(TSI, na.rm = TRUE)
),
by = list(LOC_DESC, LakeID)
])
dWQ5a[ LOC_DESC == "LOON_MARSH", LakeID := 111111111]
setkey(areaDepth, "LakeID")
setkey(dWQ5a, "LakeID")
areaDepth[ , LOC_DESC := NULL]
dWQ5 <- copy(areaDepth[ dWQ5a])
## Will need to reformat LOC_ID to be the same as dtWide
dWQ5[ , LakeID := as.character(LakeID)]
## Add zeros to front of Tamarack lakes' numbers
dWQ5[ grep("^302410", LakeID), ]
dWQ5[ grep("^302410", LakeID), LakeID := paste0("0", LakeID)]
dWQ5[ grep("^302410", LakeID), ]
dWQ5[ grep("302410", LakeID), ]
## And also George and Blackbird
dWQ5[ grep("^2009100", LakeID), ]
dWQ5[ grep("^2009100", LakeID), LakeID := paste0("0", LakeID)]
dWQ5[ grep("^3019700", LakeID), ]
dWQ5[ grep("^3019700", LakeID), LakeID := paste0("0", LakeID)]
## Mke sure turtle is the same across
dWQ[ grep("TURT", LOC_DESC), max(MAX_DEPTH, na.rm = TRUE)]
dWQ2[ grep("TURT", LOC_DESC), max(MAX_DEPTH, na.rm = TRUE)]
dWQ3[ grep("TURT", LOC_DESC), max(MAX_DEPTH, na.rm = TRUE)]
dWQ5[ grep("TURT", LOC_DESC) ]
dtWide[ grep("Turt", Lake) ]
# Get dates and months
dWQ3[ , Date := as_date(SAMPLE_DATE)]
dWQ3[ , Month:= month(Date)]
# Write loon data for fish model
write.csv(x = dtWide, file = "LoonHGblood.csv")
########################################
###### CORRECT WATER QUALITY ID's ######
########################################
dWQ5[ , LakeID := gsub( "^0", "", LakeID)]
dtWide[ , LakeID := gsub( "^0", "", LakeID)]
dtWide[ grep("tamarac", Lake, ignore.case=T), LakeID := "3024102"]
dtWide[ grep("george", Lake, ignore.case = T), LakeID := "2009100" ]
dtWide[grep("clearwater", Lake, ignore.case=T), LakeID := "18003800"]
dtWide[ grep("East Fox", Lake), LakeID := "18029700"]
dtWide[ grep("West Fox", Lake), LakeID := "18029700"]
dtWide[grep("east rabbit", Lake, ignore.case=T), LakeID := "18009301"]
dtWide[grep("west rabbit", Lake, ignore.case=T), LakeID := "18009302"]
dtWide[grep("burntside", Lake, ignore.case=T), LakeID := "69011800" ]
dtWide[grep("little birch", Lake, ignore.case=T), LakeID := "77008900"]
dtWide[grep("77008401", LakeID, ignore.case=T), LakeID := "77008400"]
dtWide[grep("wild|rice", Lake, ignore.case=T), LakeID := "69037100"]
dtWide[grep("(south.*turtle)|(turtle.*south)", Lake, ignore.case = T), LakeID := "56037700"]
dtWide[grep("Mantrap", Lake, ignore.case = T), LakeID := "29015100"]
dtWide[grep("East Vermilion", Lake, ignore.case = T), LakeID := "69037800"]
dtWide[grep("Monongalia", Lake, ignore.case = T), LakeID := "34015800"]
dtWide[dtWide$Lake == "East Fox", "LakeID"] = "18029800"
dtWide[ grep("tamarac", dtWide$Lake, ignore.case=T), "LakeID"] = "3024102"
dtWide[dtWide$Lake == "South Tamarack", "LakeID"] = "3024101"
dtWide[ grep("george", dtWide$Lake, ignore.case = T), "LakeID"] = "2009100"
dtWide[grep("clearwater", dtWide$Lake, ignore.case=T), "LakeID"] = "18003800"
dtWide[ grep("East Fox", dtWide$Lake), "LakeID"] = "18029800"
dtWide[grep("77008400", dtWide$LakeID, ignore.case=T), "LakeID"] = "77008401" #big birch
dtWide[grep("29015100", dtWide$LakeID, ignore.case = T), "LakeID"] = "29015104" #mantrap
dtWide[grep("69037800", dtWide$LakeID, ignore.case = T), "LakeID"] = "69037801" #east vermilion
dtWide[grep("Monongalia - main", dtWide$Lake, ignore.case = T), "LakeID"] = "34015801"
dtWide[grep("Monongalia.*crow", dtWide$Lake, ignore.case = T), "LakeID"] = "34015802"
dtWide[grep("Mckeown", dtWide$Lake, ignore.case = T), "LakeID"] = "11026100"
dtWide[grep("stump", dtWide$Lake, ignore.case = T), ]
########################################
########################################
########################################
# # set keys for joining
setkey(dtWide, 'LakeID')
setkey(dWQ5, 'LakeID')
## Merge data together
dtThree <- copy(dWQ5[dtWide])
# Write water quality data
write.csv(x = dWQ5, file = "WaterQualityCheck27July17_2018_03_09.csv", row.names = FALSE)
###################
###################
#### RUN MODEL ####
###################
###################
perchHG <- fread("perchHGAvg2018_03_09.csv")
# Create lakeYearID for perch/water data
perchHG[ , lakeYearID := paste(LakeID, Year, sep = "_")]
dtThree[ , lakeYearID := paste(LakeID, Year, sep = "_")]
# Set keys for joining data
setkey(dtThree, "lakeYearID")
setkey(perchHG, "lakeYearID")
# join water quality data to perch/loon data
dtFour <- perchHG[dtThree]
# check NAs
nrow(dtFour[!is.na(perchHG),])
# add in manual missing data from steve
dtFour[LakeID == 18029700, ALK := 121] #fix west fox lake ALK average
dtFour[LakeID == 18029800, ALK := 113] #fix east fox lake ALK average
dtFour[LakeID == 02009100, ALK := 71.6667] #fix george lake ALK average
dtFour[LakeID == 034006600, ALK := 175] #fix Long lake ALK average
dtFour[LOC_DESC == "POINT", CHLA := 5.75] #fix Point lake CHLA average
dtFour[LakeID == 77008401, ALK := 149.4286] #fix birch CHLA
dtFour[LakeID == 77008401, SECCHI := 2.9883] #fix birch CHLA
dtFour[LakeID == 77008401, PH := 8.6212] #fix birch CHLA
dtFour[LakeID == 77008401, Phosp := .0361] #fix birch CHLA
dtFour[LakeID == 77008401, CHLA := 9.9512] #fix birch CHLA
# add missing loon masses
dtFour[LakeID == 69011800 & is.na(Mass), Mass := 1.27]
dtFour[LakeID == 69037100 & is.na(Mass), Mass := .94]
#save results
write.csv(file = "LoonData2018_03_09.csv",
x = dtFour, row.names = FALSE)