croplotr

A package to analyze crop models outputs

https://github.com/sticsrpacks/croplotr

Repository

A package to analyze crop models outputs

README.Rmd

---
output: github_document
editor_options: 
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  markdown: 
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---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
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```

# CroPlotR



[![Project Status: WIP – Initial development is in progress, but there
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coverage](https://codecov.io/gh/SticsRPacks/CroPlotR/branch/master/graph/badge.svg)](https://app.codecov.io/gh/SticsRPacks/CroPlotR?branch=master)
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[![DOI](https://zenodo.org/badge/263962392.svg)](https://zenodo.org/badge/latestdoi/263962392)



`CroPlotR` aims at the standardization of the process of analyzing the
outputs from crop models such as
[STICS](https://www6.paca.inrae.fr/stics_eng/),
[APSIM](https://www.apsim.info/) or really any model.

Its use does not need any particular adaptation if your model has been
wrapped with the [CroptimizR](https://github.com/SticsRPacks/CroptimizR)
package.

If you want to be notified when a new release of this package is made,
you can tick the Releases box in the "Watch / Unwatch =\> Custom" menu
at the top right of [this
page](https://github.com/SticsRPacks/CroPlotR).

## Table of Contents

-   [1. Installation](#1-installation)
-   [2. Examples](#2-examples)
    -   [2.1 Plotting](#21-plotting)
        -   [2.1.1 Dynamic plots](#211-dynamic-plots)
        -   [2.1.2 Scatter plots](#212-scatter-plots)
        -   [2.1.3 Group comparison](#213-group-comparison)
        -   [2.1.4 Plot saving](#214-plot-saving)
        -   [2.1.5 Plot extracting](#215-plot-extracting)
    -   [2.2 Statistics](#22-statistics)
        -   [2.2.1 Dynamic plots](#221-simple-case)
        -   [2.2.2 Several groups](#222-several-groups)
        -   [2.2.3 Statistics plot](#223-statistics-plot)
    -   [2.3 Data manipulation](#23-data-manipulation)
-   [3. Tools](#3-tools)
    -   [3.1 ggplotly](#31-ggplotly)
    -   [3.2 patchwork](#32-patchwork)
-   [4. Help](#4-help)
-   [5. Citation](#5-Citation)

## 1. Installation

You can install the released version of CroPlotR from
[Github](https://github.com/SticsRPacks/CroPlotR) either using
`devtools` or the lightweight `remotes` package:

-   With `devtools`

```{r eval=FALSE}
devtools::install_github("SticsRPacks/CroPlotR@*release")
```

-   With `remotes`

```{r eval=FALSE}
# install.packages("remotes")
remotes::install_github("SticsRPacks/CroPlotR@*release")
```

Normally, all the package dependencies will be installed for CRAN
packages.

## 2. Examples

At the moment, only one function is exported for plots
[`plot()`](https://sticsrpacks.github.io/CroPlotR/reference/plot.cropr_simulation.html)
(and its alias `autoplot()`), and one for the statistics
[`summary()`](https://sticsrpacks.github.io/CroPlotR/reference/summary.cropr_simulation.html).
These functions should be the only one you need for all your plots and
summary statistics. Additional ones are provided to simplify the
manipulation of simulated data (see [2.3 Data
manipulation](#23-data-manipulation)).

In the following, an example using the STICS crop model is presented. If
you want to use another model for which a wrapper has been designed for
the [CroptimizR](https://github.com/SticsRPacks/CroptimizR) package,
just consider defining the `sim` variable used in the examples below as
`sim <- result$sim_list`, where `result` is the list returned by your
model wrapper. Examples of use of CroPlotR with Stics and APSIM model
wrappers can be found in [CroptimizR's
website](https://sticsrpacks.github.io/CroptimizR/) (see Articles tab).

In the following example a simulation of three situations (called USM in
STICS) with their observations is used:

-   an intercrop of Wheat and pea
-   a Pea in sole crop
-   a Wheat in sole crop

Let's import the simulation and observation data:

```{r}
library(CroPlotR)

# Importing an example with three situations with observation:
workspace <- system.file(
  file.path("extdata", "stics_example_1"),
  package = "CroPlotR"
)

situations <- SticsRFiles::get_usms_list(
  file = file.path(workspace, "usms.xml")
)

sim <- SticsRFiles::get_sim(
  workspace = workspace,
  usms_file = file.path(workspace, "usms.xml")
)

obs <- SticsRFiles::get_obs(
  workspace = workspace,
  usm = situations,
  usms_file = file.path(workspace, "usms.xml")
)
```

### 2.1 Plotting

#### 2.1.1 Dynamic plots

Here is an application of dynamic plots for the 3 situations:

```{r}
p <- plot(sim, obs = obs)
```

Note that the `obs` argument is explicitly named. This is because the
first argument of the function is `...` (we'll see why in a minute).

The plot function returns a named list of ggplot objects.

To plot all of them, just do

```{r}
p
```

or simply

```{r, eval=FALSE}
plot(sim, obs = obs)
```

In this case, the elements of the list take the name of the situations.

```{r}
names(p)
```

To plot only one of the graph, access it using its name:

```{r}
p$`IC_Wheat_Pea_2005-2006_N0`
```

or index:

```{r, eval=FALSE}
p[[1]]
```

It is possible to aggregate plots of multiple situations on the same
graph when situations follow one another over time. This can be done
using the `successive` parameter.

```{r}
workspace <- system.file(
  file.path("extdata", "stics_example_successive"),
  package = "CroPlotR"
)

situations <- SticsRFiles::get_usms_list(
  file = file.path(workspace, "usms.xml")
)

sim_rot <- SticsRFiles::get_sim(
  workspace = workspace,
  usm = situations,
  usms_file = file.path(workspace, "usms.xml")
)

plot(
  sim_rot,
  var = c("resmes", "masec_n"),
  successive = list(list("demo_Wheat1", "demo_BareSoil2", "demo_maize3"))
)
```

We can also overlay variables thanks to the "overlap" parameter with
dynamic plots.

```{r}
plot(sim, obs = obs, overlap = list(list("lai_n", "masec_n")))
```

> Note that it is not possible to scale the variables right now from the
> plot function (see
> [issue](https://github.com/SticsRPacks/CroPlotR/issues/2)). If you
> want to do so, you are encouraged to scale before the plotting
> function, and to add a second axis using
> [sec_axis](https://ggplot2.tidyverse.org/reference/sec_axis.html) on
> the resulting plot.

#### 2.1.2 Scatter plots

Here are the same plots, but presented as scatter plots:

```{r}
# Only plotting the first situation for this one:
plots <- plot(sim, obs = obs, type = "scatter", all_situations = FALSE)
plots$`IC_Wheat_Pea_2005-2006_N0`
```

Residues can also be represented against observations:

```{r}
# Only plotting the first situation again:
plots <- plot(
  sim,
  obs = obs,
  type = "scatter",
  select_scat = "res",
  all_situations = FALSE
)

plots[[1]]
```

All these data can also be represented with a single graph for all
situations:

```{r}
plot(sim, obs = obs, type = "scatter", all_situations = TRUE)
```

When plotting residual scatter plots, `reference_var` allows to choose
the reference variable on the x-axis. Thus, the observations or
simulations of this reference variable (to be chosen by suffixing the
variable name by "\_obs" or "\_sim") will be compared to the residuals
of each of the variables.

```{r}
plot(
  sim,
  obs = obs,
  type = "scatter",
  select_scat = "res",
  all_situations = TRUE,
  reference_var = "lai_n_sim"
)
```

The points on the graphs can be shown in different shapes to
differentiate between situations when `all_situations = TRUE`. If
desired, the names of the situations can be displayed.

```{r}
plot(
  sim,
  obs = obs[c(2, 3)],
  type = "scatter",
  all_situations = TRUE,
  shape_sit = "txt"
)
```

As you can see, this can quickly become unreadable depending on the
number of points and length of situation names; That is why you can
simply assign a different symbol to each situation.

```{r}
plot(
  sim,
  obs = obs,
  type = "scatter",
  all_situations = TRUE,
  shape_sit = "symbol"
)
```

It is also possible to represent a group of situations with the same
symbol when, for example, clusters are identified.

```{r}
plot(
  sim,
  obs = obs,
  type = "scatter",
  all_situations = TRUE,
  shape_sit = "group",
  situation_group = list(list("SC_Pea_2005-2006_N0", "SC_Wheat_2005-2006_N0"))
)
```

You can also name your `situation_group` list and thus customize (e.g
shorten) the plot legend.

```{r}
plot(
  sim,
  obs = obs,
  type = "scatter",
  all_situations = TRUE,
  shape_sit = "group",
  situation_group = list(
    "Two Single Crops" = list("SC_Pea_2005-2006_N0", "SC_Wheat_2005-2006_N0")
  )
)
```

By default, all variables are returned by `plot()`, but you can filter
them using the `var` argument:

```{r}
plot(sim, obs = obs, type = "scatter", all_situations = TRUE, var = c("lai_n"))
```

Error bars related to observations can also be added to the graph using
the `obs_sd` parameter which must be of the same shape as `obs`. In our
example, we will create a false data frame with the only purpose of
having a preview of the result. To have 95% confidence, the error bar is
equal to two standard deviations on each side of the point.

```{r}
obs_sd <- obs
names_obs <- names(obs_sd$`SC_Pea_2005-2006_N0`)
obs_sd$`SC_Pea_2005-2006_N0`[, !(names_obs %in% c("Date", "Plant"))] <-
  0.05 * obs_sd$`SC_Pea_2005-2006_N0`[, !(names_obs %in% c("Date", "Plant"))]
obs_sd$`SC_Wheat_2005-2006_N0`[, !(names_obs %in% c("Date", "Plant"))] <-
  0.2 * obs_sd$`SC_Wheat_2005-2006_N0`[, !(names_obs %in% c("Date", "Plant"))]

plot(sim, obs = obs, obs_sd = obs_sd, type = "scatter", all_situations = TRUE)
```

#### 2.1.3 Group comparison

We can compare groups of simulations alongside by simply adding the
simulations objects one after the other (that is why the first argument
of the function is `...`). Group simulations can be the results of
simulations from different model versions, or simulations with different
parameter values.

```{r}
workspace2 <- system.file(
  file.path("extdata", "stics_example_2"),
  package = "CroPlotR"
)

sim2 <- SticsRFiles::get_sim(
  workspace = workspace2,
  usms_file = file.path(workspace2, "usms.xml")
)

plot(sim, sim2, obs = obs, all_situations = FALSE)
```

Here only one plot is outputted because `workspace2` only contains the
intercrop situation.

We can also name the corresponding group in the plot by naming them
while passing to the `plot()` function:

```{r}
plot(
  "New version" = sim,
  original = sim2,
  obs = obs,
  type = "scatter",
  all_situations = FALSE
)
```

#### 2.1.4 Plot saving

The plots can be saved to disk using the `save_plot_png()` function as
follows:

```{r eval=FALSE}
plots <- plot("New version" = sim, original = sim2, obs = obs, type = "scatter")

save_plot_png(plot = plots, out_dir = "path/to/directory", suffix = "_scatter")

# or by piping:
plots <- plot(
  "New version" = sim,
  original = sim2,
  obs = obs,
  type = "scatter"
) %>%
  save_plot_png(., out_dir = "path/to/directory", suffix = "_scatter")
```

They can also be saved using the `save_plot_pdf()` function that which,
from a list of ggplots, generates a pdf file. If the `file_per_var`
parameter is TRUE, in this case the function generates one pdf file per
variable.

```{r eval=FALSE}
plots <- plot(sim, obs = obs)

save_plot_pdf(plot = plots, out_dir = "path/to/directory", file_per_var = FALSE)
```

#### 2.1.5 Plot extracting

When we have plots with several variables and several situations, the
`extract_plot` function allows to keep the situations and variables that
we need.

In the following example, we want to extract the intercrop situation and
the "masec_n" variable.

```{r}
plots <- plot(sim, obs = obs, type = "scatter", all_situations = FALSE)

extract_plot(
  plots,
  situation = c("IC_Wheat_Pea_2005-2006_N0"), var = c("masec_n")
)
```

### 2.2 Statistics

#### 2.2.1 Simple case

Here is an application of summary statistics for the 3 situations:

```{r eval=FALSE}
summary(sim, obs = obs, all_situations = FALSE)
```

```{r echo=FALSE}
s <- summary(sim, obs = obs, all_situations = FALSE)
knitr::kable(s)
```

Note that as for the `plot()` function the `obs` argument is explicitly
named. This is because the first argument of the function is `...` to be
able to compare groups (i.e. model versions or simulation with different
parameter values). In this example, a message warns the user because
some observed values have a zero value which causes a division by zero
in the calculation of certain statistical criteria, these values are
therefore filtered for the calculation of these criteria.

And as for the `plot()` function again, it is possible to compute the
statistical criteria for all situations at once.

```{r eval=FALSE}
summary(sim, obs = obs, all_situations = TRUE)
```

```{r echo=FALSE}
s <- summary(sim, obs = obs, all_situations = TRUE)
knitr::kable(s)
```

#### 2.2.2 Several groups

We can get statistics for each group of simulations by simply adding the
simulations objects one after the other (as for the `plot()` function).

```{r eval=FALSE}
summary(sim, sim2, obs = obs)
```

```{r echo=FALSE}
s <- summary(sim, sim2, obs = obs)
knitr::kable(s)
```

We can also name the corresponding group in the plot by naming them
while passing to the `summary()` function:

```{r eval=FALSE}
summary("New version" = sim, original = sim2, obs = obs)
```

```{r echo=FALSE}
s <- summary("New version" = sim, original = sim2, obs = obs)
knitr::kable(s)
```

By default, all statistics are returned by `summary`, but you can filter
them using the `stat` argument:

```{r eval=FALSE}
summary(
  "New version" = sim, original = sim2, obs = obs,
  stats = c("R2", "nRMSE")
)
```

```{r echo=FALSE}
s <- summary(
  "New version" = sim,
  original = sim2,
  obs = obs,
  stats = c("R2", "nRMSE")
)

knitr::kable(s)
```

Please read the help from
[`summary.cropr_simulation()`](https://sticsrpacks.github.io/CroPlotR/reference/summary.cropr_simulation.html)
and
[`predictor_assessment()`](https://sticsrpacks.github.io/CroPlotR/reference/predictor_assessment.html).

#### 2.2.3 Statistics plot

It is also possible to plot the statistics:

In a rather obvious way, the resulting graph will take into account all
the situations simultaneously or not according to the parameter given to
`summary`. Here is an example with `all_situations = FALSE`.

```{r}
stats <- summary(
  "New version" = sim,
  original = sim2,
  obs = obs,
  stats = c("R2", "nRMSE"),
  all_situations = FALSE
)
plot(stats)
```

And here is an example with `all_situations = TRUE`.

```{r}
stats <- summary(
  "New version" = sim,
  original = sim2,
  obs = obs,
  stats = c("R2", "nRMSE"),
  all_situations = TRUE
)

plot(stats)
```

We can choose to plot either the group or the situation in x (and the
other is used for grouping and colouring):

```{r}
stats <- summary(
  "New version" = sim,
  original = sim2,
  obs = obs,
  stats = c("R2", "nRMSE"),
  all_situations = FALSE
)

plot(stats, xvar = "situation", title = "Situation in X")
```

In the previous examples, each line corresponds to a statistical
criterion. These can also be stacked.

```{r}
stats <- summary(
  "New version" = sim,
  original = sim2,
  obs = obs,
  stats = c("pMSEs", "pMSEu"),
  all_situations = FALSE
)

plot(stats, xvar = "situation", title = "Stacked columns", group_bar = "stack")
```

Or put side by side.

```{r}
stats <- summary(
  "New version" = sim,
  original = sim2,
  obs = obs,
  stats = c("pMSEs", "pMSEu"),
  all_situations = FALSE
)

plot(
  stats,
  xvar = "situation",
  title = "Side-by-side columns",
  group_bar = "dodge"
)
```

To compare different versions on a single criterion, the function
produces a radar graph like the following one.

```{r}
sim$`SC_Pea_2005-2006_N0`$mafruit <-
  (15 / 10) * sim$`SC_Pea_2005-2006_N0`$masec_n
sim$`SC_Wheat_2005-2006_N0`$mafruit <-
  (15 / 20) * sim$`SC_Wheat_2005-2006_N0`$masec_n
sim2$`IC_Wheat_Pea_2005-2006_N0`$mafruit <-
  sim2$`IC_Wheat_Pea_2005-2006_N0`$masec_n
obs$`IC_Wheat_Pea_2005-2006_N0`$mafruit <-
  (12 / 10) * obs$`IC_Wheat_Pea_2005-2006_N0`$masec_n
obs$`SC_Pea_2005-2006_N0`$mafruit <-
  (18 / 10) * obs$`SC_Pea_2005-2006_N0`$masec_n
obs$`SC_Wheat_2005-2006_N0`$mafruit <-
  (15 / 12) * obs$`SC_Wheat_2005-2006_N0`$masec_n

stats <- summary(
  "New version" = sim,
  original = sim2,
  obs = obs,
  stats = c("R2", "nRMSE"),
  all_situations = TRUE
)

plot(
  stats,
  type = "radar",
  crit_radar = "nRMSE",
  title = "Radar chart : nRMSE"
)
```

### 2.3 Data manipulation

Observation lists can easily be handled using e.g.
[dplyr](https://CRAN.R-project.org/package=dplyr),
[tidyr](https://CRAN.R-project.org/package=tidyr) or
[tibble](https://CRAN.R-project.org/package=tibble) packages.

The use of these packages on simulated data as returned by CroptimizR
model wrappers is sometimes prevented by their attribute
`cropr_simulation`. To easily manipulate simulated data we thus provide
two functions for (i) binding rows of data simulated on different
situations in a single data.frame or tibble and (ii) go back to the
original (cropr) format by splitting this single data.frame or tibble.

```{r}
df <- bind_rows(sim)
head(df)
```

The resulting data.frame/tibble can then easily be manipulated using
standard R packages. The column `situation` contains the name of the
corresponding situation (as given in the named list `sim`).

To go back to the original format of simulated data handled by CroPlotR,
use the `split_df2sim` function:

```{r}
sim_new <- split_df2sim(df)
lapply(sim_new, head)
```

## 3. Tools

### 3.1 ggplotly

The ggplotly function in plotly library makes it very easy to create
interactive graphics from a ggplot. Do not hesitate to call it with your
plot and move your mouse over the graph to discover the features of this
function.

```{r, eval = FALSE}
library(plotly)

ggplotly(plot(sim, obs = obs, type = "dynamic")[[1]])
```

### 3.2 patchwork

There is also the patchwork library that allows you to easily combine
several ggplot into one.

```{r}
library(patchwork)

plot1 <- plot(sim, obs = obs, type = "scatter", var = "lai_n")[[1]]
plot2 <- plot(sim, obs = obs, var = "lai_n")[[1]]
plot3 <- plot(sim, obs = obs, type = "scatter", var = "masec_n")[[1]]
plot4 <- plot(sim, obs = obs, var = "masec_n")[[1]]

plot1 + plot2 + plot3 + plot4 + plot_layout(ncol = 2)
```

## 4. Help

You can find help for the functions directly using the name of the
function followed by the class of the object you need the method for:

-   plot:

```{r eval=FALSE}
?plot.cropr_simulation

?plot.statistics
```

-   statistics:

```{r eval=FALSE}
?summary.cropr_simulation
```

If you have any problem, please [fill an
issue](https://github.com/SticsRPacks/CroPlotR/issues) on Github.

## 5. Citation

If you have used this package for a study that led to a publication or
report, please cite us. You can either use the citation tool from Github
if you used the last version, or use `citation("CroPlotR")` from R
otherwise.

Owner

Citation (CITATION.cff)

# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "CroPlotR" in publications use:'
type: software
title: 'CroPlotR: A Package to Analyze Crop Model Simulations Outputs with Plots and
  Statistics'
version: 0.10.0
abstract: CroplotR aims at the standardization of the process of analyzing the outputs
  of crop models using plots and statistics. Users can generate plots presented in
  dynamic mode with time on the x axis and any simulated and/or observed variable(s)
  on the y axis or in scatter mode with simulation on the y axis and observations
  on the x axis. Users can differentiate simulations and observations depending on
  the situation, model version, or any grouping variable. Thirty two corresponding
  statistics can be computed in the same manner to assess the quality of the predictions
  of a model.
authors:
- family-names: Vezy
  given-names: Remi
  email: remi.vezy@cirad.fr
  orcid: https://orcid.org/0000-0002-0808-1461
- family-names: Buis
  given-names: Samuel
  email: samuel.buis@inrae.fr
  orcid: https://orcid.org/0000-0002-8676-5447
- family-names: Midingoyi
  given-names: Cyrille
  email: cyrille_ahmed.midingoyi@cirad.fr
  orcid: https://orcid.org/0009-0000-4411-7989
- family-names: Lecharpentier
  given-names: Patrice
  email: patrice.lecharpentier@inrae.fr
  orcid: https://orcid.org/0000-0002-4044-4322
- family-names: Giner
  given-names: Michel
  email: michel.giner@cirad.fr
  orcid: https://orcid.org/0000-0002-9310-2377
repository-code: https://github.com/SticsRPacks/CroPlotR
url: https://doi.org/10.5281/zenodo.4442330
date-released: '2024-03-28'
contact:
- family-names: Vezy
  given-names: Remi
  email: remi.vezy@cirad.fr
  orcid: https://orcid.org/0000-0002-0808-1461
references:
- type: software
  title: 'R: A Language and Environment for Statistical Computing'
  notes: Depends
  url: https://www.R-project.org/
  authors:
  - name: R Core Team
  institution:
    name: R Foundation for Statistical Computing
    address: Vienna, Austria
  year: '2024'
  version: '>= 4.0.0'
- type: software
  title: cli
  abstract: 'cli: Helpers for Developing Command Line Interfaces'
  notes: Imports
  url: https://cli.r-lib.org
  repository: https://CRAN.R-project.org/package=cli
  authors:
  - family-names: Csárdi
    given-names: Gábor
    email: csardi.gabor@gmail.com
  year: '2024'
- type: software
  title: crayon
  abstract: 'crayon: Colored Terminal Output'
  notes: Imports
  url: https://github.com/r-lib/crayon#readme
  repository: https://CRAN.R-project.org/package=crayon
  authors:
  - family-names: Csárdi
    given-names: Gábor
    email: csardi.gabor@gmail.com
  year: '2024'
- type: software
  title: dplyr
  abstract: 'dplyr: A Grammar of Data Manipulation'
  notes: Imports
  url: https://dplyr.tidyverse.org
  repository: https://CRAN.R-project.org/package=dplyr
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
    orcid: https://orcid.org/0000-0003-4757-117X
  - family-names: François
    given-names: Romain
    orcid: https://orcid.org/0000-0002-2444-4226
  - family-names: Henry
    given-names: Lionel
  - family-names: Müller
    given-names: Kirill
    orcid: https://orcid.org/0000-0002-1416-3412
  - family-names: Vaughan
    given-names: Davis
    email: davis@posit.co
    orcid: https://orcid.org/0000-0003-4777-038X
  year: '2024'
- type: software
  title: ggplot2
  abstract: 'ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics'
  notes: Imports
  url: https://ggplot2.tidyverse.org
  repository: https://CRAN.R-project.org/package=ggplot2
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
    orcid: https://orcid.org/0000-0003-4757-117X
  - family-names: Chang
    given-names: Winston
    orcid: https://orcid.org/0000-0002-1576-2126
  - family-names: Henry
    given-names: Lionel
  - family-names: Pedersen
    given-names: Thomas Lin
    email: thomas.pedersen@posit.co
    orcid: https://orcid.org/0000-0002-5147-4711
  - family-names: Takahashi
    given-names: Kohske
  - family-names: Wilke
    given-names: Claus
    orcid: https://orcid.org/0000-0002-7470-9261
  - family-names: Woo
    given-names: Kara
    orcid: https://orcid.org/0000-0002-5125-4188
  - family-names: Yutani
    given-names: Hiroaki
    orcid: https://orcid.org/0000-0002-3385-7233
  - family-names: Dunnington
    given-names: Dewey
    orcid: https://orcid.org/0000-0002-9415-4582
  - family-names: Brand
    given-names: Teun
    name-particle: van den
    orcid: https://orcid.org/0000-0002-9335-7468
  year: '2024'
- type: software
  title: ggrepel
  abstract: 'ggrepel: Automatically Position Non-Overlapping Text Labels with ''ggplot2'''
  notes: Imports
  url: https://ggrepel.slowkow.com/
  repository: https://CRAN.R-project.org/package=ggrepel
  authors:
  - family-names: Slowikowski
    given-names: Kamil
    email: kslowikowski@gmail.com
    orcid: https://orcid.org/0000-0002-2843-6370
  year: '2024'
- type: software
  title: gridExtra
  abstract: 'gridExtra: Miscellaneous Functions for "Grid" Graphics'
  notes: Imports
  repository: https://CRAN.R-project.org/package=gridExtra
  authors:
  - family-names: Auguie
    given-names: Baptiste
    email: baptiste.auguie@gmail.com
  year: '2024'
- type: software
  title: lifecycle
  abstract: 'lifecycle: Manage the Life Cycle of your Package Functions'
  notes: Imports
  url: https://lifecycle.r-lib.org/
  repository: https://CRAN.R-project.org/package=lifecycle
  authors:
  - family-names: Henry
    given-names: Lionel
    email: lionel@posit.co
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
    orcid: https://orcid.org/0000-0003-4757-117X
  year: '2024'
- type: software
  title: plyr
  abstract: 'plyr: Tools for Splitting, Applying and Combining Data'
  notes: Imports
  url: http://had.co.nz/plyr
  repository: https://CRAN.R-project.org/package=plyr
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@rstudio.com
  year: '2024'
- type: software
  title: reshape2
  abstract: 'reshape2: Flexibly Reshape Data: A Reboot of the Reshape Package'
  notes: Imports
  url: https://github.com/hadley/reshape
  repository: https://CRAN.R-project.org/package=reshape2
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: h.wickham@gmail.com
  year: '2024'
- type: software
  title: rlang
  abstract: 'rlang: Functions for Base Types and Core R and ''Tidyverse'' Features'
  notes: Imports
  url: https://rlang.r-lib.org
  repository: https://CRAN.R-project.org/package=rlang
  authors:
  - family-names: Henry
    given-names: Lionel
    email: lionel@posit.co
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  year: '2024'
- type: software
  title: rstudioapi
  abstract: 'rstudioapi: Safely Access the RStudio API'
  notes: Imports
  url: https://rstudio.github.io/rstudioapi/
  repository: https://CRAN.R-project.org/package=rstudioapi
  authors:
  - family-names: Ushey
    given-names: Kevin
    email: kevin@rstudio.com
  - family-names: Allaire
    given-names: JJ
    email: jj@posit.co
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  - family-names: Ritchie
    given-names: Gary
    email: gary@posit.co
  year: '2024'
- type: software
  title: stringr
  abstract: 'stringr: Simple, Consistent Wrappers for Common String Operations'
  notes: Imports
  url: https://stringr.tidyverse.org
  repository: https://CRAN.R-project.org/package=stringr
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  year: '2024'
- type: software
  title: tibble
  abstract: 'tibble: Simple Data Frames'
  notes: Imports
  url: https://tibble.tidyverse.org/
  repository: https://CRAN.R-project.org/package=tibble
  authors:
  - family-names: Müller
    given-names: Kirill
    email: kirill@cynkra.com
    orcid: https://orcid.org/0000-0002-1416-3412
  - family-names: Wickham
    given-names: Hadley
    email: hadley@rstudio.com
  year: '2024'
- type: software
  title: tidyselect
  abstract: 'tidyselect: Select from a Set of Strings'
  notes: Imports
  url: https://tidyselect.r-lib.org
  repository: https://CRAN.R-project.org/package=tidyselect
  authors:
  - family-names: Henry
    given-names: Lionel
    email: lionel@posit.co
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  year: '2024'
- type: software
  title: patchwork
  abstract: 'patchwork: The Composer of Plots'
  notes: Suggests
  url: https://patchwork.data-imaginist.com
  repository: https://CRAN.R-project.org/package=patchwork
  authors:
  - family-names: Pedersen
    given-names: Thomas Lin
    email: thomasp85@gmail.com
    orcid: https://orcid.org/0000-0002-5147-4711
  year: '2024'
- type: software
  title: spelling
  abstract: 'spelling: Tools for Spell Checking in R'
  notes: Suggests
  url: https://ropensci.r-universe.dev/spelling
  repository: https://CRAN.R-project.org/package=spelling
  authors:
  - family-names: Ooms
    given-names: Jeroen
    email: jeroen@berkeley.edu
    orcid: https://orcid.org/0000-0002-4035-0289
  - family-names: Hester
    given-names: Jim
    email: james.hester@rstudio.com
  year: '2024'
- type: software
  title: SticsRFiles
  abstract: 'SticsRFiles: Read and Modify ''STICS'' Input/Output Files'
  notes: Suggests
  url: https://doi.org/10.5281/zenodo.4443206
  repository: https://CRAN.R-project.org/package=SticsRFiles
  authors:
  - family-names: Lecharpentier
    given-names: Patrice
    email: patrice.lecharpentier@inrae.fr
    orcid: https://orcid.org/0000-0002-4044-4322
  - family-names: Vezy
    given-names: Remi
    email: remi.vezy@cirad.fr
    orcid: https://orcid.org/0000-0002-0808-1461
  - family-names: Buis
    given-names: Samuel
    email: samuel.buis@inrae.fr
    orcid: https://orcid.org/0000-0002-8676-5447
  - family-names: Giner
    given-names: Michel
    email: michel.giner@cirad.fr
    orcid: https://orcid.org/0000-0002-9310-2377
  year: '2024'
  version: '>= 1.1.3'
- type: software
  title: testthat
  abstract: 'testthat: Unit Testing for R'
  notes: Suggests
  url: https://testthat.r-lib.org
  repository: https://CRAN.R-project.org/package=testthat
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  year: '2024'
- type: software
  title: vdiffr
  abstract: 'vdiffr: Visual Regression Testing and Graphical Diffing'
  notes: Suggests
  url: https://vdiffr.r-lib.org/
  repository: https://CRAN.R-project.org/package=vdiffr
  authors:
  - family-names: Henry
    given-names: Lionel
    email: lionel@posit.co
  - family-names: Pedersen
    given-names: Thomas Lin
    email: thomas.pedersen@posit.co
    orcid: https://orcid.org/0000-0002-5147-4711
  - family-names: Luciani
    given-names: T Jake
    email: jake@apache.org
  - family-names: Decorde
    given-names: Matthieu
    email: matthieu.decorde@ens-lyon.fr
  - family-names: Lise
    given-names: Vaudor
    email: lise.vaudor@ens-lyon.fr
  year: '2024'

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