Overview

fluxtools is an R package that provides an interactive Shiny‐based QA/QC environment for data in the AmeriFlux BASE format. In just a few clicks, you can:

1. Upload eddy covariance data in a .csv format (AmeriFlux standard naming and timestamp conventions)
2. Visualize any two numeric columns against time (or each other)
3. Highlight statistical outliers (±σ from a linear fit) and add them to your point-removal R code
4. Manually select and remove data points via a lasso or box. Selecting these adds to the accumulated removal code
5. Copy and paste the generated code into your own R script for reproducible QA/QC
6. Download a “cleaned” CSV with excluded values (using “apply removals”) set to NA and an R script for reproducibility

This vignette shows you how to install, launch, and use the main Shiny app—run_flux_qaqc()—and walks through a typical workflow.

Installation

You can install fluxtools from CRAN, or directly from GitHub:

# Install from CRAN 
install.packages("fluxtools")

# Install from GitHub
library(devtools) 
devtools::install_github("kesondrakey/fluxtools")

Launching the Shiny App

Load fluxtools and launch the QA/QC application:

library(fluxtools)

# Add the UTC offset for your flux tower site (e.g., UTC-5 for EST)
run_flux_qaqc(-5)

Example workflow

1. Upload: Select your AmeriFlux-style CSV (e.g., US_VT1_HH_202401010000_202501010000.csv). Files can be up to 500MB (larger file sizes might be harder on the Shiny interface)

2. Choose Year(s): By default “all” is selected, but you can subset to specific years

3. Choose variables: TIMESTAMP_START is on the x-axis by default. Change the y-axis to your variable of interest (e.g., FC_1_1_1). The generated R code focuses on removing the y-axis variable

4. Select data: Use the box or lasso to select points. This populates the “Current” code box with something like:

   df <- df %>%
     mutate(
       FC_1_1_1 = case_when(
         TIMESTAMP_START == '202401261830' ~ NA_real_,
         TIMESTAMP_START == '202401270530' ~ NA_real_,
         …
         TRUE ~ FC_1_1_1
       )
     )

5. Flag data and Accumulate code: With points still selected, click “Flag data.” Selected points turn orange, and code is appended to the “Accumulated” box, allowing multiple selections per session.

6. Unflag data: Use the box or lasso to de-select points and remove from the Accumulated code box.

7. Clear Selection: To reset all selections from the current y-variable, click “Clear Selection” to reset the current view.

8. Switch variables: Change y to any other variable (e.g., SWC_1_1_1) and select more points. Click “Flag data” Code for both variables to appear:

  df <- df %>%
    mutate(
      FC_1_1_1 = case_when(
        TIMESTAMP_START == '202401261830' ~ NA_real_,
        TIMESTAMP_START == '202401270530' ~ NA_real_,
        …
        TRUE ~ FC_1_1_1
      )
    )

  df <- df %>%
    mutate(
      SWC_1_1_1 = case_when(
        TIMESTAMP_START == '202403261130' ~ NA_real_,
        TIMESTAMP_START == '202403270800' ~ NA_real_,
        …
        TRUE ~ SWC_1_1_1
      )
    )

9. Compare variables: Change to variables you would like to compare (e.g., change y to TA_1_1_1 and x to T_SONIC_1_1_1). The app computes an R² via simple linear regression. The top R² is based on points before removals, and once data is selected, a second R² will pop up - calculating the linear regression assuming the selected points have been removed

10. Highlight outliers: Use the slider to select ±σ residuals. Click “Select all ±σ outliers” to append them to the Accumulated code. Click “Clear ±σ outliers” to deselect and remove from the code box

11. Copy all: Click the Copy Icon to the right of the current or accumulated code box and paste into your own R script for documentation

12. Apply Removals: Click “Apply Removals” to remove each selected data points, from the current y-variable, to replace points with NA in a new .csv (raw data is unaffected), available using ‘export cleaned data’ and remove these values from view

13. Reload original data: Make a mistake or want a fresh start? Click Reload original data to reload the .csv from above to start over

14. Export cleaned data: Download the cleaned .csv reflecting your confirmed removals. This button will download a zip file containing your .csv, reflecting changes from using the “apply removals” button, and includes a compiled R script with the R code for those removals.

Fluxtools is an independent project and is not affiliated with or endorsed by the AmeriFlux Network. “AmeriFlux” is a registered trademark of Lawrence Berkeley National Laboratory and is used here for identification purposes only.