Creating a Custom Function to Calculate Indices
Source:vignettes/custom-indices.Rmd
custom-indices.RmdIntroduction
To maximize flexibility in calculating indices, the
get_indices() function accepts custom written functions,
allowing users to create their own function and calculate indices that
may not be included in existing methods.
Function Requirements
-
The function needs to have the following arguments:
eemlist: an eemlist object containing EEM’s data.abslist: an abslist object containing absorbance data.cuvle: the cuvette (path) length in cm.qaqc_dir: the file path to the mdl files generated withcreate_mdl()
-
The function must return a list with the following items:
abs_index: the indices calculated from the absorbance dataeem_index: the indices calculated from the EEMs data
abs_index and eem_index should both be a
data.frame with the following columns:
sample_id: sample identifier for the sample (if provided, otherwise uses sample: the sample’s file name)sample_name: sample name or description (if provided, otherwise uses sample: the sample’s file name)index: a character, the name of the index being reportedvalue: the value of the index, also accepts flag values (see details below)
Helper Functions
The eemanalyzeR package contains a number of functions helpful in creating custom indices.
To get the absorbance values at a certain wavelength the
get_absorbance() function can be used. This function can
additionally provide specific absorbance (like SUVA 254).
abslist <- example_processed_abs
#absorbance at 254 nm
a254 <- eemanalyzeR::get_absorbance(abslist, 254)
a254
#> [1] "0.00054853647738303" "0.163215306070913" "0.0806502356009794"
#> [4] "0.300921250392953"
#specific absorbance at 254 nm
suva254 <- eemanalyzeR::get_absorbance(abslist, 254, suva=TRUE)
suva254
#> [1] "DOC01" "DOC01" "4.15722863922574" "DOC01"A similar function exists for getting fluorescence values from EEMs
data: get_fluorescence(). Here, you can specify a range of
excitation and/or emission wavelengths. If stat is set to
sum the sum of fluorescence will be returned, otherwise if
max is used the maximum fluorescence across that range will
be returned.
eemlist <- example_processed_eems
#get maximum fluorescence across ranges for Peak A and D
pA <- get_fluorescence(eemlist, ex=250:260, em=380:480, stat="max")
pA
#> [1] "0.0103767473213219" "0.280569719363857" "0.340345670357128"
#> [4] "0.721431003125204"
pD <- get_fluorescence(eemlist, ex=390, em=509, stat="max")
pD
#> [1] "0.000304395508989419" "0.0199829245761789" "0.0978435434033764"
#> [4] "0.0372706531424358"
#get sum of fluorescence across range of Peak A
pA_sum <- get_fluorescence(eemlist, ex=250:260, em=380:480, stat="sum")
pA_sum
#> [1] "0.398846395176868" "15.4652173718784" "29.1296537896885"
#> [4] "34.0595693438647"Lastly, there is a function for getting spectral slopes:
get_abs_slope() which will convert the absorbance to
absorption, interpolate the data if needed, and return the spectral
slope within a wavelength range.
S275_295 <- get_abs_slope(abslist, lim=c(275,295))
S275_295
#> [1] 0.01654700 0.02306185 0.01302367 0.02306070QA/QC Flags
While the get_indices() function includes some QA/QC
checks (negative numbers, missing values, below noise, outside expected
range, etc.), there are some checks that are easier to do when
calculating the indices, or you may wish to include custom flags or
checks. To pass flags from your index function to the output, they need
to get put in the value column of the index
data.frames.
There are a few rules to follow with flags:
If an index value is NA and you want to flag with a reason why it is NA, replace the NA value with that flag value.
If you wish to report a value, but still include a flag, report the value in the form: value_flag (0.042_DATA01, 43.1_DOC01, etc.)
Helper Functions
There are few helper functions within the eemanalyzeR package that will provide some common flags.
First is the get_ratios() function. This can be helpful
when calculating things like peak ratios, where a missing value will
result in an NA or a value of 0 in the denominator will
result in an infinite value.
#calculate the ratio of Peak A to Peak T
pA <- get_fluorescence(eemlist, ex=250:260, em=380:480)
pT <- get_fluorescence(eemlist, ex=270:280, em=320:350)
rAT <- get_ratios(pA, pT)
rAT
#> [1] "1.72750162152655" "0.423565751341601" "4.07129094367473"
#> [4] "0.450601408727671"
#if Peak T is all 0, will return DATA_03 flag, indicating index couldn't
#calculated due to zero in denominator
rAT <- get_ratios(pA, 0)
rAT
#> [1] "DATA03" "DATA03" "DATA03" "DATA03"
#calculate HIX
sum_high <- get_fluorescence(eemlist, ex=254, em=435:480, stat = "sum")
sum_low <- get_fluorescence(eemlist, ex=254, em=300:345, stat="sum")
HIX <- get_ratios(sum_high,sum_low)
HIX
#> [1] "DATA01" "DATA01" "DATA01" "DATA01"
#if sum_low is NA, will return DATA_01 flag indicating the index couldn't be
#calculated due to missing data
HIX <- get_ratios(sum_high, NA)
HIX
#> [1] "DATA01" "DATA01" "DATA01" "DATA01"There’s also a function that will flag indices that can’t be
calculated because data didn’t exist for the requested wavelengths:
flag_missing(). This is useful to check when calculating
indices to flag why certain indices might be returning NA
values.
#checking absorbance data
#data exists, so there are no flags, NA is returned
flag_missing(abslist, wl=400)
#> [1] NA NA NA NA
#data doesn't exist
flag_missing(abslist, wl=100)
#> [1] "DATA01" "DATA01" "DATA01" "DATA01"
#some data exists, still calculate
flag_missing(abslist, wl=100:254, all=FALSE)
#> [1] "DATA02" "DATA02" "DATA02" "DATA02"
#checking fluorescence data
#data exists, so there are no flags, NA is returned
flag_missing(eemlist, ex=270:280, em=300:320)
#> [1] NA NA NA NA
#data doesn't exist
flag_missing(eemlist, ex=100:150, em=300:320)
#> [1] "DATA01" "DATA01" "DATA01" "DATA01"
#some data exists, still calculate
flag_missing(eemlist, ex=100:350, em=300:320, all=FALSE)
#> [1] "DATA02" "DATA02" "DATA02" "DATA02"You may also want to flag values below the method detection limit
(MDL) this can be done with: check_eem_mdl() and
check_abs_mdl(). This is useful to ensure that the blanks
are actually blank and ratio values that are returned are based on
“real” data.
#checking absorbance data
#get mdl
abs_mdl <- readRDS(file.path(system.file("extdata", package = "eemanalyzeR"),
"default/default-abs-mdl.rds"))
#data is fully above the MDL so NA is returned
check_abs_mdl(abslist[[2]], abs_mdl, wl=254)
#> [1] NA
#data is fully below the MDL so MDL01 is returned
check_abs_mdl(abslist[[1]], abs_mdl, wl=254)
#> [1] "MDL01"
#checking fluorescence data
#get mdl
eem_mdl <- readRDS(file.path(system.file("extdata", package = "eemanalyzeR"),
"default/default-eem-mdl.rds"))
#data is completely above the MDL so NA is returned
check_eem_mdl(eemlist[[2]], mdl = eem_mdl, ex=270:280, em=300:320)
#> [1] NA
#data is fully below the MDL so MDL01 is returned
check_eem_mdl(eemlist[[1]], mdl = eem_mdl, ex=270:280, em=300:320)
#> [1] "MDL01"
#some of the data is below the MDL so MDL02 is returned
eemlist[[2]]$x[1,4] <- 0.000001 #set a low value so it spans the MDL in the region for an example
check_eem_mdl(eemlist[[2]], mdl = eem_mdl, ex = 314, em=250:262)
#> [1] "MDL02"If your function generates more than one flag (say a flag for missing
data and an MDL flag) you can use the .combine_flags()
function to nicely combine the flags.
#works with a single set of flags
.combine_flags("DATA01", NA)
#> [1] "DATA01"
.combine_flags(NA, "MDL01")
#> [1] "MDL01"
.combine_flags(NA, NA)
#> [1] NA
.combine_flags("DATAO1", "MDL01")
#> [1] "DATAO1_MDL01"
.combine_flags("DATA01", "DATA01")
#> [1] "DATA01"
#also works with a vector of flags
flag1 <- c("MDL01", "MDL02", NA)
flag2 <- c("DATA01", NA, NA)
.combine_flags(flag1, flag2)
#> [1] "MDL01_DATA01" "MDL02" NA
#if you set MDL to TRUE, it knows it's combining two MDL flags
#so if one is MDL01 and one is MDL02, it will return MDL02, some of the
#data was below the MDL
#this function is useful for indices using ratios
.combine_flags("MDL01", "MDL02", mdl=TRUE)
#> [1] "MDL03"Once we’ve gotten the values and the flags, the data needs to be
formatted in a specific way to input into the get_indices()
function. To do this we can use the format_index()
function.
ex <- 240:260
em <- 300:320
vals <- get_fluorescence(eemlist, ex, em, stat = "max")
flags <- flag_missing(eemlist, ex=ex, em=em, all=FALSE)
index_formatted <- format_index(eemlist, "test_index", vals, flags)
index_formatted
#> sample_id sample_name index value
#> 1 ExampleBlank Sample Blank 1 test_index 0.000532084537187436_DATA02
#> 2 ExampleTeaStd PreTea 1% SRM test_index 0.520903992822664_DATA02
#> 3 ExampleSample Example Sample test_index 0.0851942070835534_DATA02
#> 4 ManualExampleTea 1% SRM tea test_index 1.48625281995149_DATA02Example 1
Alright, now that we’ve reviewed all the helper functions and the requirements for the custom function let’s do an example. Let’s create a custom function to calculate some new fluorescence indices presented in Zhang et al. 2025:
First let’s turn the different indices into a list of excitation and emission wavelengths.
zhang_indices <- list(USoI = list(ex=c(245,230), em=c(440,260)),
ASoI = list(ex=c(245,260), em=c(325,430)),
TSoI = list(ex=c(260,285), em=c(430,365)))We can use the get_fluorescence() helper function to
calculate the fluorescence for the numerator and denominator of the
ratio. Then we can use the get_ratios() helper function to
get the ratio. For a single index this would look like:
eemlist <- example_processed_eems
get_ratios(get_fluorescence(eemlist, zhang_indices$TSoI$ex[1], zhang_indices$TSoI$em[1]),
get_fluorescence(eemlist, zhang_indices$TSoI$ex[2], zhang_indices$TSoI$em[2]))
#> [1] "4.93586355290846" "0.528716173072744" "3.87515734359415"
#> [4] "0.48620210278511"To get all the indices let’s use the lapply function.
results <- lapply(names(zhang_indices), function(index_name) {
index <- zhang_indices[[index_name]]
get_ratios(get_fluorescence(eemlist, index$ex[1], index$em[1]),
get_fluorescence(eemlist, index$ex[2], index$em[2]))
})While we’re calculating each index, let’s also use the
flag_missing() function to see if there’s any data missing.
Since we’re finding the maximum value, we’ll set all to
FALSE so it will report a value if there’s some data. We’ll
also check to see if any of the data is below the MDL, since we have two
discrete values we can use .combine_flags() to combine the
returned MDL flags. Lastly, we’ll use the format_index()
function to make sure everything is correctly formatted.
results <- lapply(names(zhang_indices), function(index_name){
#get values
index <- zhang_indices[[index_name]]
vals <- get_ratios(get_fluorescence(eemlist, index$ex[1], index$em[1]),
get_fluorescence(eemlist, index$ex[2], index$em[2]))
#get flags
missflags <- flag_missing(eemlist, ex=index$ex, em=index$em, all=TRUE)
mdlflags <- .combine_flags(check_eem_mdl(eemlist, eem_mdl, index$ex[1], index$em[1]),
check_eem_mdl(eemlist, eem_mdl, index$ex[2], index$em[2]), mdl=TRUE)
flags <- .combine_flags(missflags, mdlflags)
#add sample names and make into data.frame (get index name)
res <- format_index(eemlist, index_name, vals, flags)
#return res
return(res)
})
results <- do.call(rbind, results)Now we need to make sure to add in the MDL data so we can use it to
check our values. If the MDL is NULL, we want to have a warning, but
still run. We can use the get_qaqc() function
mdl <- get_qaqc(file.path(system.file("extdata", package = "eemanalyzeR")), type = "mdl")Lastly, we just need to return the results as a list. Since we don’t have any absorbance data we’ll just make it NA.
list(abs_index = NA,
eem_index=results)
#> $abs_index
#> [1] NA
#>
#> $eem_index
#> sample_id sample_name index value
#> 1 ExampleBlank Sample Blank 1 USoI DATA01
#> 2 ExampleTeaStd PreTea 1% SRM USoI DATA01
#> 3 ExampleSample Example Sample USoI DATA01
#> 4 ManualExampleTea 1% SRM tea USoI DATA01
#> 5 ExampleBlank Sample Blank 1 ASoI DATA01_MDL03
#> 6 ExampleTeaStd PreTea 1% SRM ASoI DATA01
#> 7 ExampleSample Example Sample ASoI DATA01
#> 8 ManualExampleTea 1% SRM tea ASoI DATA01
#> 9 ExampleBlank Sample Blank 1 TSoI MDL01
#> 10 ExampleTeaStd PreTea 1% SRM TSoI 0.528716173072744
#> 11 ExampleSample Example Sample TSoI 3.87515734359415
#> 12 ManualExampleTea 1% SRM tea TSoI 0.48620210278511Great, that all looks good, now we just need to combine all that code to a custom function.
zhang2025 <- function(eemlist, abslist, cuvle=1, qaqc_dir){
#making sure eemlist and abslist are correct objects
stopifnot(eemanalyzeR:::.is_eemlist(eemlist), eemanalyzeR:::.is_abslist(abslist),
is.numeric(cuvle), all(sapply(eemlist, attr, "is_doc_normalized"))==FALSE)
#get mdl data
mdl <- get_qaqc(qaqc_dir, type = "mdl")
eem_mdl <- mdl$eem_mdl
#specify indices
zhang_indices <- list(USoI = list(ex=c(245,230), em=c(440,260)),
ASoI = list(ex=c(245,260), em=c(325,430)),
TSoI = list(ex=c(260,285), em=c(430,365)))
#get results as a data.frame
results <- lapply(names(zhang_indices), function(index_name){
index <- zhang_indices[[index_name]]
vals <- get_ratios(get_fluorescence(eemlist, index$ex[1], index$em[1]),
get_fluorescence(eemlist, index$ex[2], index$em[2]))
missflags <- flag_missing(eemlist, ex=index$ex, em=index$em, all=TRUE)
mdlflags <- .combine_flags(check_eem_mdl(eemlist, eem_mdl, index$ex[1], index$em[1]),
check_eem_mdl(eemlist, eem_mdl, index$ex[2], index$em[2]), mdl=TRUE)
flags <- .combine_flags(missflags, mdlflags)
res <- format_index(eemlist, index_name, vals, flags)
return(res)
})
#combine lists together
results <- do.call(rbind, results)
return(list(abs_index = NA,
eem_index=results))
}Now let’s test our custom function with the
get_indices() function.
indices <- get_indices(example_processed_eems,
example_processed_abs,
index_method = zhang2025,
return = "wide",
qaqc_dir = file.path(system.file("extdata", package = "eemanalyzeR")))
indices$eem_index
#> # A tibble: 4 × 5
#> sample_id sample_name USoI ASoI TSoI
#> <chr> <chr> <chr> <chr> <chr>
#> 1 ExampleBlank Sample Blank 1 DATA01 DATA01_MDL03 MDL01
#> 2 ExampleTeaStd PreTea 1% SRM DATA01 DATA01 0.5287
#> 3 ExampleSample Example Sample DATA01 DATA01 3.875
#> 4 ManualExampleTea 1% SRM tea DATA01 DATA01 0.4862Example 2
The last example only used fluorescence data. Let’s create another custom function focused on absorbance indices. We’ll create a function to calculate the indices used in Erlandsson et al. 2012:
| Index | Description |
|---|---|
| a254 | absorbance at 254 nm |
| a420 | absorbance at 420 nm |
| a220_a254 | ratio of absorbance at 220 to 254 nm |
| a250_a364 | ratio of absorbance at 250 to 364 nm |
| a300_a400 | ratio of absorbance at 300 to 400 nm |
| S275_295 | spectral slope between 275 to 295 nm |
| SR | spectral ratio |
First let’s turn the indices into a list of wavelengths to help with flagging and calculating. Note that for the ratios we only need two specific wavelengths, so we’re making that a vector of just those two wavelengths, while the spectral slope needs all the wavelengths between those two values, so we’re making that a vector of all the wavelengths between those two wavelengths.
erlandsson_index <- list(a254=254,
a420=420,
a220_a254=c(220,250),
a254_a364=c(254,364),
a300_a400=c(300,400),
S275_295=c(275:295),
SR=c(275:295, 350:400))We can use the get_absorbance() helper function to
calculate the absorbance at 254 and 420. For a single wavelength that
looks like:
abslist <- example_processed_abs
a254 <- get_absorbance(abslist, wl=erlandsson_index[[1]])
a254
#> [1] "0.00054853647738303" "0.163215306070913" "0.0806502356009794"
#> [4] "0.300921250392953"Let’s use lapply to get the absorbance, add any flags, and format it
correctly. We’ll use base::do.call() to bind all the lists
of indices together.
abs_data <- lapply(names(erlandsson_index[1:2]), function(index_name){
index <- erlandsson_index[[index_name]]
#get values
vals <- get_absorbance(abslist, wl=index)
#get flags
missflags <- flag_missing(abslist, wl=index, all=TRUE)
mdlflags <- check_abs_mdl(abslist, abs_mdl, wl=index)
flags <- .combine_flags(missflags, mdlflags)
#add sample names and make into data.frame (get index name)
res <- format_index(abslist, index_name, vals, flags)
#return res
return(res)
})
abs_data <- do.call(rbind, abs_data)Next lets get the ratios, we can still use the
get_absorbance() helper function to get the absorbance at
the wavelengths, and then use the get_ratios() helper
function to get the ratio with any flags. In the case below, we don’t
have absorbance at 220 nm, so we get flags of DATA01
a220 <- get_absorbance(abslist, wl=220)
a254 <- get_absorbance(abslist, wl=254)
a220_a254 <- get_ratios(a220, a254)
a220_a254
#> [1] "DATA01" "DATA01" "DATA01" "DATA01"Since we’re getting multiple different sets of ratios, let’s use
base::lapply() to get all of the ratios at once.
ratios <- lapply(names(erlandsson_index[3:5]), function(index_name){
index <- erlandsson_index[[index_name]]
#get values
a1 <- get_absorbance(abslist, wl=index[1])
a2 <- get_absorbance(abslist, wl=index[2])
get_ratios(a1, a2)
})While we’re calculating each index, let’s also use the
flag_missing() function to see if there’s any data missing.
Lastly, we’ll use the format_index() function to make sure
everything is correctly formatted. We’ll use
base::do.call() to bind all the lists of indices
together.
ratios <- lapply(names(erlandsson_index[3:5]), function(index_name){
index <- erlandsson_index[[index_name]]
#get values
a1 <- get_absorbance(abslist, wl=index[1])
a2 <- get_absorbance(abslist, wl=index[2])
vals <- get_ratios(a1, a2)
#get flags
missflags <- flag_missing(abslist, wl=index, all=TRUE)
mdlflags <- check_abs_mdl(abslist, abs_mdl, wl=index)
flags <- .combine_flags(missflags, mdlflags)
#add sample names and make into data.frame (get index name)
res <- format_index(abslist, index_name, vals, flags)
#return res
return(res)
})
ratios <- do.call(rbind, ratios)Lastly we need to get the spectral slopes and spectral ratio. For
these we’ll use the get_abs_slope() function.
index <- "S275_295"
vals <- get_abs_slope(abslist, c(275,295))
missflags <- flag_missing(abslist, wl=erlandsson_index[[index]])
mdlflags <- check_abs_mdl(abslist, mdl=abs_mdl, wl=erlandsson_index[[index]])
flags <- .combine_flags(missflags, mdlflags)
S275_295 <- format_index(abslist, index, vals, flags)
index <- "SR"
vals <- get_ratios(get_abs_slope(abslist, c(275,295)), get_abs_slope(abslist, c(350,400)))
missflags <- flag_missing(abslist, wl=erlandsson_index[[index]])
mdlflags <- .combine_flags(check_abs_mdl(abslist, mdl=abs_mdl, wl=275:295),
check_abs_mdl(abslist, mdl=abs_mdl, wl=350:400))
flags <- .combine_flags(missflags, mdlflags)
SR <- format_index(abslist, index, vals, flags)Now we need to make sure to add in the MDL data so we can use it to check our values. If the MDL is NULL, we want to have a warning, but still run.
mdl <- get_qaqc(file.path(system.file("extdata", package = "eemanalyzeR")), type = "mdl")Lastly, we just need to combine all the indices into a data.frame and
return the results as a list using another base::do.call().
Since we don’t have any EEMs data we’ll just make it NA.
results <- do.call(rbind, list(abs_data, ratios, S275_295, SR))
list(abs_index = results,
eem_index=NA)
#> $abs_index
#> sample_id sample_name index value
#> 1 ExampleBlank Sample Blank 1 a254 MDL01
#> 2 ExampleTeaStd PreTea 1% SRM a254 0.163215306070913
#> 3 ExampleSample Example Sample a254 0.0806502356009794
#> 4 ManualExampleTea 1% SRM tea a254 0.300921250392953
#> 5 ExampleBlank Sample Blank 1 a420 MDL01
#> 6 ExampleTeaStd PreTea 1% SRM a420 0.0097782541629343
#> 7 ExampleSample Example Sample a420 0.005703926210965
#> 8 ManualExampleTea 1% SRM tea a420 0.0174349110598922
#> 9 ExampleBlank Sample Blank 1 a220_a254 DATA01_MDL01
#> 10 ExampleTeaStd PreTea 1% SRM a220_a254 DATA01
#> 11 ExampleSample Example Sample a220_a254 DATA01
#> 12 ManualExampleTea 1% SRM tea a220_a254 DATA01
#> 13 ExampleBlank Sample Blank 1 a254_a364 MDL01
#> 14 ExampleTeaStd PreTea 1% SRM a254_a364 4.30029009047272
#> 15 ExampleSample Example Sample a254_a364 5.03597089422996
#> 16 ManualExampleTea 1% SRM tea a254_a364 4.47739151485424
#> 17 ExampleBlank Sample Blank 1 a300_a400 MDL01
#> 18 ExampleTeaStd PreTea 1% SRM a300_a400 6.02242754070733
#> 19 ExampleSample Example Sample a300_a400 5.46607410055233
#> 20 ManualExampleTea 1% SRM tea a300_a400 6.50764672772594
#> 21 ExampleBlank Sample Blank 1 S275_295 MDL01
#> 22 ExampleTeaStd PreTea 1% SRM S275_295 0.0230618505221762
#> 23 ExampleSample Example Sample S275_295 0.0130236674635653
#> 24 ManualExampleTea 1% SRM tea S275_295 0.0230606966213098
#> 25 ExampleBlank Sample Blank 1 SR MDL01
#> 26 ExampleTeaStd PreTea 1% SRM SR 1.03003744636482
#> 27 ExampleSample Example Sample SR 0.740240611049655
#> 28 ManualExampleTea 1% SRM tea SR 1.00357361747553
#>
#> $eem_index
#> [1] NAGreat, that all looks good, now we just need to combine all that code to a custom function.
erlandsson2012 <- function(eemlist, abslist, cuvle=1, qaqc_dir){
#making sure eemlist and abslist are correct objects
stopifnot(eemanalyzeR:::.is_eemlist(eemlist), eemanalyzeR:::.is_abslist(abslist),
is.numeric(cuvle), all(sapply(eemlist, attr, "is_doc_normalized"))==FALSE)
#get mdl data
mdl <- get_qaqc(qaqc_dir, type = "mdl")
abs_mdl <- mdl$abs_mdl
#specify wavelengths
erlandsson_index <- list(a254=254,
a420=420,
a220_a254=c(220,250),
a254_a364=c(254,364),
a300_a400=c(300,400),
S275_295=c(275:295),
SR=c(275:295, 350:400))
#get results as a data.frame
abs_data <- lapply(names(erlandsson_index[1:2]), function(index_name){
index <- erlandsson_index[[index_name]]
#get values
vals <- get_absorbance(abslist, wl=index)
#get flags
missflags <- flag_missing(abslist, wl=index, all=TRUE)
mdlflags <- check_abs_mdl(abslist, abs_mdl, wl=index)
flags <- .combine_flags(missflags, mdlflags)
#add sample names and make into data.frame (get index name)
res <- format_index(abslist, index_name, vals, flags)
#return res
return(res)
})
abs_data <- do.call(rbind, abs_data)
ratios <- lapply(names(erlandsson_index[3:5]), function(index_name){
index <- erlandsson_index[[index_name]]
#get values
a1 <- get_absorbance(abslist, wl=index[1])
a2 <- get_absorbance(abslist, wl=index[2])
vals <- get_ratios(a1, a2)
#get flags
missflags <- flag_missing(abslist, wl=index, all=TRUE)
mdlflags <- check_abs_mdl(abslist, abs_mdl, wl=index)
flags <- .combine_flags(missflags, mdlflags)
#add sample names and make into data.frame (get index name)
res <- format_index(abslist, index_name, vals, flags)
#return res
return(res)
})
ratios <- do.call(rbind, ratios)
index <- "S275_295"
vals <- get_abs_slope(abslist, c(275,295))
missflags <- flag_missing(abslist, wl=erlandsson_index[[index]])
mdlflags <- check_abs_mdl(abslist, mdl=abs_mdl, wl=erlandsson_index[[index]])
flags <- .combine_flags(missflags, mdlflags)
S275_295 <- format_index(abslist, index, vals, flags)
index <- "SR"
vals <- get_ratios(get_abs_slope(abslist, c(275,295)),
get_abs_slope(abslist, c(350,400)))
missflags <- flag_missing(abslist, wl=erlandsson_index[[index]])
mdlflags <- .combine_flags(check_abs_mdl(abslist, mdl=abs_mdl, wl=275:295),
check_abs_mdl(abslist, mdl=abs_mdl, wl=350:400))
flags <- .combine_flags(missflags, mdlflags)
SR <- format_index(abslist, index, vals, flags)
#combine lists together
results <- do.call(rbind, list(abs_data, ratios, S275_295, SR))
return(list(abs_index = results,
eem_index=NA))
}Now let’s test our custom function with the
get_indices() function.
indices <- get_indices(example_processed_eems, example_processed_abs, index_method = erlandsson2012,
qaqc_dir = file.path(system.file("extdata", package = "eemanalyzeR")), return="wide")
indices$abs_index
#> # A tibble: 4 × 9
#> sample_id sample_name a254 a420 a220_a254 a254_a364 a300_a400 S275_295 SR
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ExampleB… Sample Bla… MDL01 MDL01 DATA01_M… MDL01 MDL01 MDL01 MDL01
#> 2 ExampleT… PreTea 1% … 0.16… 0.00… DATA01 4.3 6.022 0.02306 1.03
#> 3 ExampleS… Example Sa… 0.08… 0.00… DATA01 5.036 5.466 0.01302 0.74…
#> 4 ManualEx… 1% SRM tea 0.30… 0.01… DATA01 4.477 6.508 0.02306 1.004References
Zhang, H., Hou, J., Nie, L., Hao, Y., Gao, H., & Yu, H. (2025). Developing new spectral indices for identifying DOM sources in Liaohe River in a large-scale river basin by fluorescence spectroscopy and random forest model. Process Safety and Environmental Protection, 201, 107553. https://doi.org/10.1016/j.psep.2025.107553
Erlandsson, M., N. Futter, M., N. Kothawala, D., & J. Köhler, S. (2012). Variability in spectral absorbance metrics across boreal lake waters. Journal of Environmental Monitoring, 14(10), 2643-2652. https://doi.org/10.1039/C2EM30266G