Troubleshooting Missing Parameters In EMS Data Queries With Rems Package

Introduction

When working with environmental monitoring data, ensuring data completeness is crucial for accurate analysis and informed decision-making. This article delves into a common challenge encountered when querying data from Environmental Monitoring System (EMS) databases: missing parameters. Specifically, we address a scenario where a user, querying data for a station on the Nicola River using the rems package in R, found that certain parameters, such as Total Phosphorus, were absent from the results obtained through the rems interface, despite their presence in the EMS Arc Dashboard. This discrepancy highlights the complexities involved in data retrieval and the importance of understanding the nuances of data access methods. This article aims to provide a comprehensive analysis of this issue, offering insights into potential causes and solutions for ensuring complete and reliable environmental data retrieval using the rems package.

Background: The Scenario

A user attempted to retrieve data for a specific station on the Nicola River using the rems package in R. The code snippet provided in the original query demonstrates a standard workflow for accessing and filtering EMS data:

library(rems)
library(dplyr)
library(lubridate)

start_date <- ymd('2024-09-30')
end_date <- ymd('2025-04-01')
ems_2yr <- get_ems_data(which = '2yr')

relv_ems_ids <- c(
  # NICOLA RIVER AT THE MOUTH
  'E216848',
)

# Filtering data for relevant locations
curr_ems <- ems_2yr |>
  filter(EMS_ID %in% relv_ems_ids) |>
  filter(COLLECTION_START >= start_date & COLLECTION_START <= end_date)

The user filtered data for a specific time period (September 30, 2024, to April 1, 2025) and location (EMS_ID 'E216848'). While the query returned data within the specified timeframe, it was noted that certain parameters, notably Total Phosphorus, were missing from the results. Upon cross-referencing with the EMS Arc Dashboard, records for Total Phosphorus were found to exist for the same station and time period. This inconsistency raised concerns about the completeness of the data retrieved using the rems package.

The Importance of Complete Data

In environmental monitoring, the completeness of data is paramount. Missing parameters can lead to inaccurate assessments of water quality, skewed trend analyses, and potentially flawed decision-making. For instance, the absence of Total Phosphorus data can hinder the evaluation of nutrient loading and its impact on aquatic ecosystems. Therefore, it is essential to identify and address any discrepancies in data retrieval to ensure the reliability of the results.

The Preference for rems

The user expressed a preference for using the rems interface due to several advantages it offers over downloading data directly from the EMS Arc Dashboard. These advantages include consistent column nomenclature, standardized parameter names, and the inclusion of crucial metadata such as test method codes, matrix information, and sample types (e.g., blanks or regular samples). These features streamline data processing and analysis, making rems a valuable tool for environmental data management. However, the issue of missing parameters necessitates a closer examination of the potential causes and solutions.

Potential Causes for Missing Parameters

Several factors could contribute to the discrepancy between the data available on the EMS Arc Dashboard and the data retrieved using the rems package. Understanding these potential causes is crucial for troubleshooting and resolving the issue. Let's explore some of the primary reasons why parameters might be missing from the rems query results:

1. Data Availability and Publication Delays

One of the most common reasons for missing parameters is related to the timing of data publication. Environmental monitoring data often undergoes quality control and validation processes before being made publicly available. There may be a delay between the time a sample is collected and analyzed and the time the data is published to the EMS database. Therefore, if the EMS Arc Dashboard is updated more frequently than the rems data cache, it is possible that recent data, including Total Phosphorus records, may be visible on the dashboard but not yet accessible through rems. To address this, it is essential to check the data update frequency and consider potential delays in data publication.

2. Data Filtering and Subsetting within rems

The rems package may employ internal filtering or subsetting mechanisms that unintentionally exclude certain parameters. This could be due to default settings, specific query parameters, or data quality flags that trigger the exclusion of certain records. For example, the get_ems_data() function might have default filters that omit data based on specific quality control criteria or parameter types. Additionally, if the user's query includes filters based on data quality flags or other metadata fields, it is possible that records for Total Phosphorus are being inadvertently excluded. To investigate this, it is crucial to examine the rems package documentation and explore any available options for controlling data filtering and subsetting.

3. Data Storage and Database Structure

The structure of the EMS database itself can influence the availability of parameters. Data may be stored in different tables or schemas, and the rems package might not be configured to access all of these data sources. It is also possible that certain parameters, such as Total Phosphorus, are stored in a separate table or have different naming conventions that are not recognized by the rems query. Understanding the database schema and data storage practices is essential for ensuring that all relevant data sources are accessed. Collaborating with database administrators or data experts can help identify any structural issues that might be contributing to the missing parameter problem.

4. Parameter Naming and Mapping Discrepancies

Inconsistencies in parameter naming and mapping can also lead to missing data. The EMS Arc Dashboard and the rems package may use different naming conventions for parameters, making it difficult to retrieve data using a direct query. For example, Total Phosphorus might be referred to by a different name or code in the rems database compared to the dashboard. Furthermore, if there are mapping errors between parameter names and their corresponding database fields, data retrieval may be incomplete. To resolve this issue, it is necessary to carefully examine the parameter naming conventions used by both the EMS Arc Dashboard and the rems package and ensure that queries are formulated using the correct parameter names or codes.

5. Data Quality Flags and Validation Procedures

Data quality flags and validation procedures play a critical role in ensuring the accuracy and reliability of environmental monitoring data. However, they can also contribute to missing parameters if certain data records are flagged as invalid or unreliable. The rems package may be configured to exclude data records that have been flagged with specific quality control codes. If Total Phosphorus data is being flagged due to quality concerns, it may be excluded from the rems query results. To address this, it is important to understand the data quality flagging criteria and assess whether the excluded data is indeed unreliable or if the flags are overly conservative. In some cases, it may be necessary to consult with data quality experts or adjust the data filtering criteria to include valid data records.

Troubleshooting Steps and Solutions

Addressing the issue of missing parameters requires a systematic approach to troubleshooting and resolution. Here are several steps and solutions that can be employed to identify and rectify the problem:

1. Verify Data Availability and Publication Status

Begin by confirming the data's availability and publication status. Check the EMS Arc Dashboard for the most recent data updates and compare them to the rems data cache. If there is a significant time lag, it may simply be a matter of waiting for the data to be updated in the rems database. Contacting the data providers or database administrators can provide insights into data update schedules and potential delays. Additionally, explore any available documentation or metadata that specifies data publication timelines.

2. Examine rems Package Documentation and Settings

Thoroughly review the rems package documentation to understand its data filtering and subsetting capabilities. Pay close attention to any default settings or query parameters that might be inadvertently excluding Total Phosphorus data. Experiment with different query options and explore any available functions for controlling data filtering. For example, the rems package might have options for including or excluding data based on quality control flags or parameter types. Consult the package documentation or online forums for guidance on specific query parameters and their effects on data retrieval.

3. Inspect the EMS Database Schema and Structure

Investigate the EMS database schema and structure to gain insights into how data is stored and organized. Identify the tables or schemas that contain environmental monitoring data and determine if Total Phosphorus data is stored in a separate location. If necessary, consult with database administrators or data experts to understand the database structure and access methods. Examining the database schema can reveal potential issues related to data storage, naming conventions, and mapping errors. This information is crucial for formulating accurate queries and ensuring that all relevant data sources are accessed.

4. Check Parameter Naming Conventions and Mapping

Compare the parameter naming conventions used by the EMS Arc Dashboard and the rems package. Verify that the correct parameter names or codes are being used in the rems queries. If there are discrepancies, update the queries to use the appropriate parameter names. Consider creating a mapping table that translates between different naming conventions to facilitate data retrieval and analysis. Additionally, explore the rems package's capabilities for mapping parameter names and codes. Some packages provide functions or tools for resolving naming inconsistencies and ensuring accurate data retrieval.

5. Evaluate Data Quality Flags and Filtering Criteria

Assess the impact of data quality flags and filtering criteria on the missing parameter issue. Understand the criteria used to flag data as invalid or unreliable and determine if Total Phosphorus data is being excluded due to these flags. If the flags are overly conservative or if there is reason to believe that the flagged data is still valid, consider adjusting the filtering criteria or consulting with data quality experts. It is important to strike a balance between data quality and data completeness. Excluding data solely based on flags without further evaluation can lead to information loss and potentially biased results.

6. Consult with rems Package Developers and Community

If the issue persists despite the above troubleshooting steps, consider reaching out to the rems package developers and community for assistance. Online forums, mailing lists, and issue trackers are valuable resources for seeking help and sharing experiences. Provide detailed information about the problem, including the code used, the expected results, and the actual results obtained. The rems developers and community members may be able to provide insights, identify bugs, or suggest alternative approaches for data retrieval. Collaborating with experts can often lead to effective solutions and improvements in data access methods.

7. Implement Data Validation and Reconciliation Procedures

To ensure data completeness and accuracy, implement data validation and reconciliation procedures. Regularly compare data retrieved using different methods, such as the rems package and the EMS Arc Dashboard, to identify any discrepancies. Develop automated scripts or workflows for data validation and reconciliation to streamline the process and minimize manual effort. Data validation procedures can include checks for missing values, outliers, and inconsistencies between different data sources. Reconciliation procedures can involve merging data from multiple sources, resolving naming conflicts, and addressing data quality issues. By implementing these procedures, it is possible to maintain a comprehensive and reliable environmental monitoring dataset.

Alternative Solutions

In situations where resolving the missing parameter issue within rems proves challenging or time-consuming, alternative solutions may be considered. While these solutions may not be ideal in the long term, they can provide temporary workarounds for accessing the necessary data:

1. Direct Data Download from EMS Arc Dashboard

As the user mentioned, downloading data directly from the EMS Arc Dashboard is an option. While this approach may require additional data processing steps due to differences in column nomenclature and parameter names, it can provide access to the missing Total Phosphorus data. Develop scripts or workflows to standardize the data downloaded from the dashboard and integrate it with the data retrieved using rems. This approach can serve as a temporary solution while the underlying issue within rems is being addressed.

2. Manual Data Entry or Compilation

In some cases, manual data entry or compilation may be necessary to fill in the gaps caused by missing parameters. This approach is labor-intensive and prone to errors, but it can be a viable option when the amount of missing data is limited. Carefully review the available data sources, such as laboratory reports or field notes, and manually enter the missing Total Phosphorus data into the dataset. Implement quality control checks to minimize errors and ensure data accuracy. Manual data entry should be considered a last resort and should be replaced with automated solutions whenever possible.

Conclusion

Addressing missing parameters in EMS data queries is crucial for ensuring the accuracy and reliability of environmental monitoring data. The scenario presented in this article highlights the complexities involved in data retrieval and the importance of understanding the potential causes of data discrepancies. By systematically troubleshooting the issue, implementing appropriate solutions, and considering alternative approaches, it is possible to overcome the challenges of missing parameters and maintain a comprehensive environmental monitoring dataset. The rems package remains a valuable tool for accessing and managing EMS data, but it is essential to be aware of its limitations and to employ best practices for data validation and reconciliation. Continued collaboration between data users, package developers, and database administrators is crucial for improving data access methods and ensuring the availability of complete and reliable environmental information.

By addressing missing parameters and ensuring data completeness, environmental scientists and decision-makers can gain a more accurate understanding of water quality trends, assess the effectiveness of environmental management strategies, and make informed decisions to protect aquatic ecosystems. The pursuit of complete and reliable environmental data is essential for achieving sustainable water resource management and safeguarding the health of our planet.