EXstat

<!-- badges: end -->

EXstat is a R package which provide an efficient and simple solution to aggregate and analyze the stationarity of time series.

EXstat is highly customizable, but the CARD extension provides a simpler solution for performing common hydroclimatic aggregations. See the CARD documentation of the CARD package for advance understanding.

This project was carried out for National Research Institute for Agriculture, Food and the Environment (Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, INRAE in french) and is at the core of MAKAHO which won the 2024 Open Science Research Data Award in the “Creating the Conditions for Reuse” category.

Installation

For latest development version R remotes::install_github("louis-heraut/EXstat")

Documentation

Extraction process

Based on dplyr, input data format is a tibble of at least a column of date, some columns of numeric value and one or more character columns for names of time series in order to identify them uniquely. Thus it is possible to have a tibble with multiple time series which can be grouped by their names.

For example, we can use the following tibble :

``` R library(dplyr)

Date

Start = as.Date("1972-01-01") End = as.Date("2020-12-31") Date = seq.Date(Start, End, by="day")

Value to analyse

set.seed(100) X = seq(1, length(Date))/1e4 + runif(length(Date), -100, 100) X[as.Date("2000-03-01") <= Date & Date <= as.Date("2000-09-30")] = NA

Creation of tibble

data = tibble(Date=Date, ID="serie A", X=X) ```

Which looks like that : ``` R

data

A tibble: 17,898 × 3

Date ID X 1 1972-01-01 serie A -38.4 2 1972-01-02 serie A -48.5 3 1972-01-03 serie A 10.5 4 1972-01-04 serie A -88.7 5 1972-01-05 serie A -6.29 6 1972-01-06 serie A -3.25 7 1972-01-07 serie A 62.5 8 1972-01-08 serie A -25.9 9 1972-01-09 serie A 9.31 10 1972-01-10 serie A -65.9

ℹ 17,888 more rows

ℹ Use print(n = ...) to see more rows

```

The process of variable extraction (for example the yearly mean of time series) is realised with the process_extraction() function. Minimum arguments are : * Input data described above * The function funct (for example mean) you want to use. Arguments of the chosen function can be passed to this extraction process and the function can be previously defined.

Some of the optional arguments are : * period A vector of two dates (or two unambiguous character strings that can be coerced to dates) to restrict the period of analysis. As an example, it can be c("1950-01-01", "2020-12-31") to select data from the 1st January of 1950 to the end of December of 2020. The default option is period=NULL, which considers all available data for each time serie. * time_step A character string specifying the time step of the variable extraction process. Possible values are : - "year" for a value per year - "month" for a value for each month of the year (so 12 values if at least a full year is given) - "year-month" for a value for each month of each year (so 12 times the number of given year values at the end) - "season" for a value for each season of th year (so by default 4 values) - "year-season" for a value for each season of each year (so by default 4 times the number of given year values at the end) - "yearday" for one value per day of the year (so 365 values at the end if at least a full year is given... but more than one year seems obviously more interesting) - "none" if you want to extract a unique value for the whole time serie * sampling_period A character string or a vector of two character strings that will indicate how to sample the data for each time step defined by time_step. Hence, the choice of this argument needs to be link with the choice of the time step. For example, for a yearly extraction so if time_step is set to "year", sampling_period needs to be formated as %m-%d (a month - a day of the year) in order to indicate the start of the sampling of data for the current year. More precisly, if time_step="year" and sampling_period="03-19", funct will be apply on every data from the 3rd march of each year to the 2nd march of the following one. In this way, it is possible to create a sub-year sampling with a vector of two character strings as sampling_period=c("02-01", "07-31") in order to process data only if the date is between the 1st february and the 31th jully of each year.

More parameters are available, for example, to : * handle missing values, * use suffixes to simplify expressions, and * manage variables related to seasonality.

In this way R dataEX = process_extraction(data=data, funct=max, funct_args=list("X", na.rm=TRUE), time_step="year", sampling_period=c("05-01", "11-30"), period=c(as.Date("1990-01-01"), as.Date("2020-12-31")))

will perform a yearly extraction of the maximum value between may and november, from the 1th march of 1990 to the 31th october of 2020, ignoring NA values.

The output is also a tibble with a column of date, of character for the name of time series and a numerical column with the extracted variable from the time series.

``` R

dataEX

A tibble: 31 × 3

ID Date X 1 serie A 1990-05-01 100. 2 serie A 1991-05-01 101. 3 serie A 1992-05-01 100. 4 serie A 1993-05-01 99.9 5 serie A 1994-05-01 99.0 6 serie A 1995-05-01 100. 7 serie A 1996-05-01 100. 8 serie A 1997-05-01 101. 9 serie A 1998-05-01 99.6 10 serie A 1999-05-01 101.

ℹ 21 more rows

ℹ Use print(n = ...) to see more rows

```

Other examples of more complex cases are available in the package documentation. Try starting with R library(EXstat) ?EXstat

Trend analyse

The stationarity analyse is computed with the process_trend() function on the extracted data dataEX. The statistical test used here is the Mann-Kendall test[^mann][^kendall].

Hence, the following expression

R trendEX = process_trend(data=dataEX)

produces the result below

```

A tibble: 1 × 12

ID variableen level H p a b periodtrend
1 serie A X 0.1 TRUE 0.0958 0.0260 99.3

meanperiodtrend anormalise anormalisemin anormalise_max 1 NA 0.0260 0.0260 0.0260 ```

It is a tibble which precises, among other information, the name of the time serie the p value and a the Theil-Sen's slope[^theil][^sen], for each row.

Finaly, as the p value is below 0.1, the previous time serie shows an increasing linear trend which can be represented by the equation Y = 0.0260*X + b with a type I error of 10 % or a trust of 90 %.

FAQ

📬 — I would like an upgrade / I have a question / Need to reach me
Feel free to open an issue ! I’m actively maintaining this project, so I’ll do my best to respond quickly.
I’m also reachable on my institutional INRAE email for more in-depth discussions.

🛠️ — I found a bug
- Good Solution : Search the existing issue list, and if no one has reported it, create a new issue !
- Better Solution : Along with the issue submission, provide a minimal reproducible code sample.
- Best Solution : Fix the issue and submit a pull request. This is the fastest way to get a bug fixed.

🚀 — Want to contribute ?
If you don't know where to start, open an issue.

If you want to try by yourself, why not start by also opening an issue to let me know you're working on something ? Then:

• Fork this repository
  
• Clone your fork locally and make changes (or even better, create a new branch for your modifications)
• Push to your fork and verify everything works as expected
• Open a Pull Request on GitHub and describe what you did and why
• Wait for review
• For future development, keep your fork updated using the GitHub “Sync fork” functionality or by pulling changes from the original repo (or even via remote upstream if you're comfortable with Git). Otherwise, feel free to delete your fork to keep things tidy !

If we’re connected through work, why not reach out via email to see if we can collaborate more closely on this repo by adding you as a collaborator !

Refer to the CONTRIBUTING file for contribution guidelines and help.

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

[^mann]: Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society, 245-259. [^kendall]: Kendall, M.G. (1975) Rank Correlation Methods. 4th Edition, Charles Grifin, London. [^theil]: Theil, H. (1950). A rank-invariant method of linear and polynomial regression analysis. Indagationes mathematicae, 12(85), 173. [^sen]: Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association, 63(324), 1379-1389.