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goFlux: A user-friendly way to calculate GHG fluxes yourself, regardless of user experience
https://github.com/Qepanna/goFlux
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goFlux: A user-friendly way to calculate GHG fluxes yourself, regardless of user experience
Host: GitHub
URL: https://github.com/Qepanna/goFlux
Owner: Qepanna
License: gpl-2.0
Created: 2023-08-30T09:30:06.000Z (over 1 year ago)
Default Branch: master
Last Pushed: 2024-09-05T08:54:17.000Z (4 months ago)
Last Synced: 2024-10-29T19:41:44.455Z (2 months ago)
Language: R
Homepage: https://qepanna.quarto.pub/goflux/
Size: 18.1 MB
Stars: 18
Watchers: 3
Forks: 5
Open Issues: 10
Metadata Files:
- Readme: README.md
- License: LICENSE
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open-sustainable-technology - goFlux - A user-friendly way to calculate greenhouse gas fluxes in soil yourself, regardless of user experience. (Emissions / Emission Observation and Modeling)
README

        

# goFlux: A user-friendly way to calculate GHG fluxes yourself, regardless of user experience

### One Package to rule them all

Non-steady state (static) chambers are widely used for measuring soil

greenhouse gas (GHG) fluxes, such as CO₂, CH₄,

N₂O, NH₃, CO, and water vapor (H₂O).

While linear regression (LM) is commonly used to estimate GHG fluxes,

this method tends to underestimate the pre-deployment flux

(*f*₀). Indeed, non-linearity is expected when the gas

concentration increases or decreases inside a closed chamber, due to

changes in gas gradients between the soil and the air inside the

chamber. In addition, lateral gas flow and leakage contribute to

non-linearity. Many alternative to LM have been developed to try to

provide a more accurate estimation of *f*₀, for instance the

method of [Hutchinson and Mosier (HM)

(1981)](https://doi.org/10.2136/sssaj1981.03615995004500020017x), which

was implemented in the [`HMR`](https://cran.r-project.org/package=HMR)

package by [Pedersen et al.,

2010](https://doi.org/10.1111/j.1365-2389.2010.01291.x). However,

non-linear models have a tendency to largely overestimate some flux

measurements, due to an exaggerated curvature. Therefore, the user is

expected to decide which method is more appropriate for each flux

estimate. With the advent of portable greenhouse gas analyzers

(e.g. [Los Gatos Research (ABB) laser gas

analyzers](https://new.abb.com/products/measurement-products/analytical/laser-gas-analyzers/laser-analyzers/lgr-icos-portable-analyzers)),

soil GHG fluxes have become much easier to measure, and more efficient

ways to calculate these flux estimates are needed in order to process

large amounts of data. A recent approach was developed by [Hüppi et al.,

2018](https://doi.org/10.1371/journal.pone.0200876) to restrict the

curvature in the HM model for a more reliable flux estimate. In the HM

model, the curvature is controlled by the kappa parameter, $\kappa$.

Hüppi et al. suggest the use of the `KAPPA.MAX` to limit the maximal

curvature allowed in the model (see their R package

[`gasfluxes`](https://cran.r-project.org/package=gasfluxes), available

on the CRAN). This procedure introduces less arbitrary decisions in the

flux estimation process.

Previous software developed to calculate GHG fluxes are limited in many

aspects that the goFlux package is meant to overcome. Most are limited

to the linear regression approach (e.g. [Flux

Puppy](https://www.sciencedirect.com/science/article/pii/S0168192319301522),

and the R packages [`flux`](https://cran.r-project.org/package=flux) and

[`FluxCalR`](https://github.com/junbinzhao/FluxCalR)), others do not

include data pre-processing (e.g. the R packages

[`HMR`](https://cran.r-project.org/package=HMR),

[`flux`](https://cran.r-project.org/package=flux) and

[`gasfluxes`](https://cran.r-project.org/package=gasfluxes)), or if they

do, they are compatible with only a few designated systems (e.g. [Flux

Puppy](https://www.sciencedirect.com/science/article/pii/S0168192319301522)

and [`FluxCalR`](https://github.com/junbinzhao/FluxCalR)), and none

include an automatic selection of the best flux estimate (LM or HM)

based on objective criteria, except the R packages

[`gasfluxes`](https://cran.r-project.org/package=gasfluxes) and

[`HMR`](https://cran.r-project.org/package=HMR).

This new R package `goFlux` is meant to be “student proof”, meaning that

no extensive knowledge or experience is needed to import raw data into

R, choose the best model to calculate fluxes (LM or HM), quality check

the results objectively and obtain high quality flux estimates. The

package contains a wide range of functions that allows the user to

import raw data from a variety of instruments (LI-COR, LGR, GAIA2TECH,

Gasmet, Picarro, Aeris and PP-Systems); calculate fluxes from a variety

of GHG (CO₂, CH₄, N₂O, NH₃,

CO and H₂O) with both linear (LM) and non-linear (HM) flux

calculation methods; align instruments clocks after data import;

interactively identify measurements (start and end time) if there are no

automatic chamber recordings (e.g. LI-COR smart chamber); plot

measurements for easy visual inspection; and quality check the

measurements based on objective criteria.

> *Three R packages for the Elven-kings under the CRAN,  

> Seven for the Dwarf-lords in their halls of open software,  

> Nine for Mortal Men doomed to write scripts themselves,  

> One for the Dark Lady on her dark throne  

> In the Land of GitHub where the Shadows lie.  

> One Package to rule them all, One Package to find them,  

> One Package to bring them all and in the darkness bind them  

> In the Land of GitHub where the Shadows lie.*

## About the package

The R package `goFlux` is meant to be “student proof”, meaning that no

extensive knowledge or experience is needed to import raw data into R

(except for knowing how to use R, of course), choose the best model to

calculate fluxes (LM or HM, that is the question. -Shakespeare, 1603),

quality check the results objectively (hence the no experience needed)

and obtain high quality flux estimates from static chamber measurements

(wonderful!).

Look up [this webpage](https://qepanna.quarto.pub/goflux/) for a

demonstration of the package usage.

### Import and measurement identification

The package contains a wide range of functions that let the user import

raw data from a variety of instruments:

- [**LI-COR trace gas

  analyzers**](https://www.licor.com/env/products/trace-gas/): LI-6400

  for CO₂ and H₂O, LI-7810 for CO₂,

  CH₄ and H₂O, LI-7820 for N₂O and

  H₂O, and LI-8100 for CO₂ and H₂O

- [**LI-COR Smart

  Chamber**](https://www.licor.com/env/products/soil-flux/survey): for

  an easy import of data from any gas analyzer

- [**Los Gatos Research (ABB) laser gas

  analyzers**](https://new.abb.com/products/measurement-products/analytical/laser-gas-analyzers/laser-analyzers/lgr-icos-portable-analyzers):

  Ultra-portable Greenhouse Gas Analyzer (GLA132-UGGA) and Microportable

  Greenhouse Gas Analyzer (GLA131-MGGA) for CO₂,

  CH₄ and H₂O, and GLA151-N2OM1 for

  N₂O, CH₄ and H₂O

- [**Picarro G2508 Gas Concentration

  Analyzer**](https://www.picarro.com/environmental/products/g2508_gas_concentration_analyzer):

  for CO₂, CH₄, N₂O, NH₃ and

  H₂O

- [**Picarro GasScouter^TM G4301 Mobile Gas Concentration

  Analyzer**](https://www.picarro.com/products/gas-scouter-g4301): for

  CO₂, CH₄ and H₂O

- [**GAIATECH Automated ECOFlux

  chamber**](https://www.dmr.eu/technologies/gas-emission-measurements-eco2flux/automated-eco2flux-chamber/):

  for an easy import of data from any gas analyzer

- [**Gasmet DX4015 portable analyzer for humid

  conditions**](https://www.gasmet.com/products/category/portable-gas-analyzers/dx4015/):

  for CO, CO₂, CH₄, N₂O, NH₃

  and H₂O

- [**PP-Systems EGM-5 Portable CO₂ Gas

  Analyzer**](https://ppsystems.com/egm-5/): for CO₂,

  O₂ and H₂O

- [**Aeris technologies MIRA Ultra Gas

  Analyzers**](https://aerissensors.com/ultra-series/):

  N₂O/CO₂ high accuracy analyzer (+H₂O)

  and CH₄/C₂H₆ Natural Gas Leak

  Detection System (+H₂O)

- **Custom made multiplexer** from the University of Padova, Italy,

  which integrates data from multiple automatic chambers linked to a

  Gasmet DX4015.

After import, the user can choose from two methods to define the start

and end points of each measurement and assign a UniqueID:

- **Manual identification of measurements** - based on `start.time`,

  provided separately in an auxiliary file. The function `obs.win`

  splits the imported data into a list of data frame (divided by

  `UniqueID`) and creates an observation window around the `start.time`

  to allow for a manual selection of the start and end points of each

  measurements, using the function `click.peak2`.

- **Automatic selection of measurements** - based on automatic

  recordings of chamber opening and closing from an instrument such as

  the LI-COR Smart Chamber or the GAIATECH Automated ECOFlux chamber.

Follow these links for more details and demonstrations about [importing

raw data](https://qepanna.quarto.pub/goflux/import.html) from the listed

instruments, or the [identification of

measurements](https://qepanna.quarto.pub/goflux/manualID.html).

### Flux calculation

The function `goFlux` calculates fluxes from a variety of greenhouse

gases (CO₂, CH₄, N₂O, NH₃,

CO, and H₂O) using both linear (LM) and non-linear (HM;

[Hutchinson and Mosier,

1981](https://doi.org/10.2136/sssaj1981.03615995004500020017x)) flux

calculation methods. The HM model for the chamber concentration $C_t$ at

time $t > 0$ after deployment is given by:

$$\mathbf{Eqn~1}~~~~~~C_t = \varphi~+~(C_0 - \varphi)e^{-~\kappa~t}$$

Where $\varphi$ is the assumed concentration of constant gas source

below the surface (also known as $C_i$), $C_0$ is the concentration in

the chamber at the moment of chamber closure and $\kappa$ (kappa)

determines the curvature of the model. A large kappa returns a strong

curvature.

A maximum threshold for this parameter, kappa-max ($k.max$), can be

calculated from the linear flux estimate ($LM.flux$), the minimal

detectable flux ($MDF$) and the time of chamber closure ($t$) ([Hüppi et

al., 2018](https://doi.org/10.1371/journal.pone.0200876)).

$$\mathbf{Eqn~2}~~~~~~k.max = \frac{LM.flux}{MDF~\times~t}$$

Where $LM.flux$ and $MDF$ have the same units (nmol or

µmol·m^-2·s^-1) and $t$ is in seconds. Therefore,

the units of kappa-max is s^-1. This limit of kappa-max is

included in the `goFlux` function, so that the non-linear flux estimate

cannot exceed this maximum curvature. See below for more details about

the minimal detectable flux (MDF).

All flux estimates, including the MDF, are multiplied by a $flux.term$

which is used to correct for water vapor inside the chamber, as well as

convert the units to obtain a term in nmol or

µmol·m^-2·s^-1:

$$\mathbf{Eqn~3}~~~~~~flux.term = \frac{(1 - H_2O)~V~P}{A~R~T}$$

Where $H_2O$ is the water vapor in mol·mol^-1, $V$ is the

volume inside the chamber in liters, $P$ is the pressure in kPa, $A$ is

the surface area inside the chamber in m², $R$ is the

universal gas constant in L·kPa·K^-1·mol^-1. Each

parameters are measured inside the chamber at $t = 0$.

More details and demonstrations about the function `goFlux` can be found

[here](https://qepanna.quarto.pub/goflux/goFlux.html).

### Automatic selection of the best flux estimate

Following flux calculation, the user can select the best flux estimate

(LM or HM) based on objective criteria, using the `best.flux` function:

- **Assumed non-linearity**: If all criteria are met, the best flux

  estimate is assumed to be the non-linear estimate from the Hutchinson

  and Mosier model.

- **G-factor**: the ratio between a non-linear flux estimate

  (e.g. Hutchinson and Mosier; HM) and a linear flux estimate.

- **Kappa ratio**: maximal limit of the ratio between kappa and

  kappa-max ($k.max$).

- **Indices of model fit**: the best model can be selected based on a

  selection of indices of model fit: MAE, RMSE, SE and AICc. In addition

  to the automatic selection of the best flux estimate based on these

  indices of model fit, measurements can be flagged “noisy” using these

  criteria.

In addition, the `best.flux` function can flag measurements that are

below detection limit (MDF and *p-value*), out of bounds (intercept), or

too short (number of observations).

- **Minimal Detectable Flux (MDF)**: limit under which a flux estimate

  is considered below the detection limit.

- **Statistically significant flux (*p-value*)**: limit under which a

  flux estimate is considered statistically non-significant, and below

  the detection limit.

- **Intercept**: inferior and superior limits of the intercept (initial

  concentration; $C_0$).

- **Number of observations**: limit under which a measurement is flagged

  for being too short.

By default, all criteria are included:

`criteria = c("MAE", "RMSE", "AICc", "SE", "g-factor", "kappa", "MDF", "nb.obs", "p-value", "intercept")`

A demonstration of the usage of the function `best.flux` can be found

[here](https://qepanna.quarto.pub/goflux/bestflux.html).

#### **G-factor**

The g-factor is the ratio between a non-linear flux estimate

(e.g. Hutchinson and Mosier; HM) and a linear flux estimate ([Hüppi et

al., 2018](https://doi.org/10.1371/journal.pone.0200876)).

$$\mathbf{Eqn~4}~~~~~~g-factor = \frac{HM.flux}{LM.flux}$$

With the `best.flux` function, one can choose a limit at which the HM

model is deemed to overestimate (*f*₀). Recommended

thresholds for the g-factor are \<4 for a flexible threshold, \<2 for a

medium threshold, or \<1.25 for a more conservative threshold. The

default threshold is `g.limit = 2`. If the g-factor is above the

specified threshold, the best flux estimate will switch to LM instead of

HM. If `HM.flux/LM.flux` is larger than `g.limit`, a warning is given in

the columns `HM.diagnose` and `quality.check`.

#### **Minimal Detectable Flux (MDF)**

The minimal detectable flux ($MDF$) is based on instrument precision

($prec$) and measurement time ($t$) ([Christiansen et al.,

2015](https://doi.org/10.1016/j.agrformet.2015.06.004)).

$$\mathbf{Eqn~5}~~~~~~MDF = \frac{prec}{t}~\times~flux.term$$

Where the instrument precision is in the same units as the measured gas

(ppm or ppb) and the measurement time is in seconds.

Below the MDF, the flux estimate is considered under the detection

limit, but not null. Therefore, the function will not return a 0. There

will simply be a warning in the columns `quality.check`, `LM.diagnose`

or `HM.diagnose` to warn of a flux estimate under the detectable limit.

No best flux estimate is chosen based on MDF.

#### **Kappa ratio**

The parameter kappa determines the curvature of the non-linear

regression in the Hutchinson and Mosier model, as shown in equation 1.

The limit of kappa-max, as calculated in equation 2, is included in the

`goFlux` function, so that the non-linear flux estimate cannot exceed

this maximum curvature.

In the function `best.flux`, one can choose the linear flux estimate

over the non-linear flux estimate based on the ratio between kappa and

kappa-max (`k.ratio`). A value of 1 indicates `HM.k = k.max`, and a

value of e.g. 0.5 indicates `HM.k = 0.5*k.max`. The default setting is

`k.ratio = 1`. If `HM.k/k.max` is larger than `k.ratio`, a warning is

issued in the columns `HM.diagnose` and `quality.check`. The ratio is

expressed as a percentage of kappa-max in the plots.

#### **Indices of model fit**

In the `best.flux` function, we included multiple choices of indices of

model fit, described below. One can choose to include however many of

them in the function. If multiple of them are included, the selection of

the best model will be made based on a scoring system. Both models start

with a score of 0. For each criteria, whichever model performs worst is

given +1. After all selected criteria have been evaluated, the model

with the lowest score wins. In case of a tie, the non-linear flux

estimate is always chosen by default, as non-linearity is assumed. The

score is printed in the output data frame in the columns `HM.score` and

`LM.score`.

##### **Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)**

The mean absolute error (MAE) is the arithmetic mean of the absolute

residuals of a model, calculated as follows:

$$

\mathbf{Eqn~6}~~~~~~MAE = \frac{1}{n} \sum_{i = 1}^{n}{\lvert{y_i-\hat{y_i}}\rvert}

$$

Where $y_i$ is the measured value, $\hat{y_i}$ is the predicted value

and $n$ is the number of observations.

The root mean square error (RMSE) is very similar to the MAE. Instead of

using absolute errors, it uses squared errors, and the mean of the

squared errors is then rooted as follows:

$$

\mathbf{Eqn~7}~~~~~~RMSE = \sqrt{\frac{1}{n} \sum_{i = 1}^{n}{({y_i-\hat{y_i}})^2}}

$$

Because of the squared errors, RMSE is sensitive to outliers. Indeed, a

few large errors will have a significant impact on the RMSE. Therefore,

RMSE will always be larger than or equal to MAE ([Pontius et al.,

2008](https://doi.org/10.1007/s10651-007-0043-y)).

Mathematically, RMSE is equivalent to the standard deviation of the

residuals. Indeed, for a constant gas concentration, the standard

deviation is expressed as follows:

$$

\mathbf{Eqn~8}~~~~~~\sigma = \sqrt{\frac{1}{N} \sum_{i = 1}^{N}{({x_i-\mu})^2}}

$$

Where $x_i$ is the measured value, $N$ is the size of the population and

$\mu$ is the population mean. The standard deviation is used to

calculate the precision of an instrument. In that case, $\mu$ is a known

constant gas concentration and $N$ is the number of observations.

Considering all of the above, MAE, RMSE and the standard deviation are

all measures of how much the data points are scattered around the true

mean or the regression. Therefore, MAE and RMSE can be compared to the

instrument precision to determine if a measurement is noisy. For a MAE

or RMSE larger than the instrument precision, the measurement is

considered to have more noise than normally expected.

If MAE is chosen as a criteria in `best.flux`, the model with the

smallest MAE is chosen. If both models have a MAE smaller than the

instrument precision, the non-linear flux estimate is always chosen by

default, as non-linearity is assumed. When MAE is larger than the

instrument precision, a warning is given in the columns `quality.check`,

`LM.diagnose` or `HM.diagnose` to warn of a noisy measurement. RMSE

functions exactly the same was as MAE in the `best.flux` function.

##### **Standard Error**

While the standard deviation describes how the data points are scattered

around the true mean, the standard error of a measurement tells how

accurate that measurement is compared to the true population mean

([Altman and Bland, 2005](https://doi.org/10.1136%2Fbmj.331.7521.903)).

If considering the standard deviation as used to calculate instrument

precision (equation 8), the instrument standard error (instrument

accuracy) can be defined as the standard deviation divided by the square

root of the number of observations:

$$\mathbf{Eqn~9}~~~~~~\sigma_\bar{x} = \frac{\sigma}{\sqrt{n}}$$

Practically, this means that the accuracy increases with the sample size

of a measurement. In other words, if an instrument is imprecise and the

measurement has a lot of noise, it is still possible to get a more

accurate estimate of the true mean by increasing the number of

observations. With high-frequency GHG analyzers, that means increasing

the chamber closure time.

To calculate the standard error of a regression, one can use the delta

method (`deltamethod` from the `msm` package), which propagates the

total error of the flux calculation for each parameter included in the

formula. The delta method approximates the standard error of a

regression $g(X)$ of a random variable $X = (x_1, x_2, ...)$, given

estimates of the mean and covariance matrix of $X$ ([Oehlert,

1992](https://doi.org/10.2307/2684406); [Mandel,

2013](https://doi.org/10.1080/00031305.2013.783880)).

In the function `best.flux`, the standard error (SE) of the measurements

can be compared to the standard error of the instrument

($\sigma_\bar{x}$). If SE is chosen as a criteria in `best.flux`, the

model with the smallest SE is chosen. If both models have a SE smaller

than the instrument precision, the non-linear flux estimate is always

chosen by default, as non-linearity is assumed. When SE is larger than

the instrument accuracy ($\sigma_\bar{x}$), a warning is given in the

columns `quality.check`, `LM.diagnose` or `HM.diagnose` to warn of a

noisy measurement.

##### **Akaike Information Criterion corrected for small sample size (AICc)**

The AIC estimates the relative quality of a statistical model and is

used to compare the fitting of different models to a set of data

([Akaike, 1974](https://doi.org/10.1109/TAC.1974.1100705)). Consider the

formula for AIC:

$$\mathbf{Eqn~10}~~~~~~AIC = 2k - 2ln(\hat{L})$$

Where $k$ is the number of parameters in the model and $\hat{L}$ is the

maximized value of the likelihood function for the model. AIC deals with

the trade-off between the goodness of fit of a model and the simplicity

of the model. In other words, the AIC is a score that deals with both

the risk of underfitting and the risk of overfitting, and the model with

the lowest score has the best model fit.

In flux calculation, the linear model contains two parameters: the slope

and the intercept ($C_0$), whereas the Hutchinson and Mosier model

(equation 1) contains three parameters: the assumed concentration of

constant gas source below the surface ($\varphi$), the concentration in

the chamber at the moment of chamber closure ($C_0$) and the curvature,

kappa ($\kappa$). If both models have a very similar fit (maximum

likelihood), then the linear model would win because it has less

parameters. However, when the sample size is small (\<40 observations

per parameter; i.e. \<120 observations when calculating HM), there is an

increased risk that AIC selects a model with too many parameters. To

address this risk of overfitting, AICc was developed ([Sugiura,

1978](https://doi.org/10.1080/03610927808827599)):

$$\mathbf{Eqn~11}~~~~~~AICc = AIC + \frac{2k^2 + 2k}{n - k - 1}$$

Where $n$ denotes the number of observations and $k$ the number of

parameters in the model.

If AICc is selected as a criteria in the `best.flux` function, the model

with the lowest AICc wins.

#### **Intercept**

If the initial gas concentration (*C₀*) calculated for the

flux estimates (`HM.C0` and `LM.C0`) deviates from the true

*C₀* (measured concentration at $t = 0$) by more or less than

10% of the difference between *C₀* and the final gas

concentration at the end of the measurement (*C_t*), a warning

is issued in the columns `quality.check`, `LM.diagnose` or `HM.diagnose`

to warn of an intercept out of bounds. Alternatively, one can provide

boundaries for the intercept, for example: `intercept.lim = c(380, 420)`

for a true *C₀* of 400 ppm.

#### **Statistically significant flux (*p-value*)**

This criteria is only applicable to the linear flux. Under a defined

*p-value*, the linear flux estimate is deemed non-significant, i. e.,

flux under the detectable limit. The default threshold is

`p.val = 0.05`. No best flux estimate is chosen based on *p-value*. If

`LM.p.val < p.val`, a warning is given in the columns `quality.check`

and `LM.diagnose` to warn of an estimated flux under the detection

limit.

#### **Number of observations**

Limit under which a measurement is flagged for being too short

(`nb.obs < warn.length`). With nowadays’ portable greenhouse gas

analyzers, the frequency of measurement is usually one observation per

second. Therefore, for the default setting of `warn.length = 60`, the

chamber closure time should be approximately one minute (60 seconds). If

the number of observations is smaller than the threshold, a warning is

issued in the column `quality.check`.

### Visual inspection

Finally, after finding the best flux estimates, one can plot the results

and visually inspect the measurements using the function `flux.plot` and

save the plots as pdf using `flux2pdf`.

Find out more about these functions

[here](https://qepanna.quarto.pub/goflux/flux2pdf.html).

## Installation

To install a package from GitHub, one can use the package `devtools` (or

`remotes`) from the CRAN:

``` r

if (!require("devtools", quietly = TRUE)) install.packages("devtools")

```

Then, install the `goFlux` package from GitHub. If it is not the first

time you install the package, it must first be detached before being

updated.

> The package is constantly being updated with new functions or

> de-bugging. To make sure that you are using the latest version,

> re-install the package every time you use it.

``` r

try(detach("package:goFlux", unload = TRUE), silent = TRUE)

devtools::install_github("Qepanna/goFlux")

```

**If prompted, it is recommended to update any pre-installed packages.**

The functioning of the package depends on many other packages

(`AICcmodavg`, `data.table`, `dplyr`, `ggnewscale`, `ggplot2`, `ggstar`,

`graphics`, `grDevices`, `grid`, `gridExtra`, `lubridate`, `minpack.lm`,

`msm`, `pbapply`, `plyr`, `purrr`, `rjson`, `rlist`, `SimDesign`,

`stats`, `stringr`, `tibble`, `tidyr`, `utils`), which will be installed

when installing `goFlux`.

### Troubleshoot problems with `install_github`

#### Warning: package is in use and will not be installed

If you get this warning while trying to install an R package from

GitHub:

    ## Warning: package ‘goFlux’ is in use and will not be installed

This error means that the package is loaded. Before re-installing the

package, you must first detach it:

``` r

detach("package:goFlux", unload = TRUE)

```

If this does not solve the problem, restart your session and try again.

------------------------------------------------------------------------

#### Error: API rate limit exceeded

If you get this error while trying to install an R package from GitHub:

    ## Error: Failed to install 'goFlux' from GitHub:

    ##   HTTP error 403.

    ##   API rate limit exceeded for xxx.xxx.xxx.x (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.)

    ## 

    ##   Rate limit remaining: 0/60

    ##   Rate limit reset at: 2023-10-16 09:08:07 UTC

    ## 

    ##   To increase your GitHub API rate limit

    ##   - Use `usethis::create_github_token()` to create a Personal Access Token.

    ##   - Use `usethis::edit_r_environ()` and add the token as `GITHUB_PAT`.

This error can occur while using the package `remotes` to install an R

package from GitHub. Try using the package `devtools` instead. If the

error still occurs, follow the instructions below.

##### Step 1: Set up a GitHub account

**Go to  .**

1.  Type a user name, your email address, and a password.

2.  Choose Sign up for GitHub, and then follow the instructions.

##### Step 2: Create a GitHub token

Run in R:

``` r

usethis::create_github_token()

```

On pop-up website, generate and copy your token.

##### Step 3: Set your GitHub PAT from R

Run in R with your own token generated in step 2:

``` r

credentials::set_github_pat("YourTokeninStep2")

```

## Community Guidelines

Authors: Karelle Rheault and Klaus Steenberg Larsen

The package is ready to use and fully functional, but errors may still

occur. To report any issues, suggest improvements or ask for new

features, [open an issue on

GitHub](https://github.com/Qepanna/goFlux/issues). Alternatively,

contact directly the maintainer, Karelle Rheault (),

including script and raw data if necessary. Thank you for helping me in

the development of this tool! 🙏

## How to cite

Please cite this R package using this publication in JOSS:  

  

Rheault et al., (2024). goFlux: A user-friendly way to calculate GHG

fluxes yourself, regardless of user experience. Journal of Open Source

Software, 9(96), 6393, 

## Acknowledgements

This software development was supported by the SilvaNova project funded

by the NOVO Nordisk Foundation (grant no. NNF20OC0059948).

[](https://ign.ku.dk/english/silvanova/)     [](https://novonordiskfonden.dk/en/)