---
title: "Use Case 3 - Processing Several Datasets"
author: "Julien Brun, Mitchell Maier, Irene Steves and Kristen Peach, NCEAS"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Use Case 3 - Processing Several Datasets}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
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](https://lternet.edu/working-groups/global-patterns-in-stream-energy-and-nutrient-cycling/). 

The datasets used are from the [LTER site - Luquillo](https://luquillo.lter.network/) 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

```{r libraries, message=FALSE}
#devtools::install_github("NCEAS/metajam")
library(metajam)  

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

## Constants

```{r 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

```{r download, eval=FALSE}
# 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

```{r read_data, eval=FALSE}
# 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? 

```{r attributes, eval=FALSE}
# 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?

```{r units, eval=FALSE}
# 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

```{r fixing_units, eval=FALSE}
# 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

```{r combine, eval=FALSE}
# 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.
