Use Case 3 - Processing Several Datasets

In metajam: Easily Download Data and Metadata from 'DataONE'

knitr::opts_chunk$set(collapse = TRUE,comment = "#>")

Summary

This vignette aims to showcase a use case using the 2 main functions of metajam - download_d1_data and read_d1_files using a data processing workflow developed by the NCO synthesis working group Stream Elemental Cycling.

The datasets used are from the LTER site - Luquillo and can be found in the PASTA data repository https://dx.doi.org/doi:10.6073/pasta/f9df56348f510da0113b1e6012fa2967. This data package is a collection of 8 datasets of stream water samples from 8 different locations of the Luquillo Mountains.

Our goal is to read the data for the 8 different sampling sites and aggregate them into one harmonized dataset. We will use the metadata to check if the data structures and units are the same across the 8 different sampling sites before performing the aggregation.

Libraries

#devtools::install_github("NCEAS/metajam")
library(metajam)  

# For wrangling the data
library(readr)
library(tidyr)
library(dplyr)
library(purrr)
library(stringr)

Constants

# Download the data from DataONE on your local machine
data_folder <- "Data_SEC"

# Ammonium to Ammoniacal-nitrogen conversion. We will use this conversion later.
coeff_conv_NH4_to_NH4N <- 0.7764676534

Download the datasets

# Create the local directory to store datasets
dir.create(data_folder, showWarnings = FALSE)

# Get the datasets unique identifiers
test_datasets_listing <- readr::read_csv(system.file("extdata", "LTER-SEC_DatasetsListing_SearchedData.csv", package = "metajam"))

# Keep only the LUQ related datasets
luq_test_datasets <- test_datasets_listing %>%
  dplyr::filter(grepl("LUQ", .$`LTER site abbreviation`)) %>%
  dplyr::select(`LTER site abbreviation`,
         `Data Repository (PASTA) URL to Archive/Metadata`,
         `Data Repository (PASTA) URL to File`,
         `Data Repository (PASTA) Filename`) %>%
  na.omit() %>%
  dplyr::arrange(`Data Repository (PASTA) Filename`) # sort the data sets alphabetically

## Batch download the datasets

# the tidiest way
local_datasets <- purrr::map(.x = luq_test_datasets$`Data Repository (PASTA) URL to File`,
                             .f = ~ download_d1_data(.x, data_folder))

# the apply way
# local_datasets <- lapply(luq_test_datasets$`Data Repository (PASTA) URL to File`, download_d1_data, data_folder)

# the map way
# local_datasets <- map(luq_test_datasets$`Data Repository (PASTA) URL to File`, function(x) {download_d1_data(x, data_folder)})

At this point, you should have all the data and the metadata downloaded inside your main directory; Data_SEC in this example. metajam organize the files as follow:

• Each dataset is stored a sub-directory named after the package DOI and the file name
• Inside this sub-directory, you will find
• the data: my_data.csv
• the raw EML with the naming convention file name + __full_metadata.xml: my_data__full_metadata.xml
• the package level metadata summary with the naming convention file name + __summary_metadata.csv: my_data__summary_metadata.csv
• If relevant, the attribute level metadata with the naming convention file name + __attribute_metadata.csv: my_data__attribute_metadata.csv
• If relevant, the factor level metadata with the naming convention file name + __attribute_factor_metadata.csv: my_data__attribute_factor_metadata.csv

Read the data and metadata in your R environment

# You could list the datasets dowloaded in the `Data_SEC` folder 
# local_datasets <- dir(data_folder, full.names = TRUE)

# or you can directly use the outputed paths from download_d1_data 
# Read all the datasets and their associated metadata in as a named list
luq_datasets <- purrr::map(local_datasets, read_d1_files) %>% 
  purrr::set_names(purrr::map(., ~.x$summary_metadata$value[.x$summary_metadata$name == "File_Name"]))

Perform checks on data structure

Is the data structure the same across sampling sites (datasets)? For example, do the datasets all have the same column names?

# list all the attributes
attributes_luq <- luq_datasets %>% purrr::map("data") %>% purrr::map(colnames)

# Check if they are identical by comparing all against the first site
for(ds in names(attributes_luq)) {
  print(identical(attributes_luq[[1]], attributes_luq[[ds]]))
}

#> => We are good, same data structure across the sampling sites

Conclusion

• the same attributes are reported at the different sampling sites

Perform checks on the units

Is data reported in identical units? For example, in every dataset is CI reported in microgramsPerLiter?

# List all the units used
luq_units <- luq_datasets %>% purrr::map("attribute_metadata") %>% purrr::map(~.[["unit"]])

# Check if they are identical by comparing all against the first site
for(us in names(luq_units)) {
  print(identical(luq_units[[1]], luq_units[[us]]))
}

#>!!! => The 2 last datasets have different units!!!!!!!!!!

# Let's check the differences
luq_units_merged <- luq_datasets %>%
  purrr::map("attribute_metadata") %>%
  purrr::map(. %>% select(attributeName, unit)) %>%
  purrr::reduce(full_join, by = "attributeName") 

## Rename
# Create the new names
luq_new_colnames <- names(luq_units) %>%
  stringr::str_split("[.]") %>%
  purrr::map(~.[1]) %>%
  paste("unit", ., sep = "_")

# Apply the new names
colnames(luq_units_merged) <- c("attributeName", luq_new_colnames)

Conclusion

• For the 2 last sampling sites RioIcacos and RioMameyesPuenteRoto, the units used for the gage height ("Gage_Ht") are in feet and not meters like the other sites
• For the 2 last sampling sites RioIcacos and RioMameyesPuenteRoto, NH4 and not NH4-N is measured

Fixing units discrepancies

# fix attribute naming discrepancies -- to be improved 
# Copy the units for Gage height
luq_units_merged <- luq_units_merged %>% 
  dplyr::mutate(unit_RioIcacos = ifelse(test = attributeName == "Gage_Ht",
                                        yes = "foot", no = unit_RioIcacos),
                unit_RioMameyesPuenteRoto = ifelse(test = attributeName == "Gage_Ht",
                                                   yes = "foot", no = unit_RioMameyesPuenteRoto))


# Copy the units for NH4
luq_units_merged <- luq_units_merged %>% 
  dplyr::mutate(unit_RioIcacos = ifelse(test = attributeName == "NH4-N",
                                        yes = "microgramsPerLiter", no = unit_RioIcacos),
                unit_RioMameyesPuenteRoto = ifelse(test = attributeName == "NH4-N",
                                                   yes = "microgramsPerLiter",
                                                   no = unit_RioMameyesPuenteRoto))

# drop the 2 last rows
luq_units_merged <- head(luq_units_merged, -2)

### Implement the unit conversion for RioIcacos and RioMameyesPuenteRoto ----

# Simplify naming
RioIcacos_data <- luq_datasets$RioIcacos$data
RioIcacos_attrmeta <- luq_datasets$RioIcacos$attribute_metadata


## RioIcacos
# Fix NAs. In this dataset "-9999" is the missing value code. So we need to replace those with NAs
RioIcacos_data <- na_if(RioIcacos_data, "-9999")

# Do the unit conversion  
RioIcacos_data <- RioIcacos_data %>% 
  dplyr::mutate( `Gage_Ht` = `Gage_Ht`* 0.3048)

# Update the units column accordingly
RioIcacos_attrmeta <- RioIcacos_attrmeta %>% 
  dplyr::mutate(unit = gsub(pattern = "foot", replacement = "meter", x = unit))

# Do the unit conversion for RioIcacos and RioMameyesPuenteRoto - NH4 to NH4-N

# Ammonium to Ammoniacal-nitrogen conversion
coeff_conv_NH4_to_NH4N <- 0.7764676534

# Unit conversion for RioIcacos and RioMameyesPuenteRoto - NH4 to NH4-N
RioIcacos_data <- RioIcacos_data %>% mutate( `NH4-N` = `NH4-N`* coeff_conv_NH4_to_NH4N)

# Update the main object 
luq_datasets$RioIcacos$data <- RioIcacos_data

## RioMameyesPuenteRoto

# Simplify naming
RioMameyesPuenteRoto_data <- luq_datasets$RioMameyesPuenteRoto$data
RioMameyesPuenteRoto_attrmeta <- luq_datasets$RioMameyesPuenteRoto$attribute_metadata

#Replace all cells with the missing value code ("-9999") with "NA"
RioMameyesPuenteRoto_data <- na_if(RioMameyesPuenteRoto_data, "-9999")

#Tidy version of unit conversion 
RioMameyesPuenteRoto_data <- RioMameyesPuenteRoto_data %>% 
  dplyr::mutate(`Gage_Ht` = `Gage_Ht`* 0.3048)

# Update the units column accordingly
RioMameyesPuenteRoto_attrmeta <- RioMameyesPuenteRoto_attrmeta %>% 
  dplyr::mutate(unit = gsub(pattern = "foot", replacement = "meter", x = unit))

# Do the unit conversion for RioMameyesPuenteRoto - NH4 to NH4-N 

#In this dataset the NH4-N column is actually empty, so this is not necessary. But here is how you would do it if you had to.

RioMameyesPuenteRoto_data <- RioMameyesPuenteRoto_data %>% 
  dplyr::mutate( `NH4-N` = `NH4-N`* coeff_conv_NH4_to_NH4N)

# Update the main object
luq_datasets$RioMameyesPuenteRoto$data <- RioMameyesPuenteRoto_data 

Append all the sampling sites into one master dataset

# bind the sampling sites data into one master dataset for LUQ
all_sites_luq <- luq_datasets %>%
  purrr::map("data") %>% 
  dplyr::bind_rows(.id = "prov")

# Replace -9999 with NAs
all_sites_luq <- na_if(all_sites_luq, "-9999")

# Write as csv
write_csv(all_sites_luq, "stream_chem_all_LUQ.csv")

General Conclusion

• Although the column names were the same in all the datasets / sampling sites, looking at the metadata we discovered that 2 sampling sites are measuring stream gage height and NH4 concentration using different protocols.
• We used the metadata to perform the necessary unit conversions to homogenize the 8 datasets before merging them into one master dataset.
• During the merge process, we added a provenance column to be able to track the origin of each row, allowing users of the master datasets to check the original datasets metadata when necessary.

metajam documentation built on Sept. 11, 2024, 9:03 p.m.