README.md
In mlfelice/plfaR: Interface for Analyzing IonVantage 13C-PLFA Data
plfaR Readme
This is a package for running QC on and processing named PLFA data. The
three major components are calculating lipid concentrations, biomarker
concentrations and mol %, and d13C isotope data, with output in a format
that can easily be further analyzed using tidyverse and statistical
packages in R.
Getting started
Installation
If the devtools package isn’t already installed, download and install
from CRAN.
install.packages("devtools")
Install plfaR package.
devtools::install_github(repo = 'mlfelice/plfaR')
The package is now ready for processing PLFA data.
Basic Workflow
Summary
This vignette shows an example workflow for basic importing and
processing of PLFA data from raw named peak lists to lipid and biomarker
concentrations and mol %.
Processing
Import packages
library(plfaR) # PLFA processing
library(tidyverse) # Basic data wrangling
library(readxl) # Import Excel files
Import sample metadata. At minimum, you should have the following
columns: * Batch * DataFileName * SampleType * SampleWt You can also
include any additional data you will want for analysis.
# Import metadata Excel file
md <- read_xlsx('plfaR_example-metadata.xlsx')
# Preview metadata
head(md)
## # A tibble: 6 x 4
## Batch DataFileName SampleType SampleWt
## <chr> <chr> <chr> <dbl>
## 1 B14 13 Standard 1.raw Standard NA
## 2 B14 Blank 1 for plot 16 17 and 7.raw Blank NA
## 3 B14 Blank 1 for plot 19 20 21.raw Blank NA
## 4 B14 Blank 2 for plot 16 17 and 7.raw Blank NA
## 5 B14 Blank 2 for plot 19 20 21.raw Blank NA
## 6 B14 Control 1 for plot 16 17 and 7.raw Control NA
Import your PLFA data. This should be your named peak list, and should
be an Excel file with the following formatting: * Either single tab OR
peak list data on a worksheet named ‘named_peaks’ * File should have
the following columns with headers: + Batch + DataFileName + RetTimeSecs
+ MajorHeightnA + TotalPeakArea1 + DisplayDelta1 + Name
peak_list <- import_batch('plfaR_example-peak-list.xlsx')
# Preview imported data
head(peak_list)
## # A tibble: 6 x 8
## BatchDataFileNa~ Batch DataFileName RetTimeSecs MajorHeightnA TotalPeakArea1
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 B14_Plot 16 100~ B14 Plot 16 100~ 526. 1.88 443498.
## 2 B14_Plot 17 0-2~ B14 Plot 17 0-2~ 526. 1.96 458006.
## 3 B14_Plot 17 20-~ B14 Plot 17 20-~ 526. 1.32 311325.
## 4 B14_Plot 17 50-~ B14 Plot 17 50-~ 526. 2.60 695964.
## 5 B14_Plot 17 100~ B14 Plot 17 100~ 526. 1.82 490706.
## 6 B14_Plot 17 150~ B14 Plot 17 150~ 526. 2.57 718811.
## # ... with 2 more variables: DisplayDelta1 <dbl>, Name <chr>
Run QC tests on the data. If there are any issues with the data, an
error message will be displayed indicating the issue. This also
generates a list containing summary dataframes showing 1) samples with
duplicate lipids (and which lipids), 2) samples missing standard peaks
(13:0, 16:0*, or 19:0), and 3) the frequency (fraction) with which each
lipid is present in a sample.
*NOTE: Although 16:0 isn’t technically a standard, it is present in
Sphagnum, so should be present in any peat samples. This can likely be
ignored if working with mineral soils.
qc_df <- quality_check(peak_list)
## Batch: B14
## ------
## Warning: Standards missing from at least one sample.
## Check data file and/or chromatograms before proceeding
# QC summary dataframes can be accessed individually
qc_df[[1]][['duplicate_lipids']]
qc_df[[1]][['missing_lipids']]
qc_df[[1]][['lipid_frequency']]
To process the data, we first calculate lipid concentrations in nmol/g
soil * In addition to supplying the peak list, we need to supply a
vector containing the sample names for the blanks (should only have 13:0
and 19:0) and standards (ie. the samples on which you want to base kval
calculations).
NOTE: process_peak_area() also has several other parameters that can
be changed, but which are constant in our lab.
# Create vector of filenames of standards (can also manually supply)
stds <- c('13 Standard 1.raw')
# Blanks represent peak area subtracted for 13:0 and 19:0, so input a named
# list with a vector for each lipid to subtract
blanks <- list( '13:0' = c('13 Standard 1.raw'),
'19:0' = c('Control 1 for plot 16 17 and 7.raw',
'Control 1 for plots 19 20 21.raw',
'Control 2 for plot 16 17 and 7.raw',
'Control 2 for plots 19 20 21.raw)'))
# Convert the peak areas to concentration (nmol/g dry soil)
conc_df <- process_peak_area(peak_list,
blanks = blanks,
standard_fnames = stds,
soil_wt_df = md)
head(conc_df)
## Name Batch DataFileName BatchDataFileName
## 1 10:0 2OH B14 Plot 17 150-200cm 8g.raw B14_Plot 17 150-200cm 8g.raw
## 2 10:0 2OH B14 Plot 17 50-100cm 8g.raw B14_Plot 17 50-100cm 8g.raw
## 3 10:0 2OH B14 Plot 17 100-150cm 8g.raw B14_Plot 17 100-150cm 8g.raw
## 4 10:0 2OH B14 Plot 21 50-100cm 8g.raw B14_Plot 21 50-100cm 8g.raw
## 5 10:0 2OH B14 Plot 20 20-50 cm 6g.raw B14_Plot 20 20-50 cm 6g.raw
## 6 10:0 2OH B14 Plot 19 50-100cm 8g.raw B14_Plot 19 50-100cm 8g.raw
## RetTimeSecs MajorHeightnA TotalPeakArea1 DisplayDelta1 BlnkArea
## 1 714.1 1.9397842 746308.6 -17.226493 0
## 2 714.2 1.6354058 821001.4 -8.610726 0
## 3 714.1 1.6381963 646790.2 -34.335439 0
## 4 714.4 2.2091562 1003103.8 -26.853440 0
## 5 714.9 0.5327858 194985.3 -30.641190 0
## 6 714.6 1.9758513 971162.6 -26.955573 0
## AreaMinusBlanks SampleType SampleWt molecular_weight_g_per_mol indicates
## 1 746308.6 Unknown 8 202 <NA>
## 2 821001.4 Unknown 8 202 <NA>
## 3 646790.2 Unknown 8 202 <NA>
## 4 1003103.8 Unknown 8 202 <NA>
## 5 194985.3 Unknown 6 202 <NA>
## 6 971162.6 Unknown 8 202 <NA>
## C_PLFA nmol_g
## 1 10 0.9448399
## 2 10 1.0394022
## 3 10 0.8188478
## 4 10 1.2699471
## 5 10 0.3291398
## 6 10 1.2295089
Next, we can calculate the concentrations of groups of indicator lipids
as well as their mol %.
indicators_df <- calculate_indicators(conc_df, soil_wt_df = md)
head(indicators_df)
## DataFileName Indicator Percent Batch SampleType SampleWt nmol_g
## 1 13 Standard 1.raw actino_lipids NaN B14 Standard NA 0
## 2 13 Standard 1.raw anaerobe_lipids NaN B14 Standard NA 0
## 3 13 Standard 1.raw b_lipids NaN B14 Standard NA 0
## 4 13 Standard 1.raw f_lipids NaN B14 Standard NA 0
## 5 13 Standard 1.raw f_to_b NaN B14 Standard NA NA
## 6 13 Standard 1.raw gram_neg_lipids NaN B14 Standard NA 0
Next, we can calculate the corrected isotope values for individual
lipds. This will calculate 2 corrections: 1) normalize measured isotope
values to the international reference scale, and 2) correct values for
the d13C of methanol added during methylation. The lipid d13C values can
be used to calculate the d13C of indicator groups.
Indicator group d13C are calculated from the unweighted average of
individual lipid d13C.
# Process lipid d13C to normalize to internationl refernce scale and account
# for the contribution of methanol to d13C
d13c_lipid_df <- correct_iso_base(df = peak_list,
d13c_correction = 0,
methanol_13c = -55.84,
min_height = 1)
## 144 of 364 peaks removed from batch B14
# Calculate d13C of indicator groups
d13c_indicator_df <- indic_iso_base(d13c_lipid_df, md)
head(d13c_lipid_df)
## Name BatchDataFileName Batch DataFileName
## 1 10:0 2OH B14_Plot 17 150-200cm 8g.raw B14 Plot 17 150-200cm 8g.raw
## 2 10:0 2OH B14_Plot 17 100-150cm 8g.raw B14 Plot 17 100-150cm 8g.raw
## 5 10:0 2OH B14_Plot 21 50-100cm 8g.raw B14 Plot 21 50-100cm 8g.raw
## 6 10:0 2OH B14_Plot 17 50-100cm 8g.raw B14 Plot 17 50-100cm 8g.raw
## 7 10:0 2OH B14_Plot 16 150-200cm 8g.raw B14 Plot 16 150-200cm 8g.raw
## 8 10:0 2OH B14_Plot 20 150-200cm 7g.raw B14 Plot 20 150-200cm 7g.raw
## RetTimeSecs MajorHeightnA TotalPeakArea1 DisplayDelta1
## 1 714.1 1.939784 746308.6 -17.226493
## 2 714.1 1.638196 646790.2 -34.335439
## 5 714.4 2.209156 1003103.8 -26.853440
## 6 714.2 1.635406 821001.4 -8.610726
## 7 714.4 1.076172 460225.0 -17.875433
## 8 714.4 1.575394 618243.9 -24.667249
## molecular_weight_g_per_mol indicates C_PLFA d13C_corrected
## 1 202 <NA> 10 -13.365143
## 2 202 <NA> 10 -32.184982
## 5 202 <NA> 10 -23.954784
## 6 202 <NA> 10 -3.887799
## 7 202 <NA> 10 -14.078976
## 8 202 <NA> 10 -21.549974
head(d13c_indicator_df)
## DataFileName BatchDataFileName Indicator avg_d13C_corrected
## 1 13 Standard 1.raw B14_13 Standard 1.raw gram_pos_lipids NA
## 2 13 Standard 1.raw B14_13 Standard 1.raw protozoa NA
## 3 13 Standard 1.raw B14_13 Standard 1.raw total_biomass -28.8483
## 4 13 Standard 1.raw B14_13 Standard 1.raw actino_lipids NA
## 5 13 Standard 1.raw B14_13 Standard 1.raw b_lipids NA
## 6 13 Standard 1.raw B14_13 Standard 1.raw anaerobe_lipids NA
## Batch SampleType SampleWt
## 1 B14 Standard NA
## 2 B14 Standard NA
## 3 B14 Standard NA
## 4 B14 Standard NA
## 5 B14 Standard NA
## 6 B14 Standard NA
These dataframes can now be analyzed with your preferred stats pipeline.
Other information
The following are the groups of lipids used for each biomarker group.
## $f_lipids
## [1] "18:1 w9c" "18:2 w6,9c"
##
## $b_lipids
## [1] "15:0 iso" "15:0 anteiso" "16:1 w7c" "16:0 10me" "18:1 w9c"
## [6] "18:1 w9t" "18:2 w6,9c" "18:0 10me" "19:0 cyclo"
##
## $gram_pos_lipids
## [1] "15:0 iso" "15:0 anteiso"
##
## $gram_neg_lipids
## [1] "16:1 w7c" "18:1 w9t"
##
## $actino_lipids
## [1] "16:0 10me" "18:0 10me"
##
## $anaerobe_lipids
## [1] "19:0 cyclo"
##
## $protozoa
## [1] "20:4 w6,9,12,15"
##
## $total_biomass
## [1] "8:0" "10:0" "10:0 2OH" "11:0" "12:0"
## [6] "12:0 2OH" "12:0 3OH" "13:0" "14:0" "14:0 2OH"
## [11] "14:0 3OH" "14:1" "15:0" "15:0 anteiso" "15:0 iso"
## [16] "16:0 10me" "16:0 2OH" "16:0 iso" "16:1 w5c" "16:1 w7c"
## [21] "16:1 w9c" "17:0" "17:0 iso" "17:0 anteiso" "17:0 cyclo"
## [26] "17:1" "17:1 iso" "18:0" "18:0 10me" "18:1 w9c"
## [31] "18:1 w9t" "18:2 w6,9c" "19:0" "19:0 cyclo" "19:1"
## [36] "20:0"
mlfelice/plfaR documentation built on June 9, 2022, 4:28 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
plfaR Readme
This is a package for running QC on and processing named PLFA data. The three major components are calculating lipid concentrations, biomarker concentrations and mol %, and d13C isotope data, with output in a format that can easily be further analyzed using tidyverse and statistical packages in R.
Getting started
Installation
If the devtools package isn’t already installed, download and install from CRAN.
install.packages("devtools")
Install plfaR package.
devtools::install_github(repo = 'mlfelice/plfaR')
The package is now ready for processing PLFA data.
Basic Workflow
Summary
This vignette shows an example workflow for basic importing and processing of PLFA data from raw named peak lists to lipid and biomarker concentrations and mol %.
Processing
Import packages
library(plfaR) # PLFA processing
library(tidyverse) # Basic data wrangling
library(readxl) # Import Excel files
Import sample metadata. At minimum, you should have the following columns: * Batch * DataFileName * SampleType * SampleWt You can also include any additional data you will want for analysis.
# Import metadata Excel file
md <- read_xlsx('plfaR_example-metadata.xlsx')
# Preview metadata
head(md)
## # A tibble: 6 x 4
## Batch DataFileName SampleType SampleWt
## <chr> <chr> <chr> <dbl>
## 1 B14 13 Standard 1.raw Standard NA
## 2 B14 Blank 1 for plot 16 17 and 7.raw Blank NA
## 3 B14 Blank 1 for plot 19 20 21.raw Blank NA
## 4 B14 Blank 2 for plot 16 17 and 7.raw Blank NA
## 5 B14 Blank 2 for plot 19 20 21.raw Blank NA
## 6 B14 Control 1 for plot 16 17 and 7.raw Control NA
Import your PLFA data. This should be your named peak list, and should be an Excel file with the following formatting: * Either single tab OR peak list data on a worksheet named ‘named_peaks’ * File should have the following columns with headers: + Batch + DataFileName + RetTimeSecs + MajorHeightnA + TotalPeakArea1 + DisplayDelta1 + Name
peak_list <- import_batch('plfaR_example-peak-list.xlsx')
# Preview imported data
head(peak_list)
## # A tibble: 6 x 8
## BatchDataFileNa~ Batch DataFileName RetTimeSecs MajorHeightnA TotalPeakArea1
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 B14_Plot 16 100~ B14 Plot 16 100~ 526. 1.88 443498.
## 2 B14_Plot 17 0-2~ B14 Plot 17 0-2~ 526. 1.96 458006.
## 3 B14_Plot 17 20-~ B14 Plot 17 20-~ 526. 1.32 311325.
## 4 B14_Plot 17 50-~ B14 Plot 17 50-~ 526. 2.60 695964.
## 5 B14_Plot 17 100~ B14 Plot 17 100~ 526. 1.82 490706.
## 6 B14_Plot 17 150~ B14 Plot 17 150~ 526. 2.57 718811.
## # ... with 2 more variables: DisplayDelta1 <dbl>, Name <chr>
Run QC tests on the data. If there are any issues with the data, an error message will be displayed indicating the issue. This also generates a list containing summary dataframes showing 1) samples with duplicate lipids (and which lipids), 2) samples missing standard peaks (13:0, 16:0*, or 19:0), and 3) the frequency (fraction) with which each lipid is present in a sample.
*NOTE: Although 16:0 isn’t technically a standard, it is present in Sphagnum, so should be present in any peat samples. This can likely be ignored if working with mineral soils.
qc_df <- quality_check(peak_list)
## Batch: B14
## ------
## Warning: Standards missing from at least one sample.
## Check data file and/or chromatograms before proceeding
# QC summary dataframes can be accessed individually
qc_df[[1]][['duplicate_lipids']]
qc_df[[1]][['missing_lipids']]
qc_df[[1]][['lipid_frequency']]
To process the data, we first calculate lipid concentrations in nmol/g soil * In addition to supplying the peak list, we need to supply a vector containing the sample names for the blanks (should only have 13:0 and 19:0) and standards (ie. the samples on which you want to base kval calculations).
NOTE: process_peak_area() also has several other parameters that can be changed, but which are constant in our lab.
# Create vector of filenames of standards (can also manually supply)
stds <- c('13 Standard 1.raw')
# Blanks represent peak area subtracted for 13:0 and 19:0, so input a named
# list with a vector for each lipid to subtract
blanks <- list( '13:0' = c('13 Standard 1.raw'),
'19:0' = c('Control 1 for plot 16 17 and 7.raw',
'Control 1 for plots 19 20 21.raw',
'Control 2 for plot 16 17 and 7.raw',
'Control 2 for plots 19 20 21.raw)'))
# Convert the peak areas to concentration (nmol/g dry soil)
conc_df <- process_peak_area(peak_list,
blanks = blanks,
standard_fnames = stds,
soil_wt_df = md)
head(conc_df)
## Name Batch DataFileName BatchDataFileName
## 1 10:0 2OH B14 Plot 17 150-200cm 8g.raw B14_Plot 17 150-200cm 8g.raw
## 2 10:0 2OH B14 Plot 17 50-100cm 8g.raw B14_Plot 17 50-100cm 8g.raw
## 3 10:0 2OH B14 Plot 17 100-150cm 8g.raw B14_Plot 17 100-150cm 8g.raw
## 4 10:0 2OH B14 Plot 21 50-100cm 8g.raw B14_Plot 21 50-100cm 8g.raw
## 5 10:0 2OH B14 Plot 20 20-50 cm 6g.raw B14_Plot 20 20-50 cm 6g.raw
## 6 10:0 2OH B14 Plot 19 50-100cm 8g.raw B14_Plot 19 50-100cm 8g.raw
## RetTimeSecs MajorHeightnA TotalPeakArea1 DisplayDelta1 BlnkArea
## 1 714.1 1.9397842 746308.6 -17.226493 0
## 2 714.2 1.6354058 821001.4 -8.610726 0
## 3 714.1 1.6381963 646790.2 -34.335439 0
## 4 714.4 2.2091562 1003103.8 -26.853440 0
## 5 714.9 0.5327858 194985.3 -30.641190 0
## 6 714.6 1.9758513 971162.6 -26.955573 0
## AreaMinusBlanks SampleType SampleWt molecular_weight_g_per_mol indicates
## 1 746308.6 Unknown 8 202 <NA>
## 2 821001.4 Unknown 8 202 <NA>
## 3 646790.2 Unknown 8 202 <NA>
## 4 1003103.8 Unknown 8 202 <NA>
## 5 194985.3 Unknown 6 202 <NA>
## 6 971162.6 Unknown 8 202 <NA>
## C_PLFA nmol_g
## 1 10 0.9448399
## 2 10 1.0394022
## 3 10 0.8188478
## 4 10 1.2699471
## 5 10 0.3291398
## 6 10 1.2295089
Next, we can calculate the concentrations of groups of indicator lipids as well as their mol %.
indicators_df <- calculate_indicators(conc_df, soil_wt_df = md)
head(indicators_df)
## DataFileName Indicator Percent Batch SampleType SampleWt nmol_g
## 1 13 Standard 1.raw actino_lipids NaN B14 Standard NA 0
## 2 13 Standard 1.raw anaerobe_lipids NaN B14 Standard NA 0
## 3 13 Standard 1.raw b_lipids NaN B14 Standard NA 0
## 4 13 Standard 1.raw f_lipids NaN B14 Standard NA 0
## 5 13 Standard 1.raw f_to_b NaN B14 Standard NA NA
## 6 13 Standard 1.raw gram_neg_lipids NaN B14 Standard NA 0
Next, we can calculate the corrected isotope values for individual lipds. This will calculate 2 corrections: 1) normalize measured isotope values to the international reference scale, and 2) correct values for the d13C of methanol added during methylation. The lipid d13C values can be used to calculate the d13C of indicator groups.
Indicator group d13C are calculated from the unweighted average of individual lipid d13C.
# Process lipid d13C to normalize to internationl refernce scale and account
# for the contribution of methanol to d13C
d13c_lipid_df <- correct_iso_base(df = peak_list,
d13c_correction = 0,
methanol_13c = -55.84,
min_height = 1)
## 144 of 364 peaks removed from batch B14
# Calculate d13C of indicator groups
d13c_indicator_df <- indic_iso_base(d13c_lipid_df, md)
head(d13c_lipid_df)
## Name BatchDataFileName Batch DataFileName
## 1 10:0 2OH B14_Plot 17 150-200cm 8g.raw B14 Plot 17 150-200cm 8g.raw
## 2 10:0 2OH B14_Plot 17 100-150cm 8g.raw B14 Plot 17 100-150cm 8g.raw
## 5 10:0 2OH B14_Plot 21 50-100cm 8g.raw B14 Plot 21 50-100cm 8g.raw
## 6 10:0 2OH B14_Plot 17 50-100cm 8g.raw B14 Plot 17 50-100cm 8g.raw
## 7 10:0 2OH B14_Plot 16 150-200cm 8g.raw B14 Plot 16 150-200cm 8g.raw
## 8 10:0 2OH B14_Plot 20 150-200cm 7g.raw B14 Plot 20 150-200cm 7g.raw
## RetTimeSecs MajorHeightnA TotalPeakArea1 DisplayDelta1
## 1 714.1 1.939784 746308.6 -17.226493
## 2 714.1 1.638196 646790.2 -34.335439
## 5 714.4 2.209156 1003103.8 -26.853440
## 6 714.2 1.635406 821001.4 -8.610726
## 7 714.4 1.076172 460225.0 -17.875433
## 8 714.4 1.575394 618243.9 -24.667249
## molecular_weight_g_per_mol indicates C_PLFA d13C_corrected
## 1 202 <NA> 10 -13.365143
## 2 202 <NA> 10 -32.184982
## 5 202 <NA> 10 -23.954784
## 6 202 <NA> 10 -3.887799
## 7 202 <NA> 10 -14.078976
## 8 202 <NA> 10 -21.549974
head(d13c_indicator_df)
## DataFileName BatchDataFileName Indicator avg_d13C_corrected
## 1 13 Standard 1.raw B14_13 Standard 1.raw gram_pos_lipids NA
## 2 13 Standard 1.raw B14_13 Standard 1.raw protozoa NA
## 3 13 Standard 1.raw B14_13 Standard 1.raw total_biomass -28.8483
## 4 13 Standard 1.raw B14_13 Standard 1.raw actino_lipids NA
## 5 13 Standard 1.raw B14_13 Standard 1.raw b_lipids NA
## 6 13 Standard 1.raw B14_13 Standard 1.raw anaerobe_lipids NA
## Batch SampleType SampleWt
## 1 B14 Standard NA
## 2 B14 Standard NA
## 3 B14 Standard NA
## 4 B14 Standard NA
## 5 B14 Standard NA
## 6 B14 Standard NA
These dataframes can now be analyzed with your preferred stats pipeline.
Other information
The following are the groups of lipids used for each biomarker group.
## $f_lipids
## [1] "18:1 w9c" "18:2 w6,9c"
##
## $b_lipids
## [1] "15:0 iso" "15:0 anteiso" "16:1 w7c" "16:0 10me" "18:1 w9c"
## [6] "18:1 w9t" "18:2 w6,9c" "18:0 10me" "19:0 cyclo"
##
## $gram_pos_lipids
## [1] "15:0 iso" "15:0 anteiso"
##
## $gram_neg_lipids
## [1] "16:1 w7c" "18:1 w9t"
##
## $actino_lipids
## [1] "16:0 10me" "18:0 10me"
##
## $anaerobe_lipids
## [1] "19:0 cyclo"
##
## $protozoa
## [1] "20:4 w6,9,12,15"
##
## $total_biomass
## [1] "8:0" "10:0" "10:0 2OH" "11:0" "12:0"
## [6] "12:0 2OH" "12:0 3OH" "13:0" "14:0" "14:0 2OH"
## [11] "14:0 3OH" "14:1" "15:0" "15:0 anteiso" "15:0 iso"
## [16] "16:0 10me" "16:0 2OH" "16:0 iso" "16:1 w5c" "16:1 w7c"
## [21] "16:1 w9c" "17:0" "17:0 iso" "17:0 anteiso" "17:0 cyclo"
## [26] "17:1" "17:1 iso" "18:0" "18:0 10me" "18:1 w9c"
## [31] "18:1 w9t" "18:2 w6,9c" "19:0" "19:0 cyclo" "19:1"
## [36] "20:0"
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.