Test Spanish translation via babeldown #95

Bisaloo · 2024-06-20T15:48:11Z

@avallecam, no need for an actual review but could you browse this quickly for a simple high-level check?

If it sounds good, I can:

pre-translate the other episodes via DeepL
in a follow up, edit our deployment workflow to try and deploy the English sites side by side.

For reference, to translate two episodes, I used almost 10% of my free monthly characters. Translating all lessons in a short amount of time may indeed require paying for DeepL, at least for 1 month.

Since babeldown is not really designed for the workbench, I had a couple of minor issue. I will coordinate with Maëlle in the coming months to see if we can streamline the process even more:

I had to remove the alt text

I had to remove the HTML from

tutorials-middle/episodes/quantify-transmissibility.Rmd

Lines 178 to 179 in dc783bc

    
           <p class="text-center" style="background-color: aliceblue">**time from infection to symptom onset** (the [incubation period](../learners/reference.md#incubation)) + **time from symptom onset to case notification** (the reporting time) 
        
           .</p>

Post-processing:

sed -i -r 's/(:+) llamada/\1 callout/g' *
sed -i -r 's/(:+) objetivos/\1 objectives/g' *
sed -i -r 's/(:+) preguntas/\1 questions/g' *
sed -i -r 's/(:+) prerrequisito/\1 prereq/g' *
sed -i -r 's/(:+) puntos clave/\1 keypoints/g' *
sed -i -r 's/(:+) desafío/\1 challenge/g' *
sed -i -r 's/(:+) lista de control/\1 checklist/g' *
sed -i -r 's/(:+) discusión/\1 discussion/g' *
sed -i -r 's/(:+) testimonio/\1 testimonial/g' *

github-actions · 2024-06-20T15:48:24Z

Thank you!

Thank you for your pull request 😃

🤖 This automated message can help you check the rendered files in your submission for clarity. If you have any questions, please feel free to open an issue in {sandpaper}.

If you have files that automatically render output (e.g. R Markdown), then you should check for the following:

🎯 correct output
🖼️ correct figures
❓ new warnings
‼️ new errors

Rendered Changes

🔍 Inspect the changes: https://github.com/epiverse-trace/tutorials-middle/compare/md-outputs..md-outputs-PR-95

The following changes were observed in the rendered markdown documents:

 config.yaml (new) |   86 ++
 renv.lock (new)   | 2419 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 2505 insertions(+)

What does this mean?

If you have source files that require output and figures to be generated (e.g. R Markdown), then it is important to make sure the generated figures and output are reproducible.

This output provides a way for you to inspect the output in a diff-friendly manner so that it's easy to see the changes that occur due to new software versions or randomisation.

⏱️ Updated at 2024-07-05 12:26:42 +0000

avallecam · 2024-06-21T09:46:08Z

this looks good! I briefly skim through them, and they look appropriate to review by a Spanish speaker.

One step after the DeepL translation I see is to recover the callout tags to English manually (e.g., questions, prerequisites here in spanish back to english)

For the DeepL assessment, I need to specify the exact four episodes to translate in this first batch:

delays-access https://github.com/epiverse-trace/tutorials-middle/blob/main/episodes/delays-access.Rmd
delays-functions https://github.com/epiverse-trace/tutorials-middle/blob/main/episodes/delays-functions.Rmd
quantify-transmissibility https://github.com/epiverse-trace/tutorials-middle/blob/main/episodes/quantify-transmissibility.Rmd
severity-static https://github.com/epiverse-trace/tutorials-middle/blob/main/episodes/severity-static.Rmd

Let us know if the characters in these four episodes overpass the limit in the amount of characters

with: sed -i -r 's/(:+) llamada/\1 callout/g' *

avallecam · 2024-06-21T11:27:50Z

All these pre-translations look appropriate. It is nice to see that it is possible to add the post-processing tags; they work nicely. Do you have the % of monthly characters used for all these?

Some next-step questions:

Translations are currently located in the folder locale/es/episodes. Do we expect them to live here?
Is this an appropriate PR to request a translation review?

Bisaloo · 2024-06-21T11:55:40Z

I have used 150k / 500k characters from my monthly quota. I used a bit more than I should have because of the small bugs mentioned in the first message, which required some debugging and trial and error.

Translations are currently located in the folder locale/es/episodes. Do we expect them to live here?

Probably yes at this time to facilitate deployment but it may change in the future. Especially as the Carpentries start standardizing their process, we will want to follow the choice they end up doing.

Is this an appropriate PR to request a translation review?

Yes, it's fine with me. Do what you think is best.

avallecam · 2024-06-21T12:19:29Z

Translations are currently located in the folder locale/es/episodes. Do we expect them to live here?

Probably yes at this time to facilitate deployment but it may change in the future. Especially as the Carpentries start standardizing their process, we will want to follow the choice they end up doing.

Sure, I agree. Some additional ones:

With the current path:

Do we have an example from the Carpentries of how this is currently visible online to the learner? like the website path to view the deployed translation online.
Have you tried to render these translations locally? To make any needed final tweaks. I'm now trying to run sandpaper::build_lesson() to see what happens.

avallecam · 2024-06-21T15:30:13Z

Have you tried to render these translations locally? To make any needed final tweaks. I'm now trying to run sandpaper::build_lesson() to see what happens.

Locally difficult to assess given long-time runs.

My current workflow would be to:

review these translations in this PR,
duplicate this repo to tutorials-middle-es,
relocate and list them in the config.yaml file for them to render.

Bisaloo · 2024-06-21T16:38:12Z

Ok, let's do this for now because I don't have availability to investigate more

avallecam · 2024-06-24T14:15:53Z

Dear reviewers, thank you for volunteering to review the automatic translation of one episode:

@lgbermeo for delays-access.Rmd
@GeraldineGomez for quantify-transmission.Rmd
@jd-otero for delays-functions.Rmd
@CarmenTamayo and @juan-umana for severity-static.Rmd

The translated files are accessible in the "Files changed" tab of this PR.

We are open to accepting your specific changes to one or multiple lines using the "Files changed" tab. To start your review, you can follow steps 5 to 8 from this how-to guide on Reviewing proposed changes in a pull request.

If you prefer an alternative method for review, please let me know, and we can coordinate another platform according to your needs. Each episode could be reviewed on different platforms.

Lastly, we mostly want your review of the Spanish translation of the text. If you have a thematic or technical improvement you would like the tutorials to have, please feel free to fill an issue.

We aim to have all translations reviewed by the 4th of July.

locale/es/episodes/delay-functions.Rmd

jd-otero · 2024-06-26T18:11:43Z

locale/es/episodes/delay-functions.Rmd

+# EpiNow2::NonParametric() can also accept the PMF values
+generation_time_influenza <-
+  EpiNow2::NonParametric(
+    pmf = influenza_generation_pmf
+  )
+
+# incubation period -------------------------------------------------------
+
+# Read the incubation period
+influenza_incubation <-
+  epiparameter::epidist_db(
+    disease = "influenza",
+    epi_dist = "incubation",
+    single_epidist = TRUE
+  )
+
+# Discretize incubation period
+influenza_incubation_discrete <-
+  epiparameter::discretise(influenza_incubation)
+
+influenza_incubation_max <-
+  quantile(influenza_incubation_discrete, p = 0.99)
+
+influenza_incubation_pmf <-
+  density(
+    influenza_incubation_discrete,
+    at = 1:influenza_incubation_max
+  )


Suggested change

# EpiNow2::NonParametric() can also accept the PMF values

generation_time_influenza <-

EpiNow2::NonParametric(

pmf = influenza_generation_pmf

)

# incubation period -------------------------------------------------------

# Read the incubation period

influenza_incubation <-

epiparameter::epidist_db(

disease = "influenza",

epi_dist = "incubation",

single_epidist = TRUE

)

# Discretize incubation period

influenza_incubation_discrete <-

epiparameter::discretise(influenza_incubation)

influenza_incubation_max <-

quantile(influenza_incubation_discrete, p = 0.99)

influenza_incubation_pmf <-

density(

influenza_incubation_discrete,

at = 1:influenza_incubation_max

)

# EpiNow2::NonParametric() también puede recibir valores de PMF

generation_time_influenza <-

EpiNow2::NonParametric(

pmf = influenza_generation_pmf

)

# Periodo de incubación -------------------------------------------------------

# Leer el periodo de incubación

influenza_incubation <-

epiparameter::epidist_db(

disease = "influenza",

epi_dist = "incubation",

single_epidist = TRUE

)

# Discretizar el periodo de incubación

influenza_incubation_discrete <-

epiparameter::discretise(influenza_incubation)

influenza_incubation_max <-

quantile(influenza_incubation_discrete, p = 0.99)

influenza_incubation_pmf <-

density(

influenza_incubation_discrete,

at = 1:influenza_incubation_max

)

jd-otero · 2024-06-26T18:12:47Z

locale/es/episodes/delay-functions.Rmd

+# EpiNow2::NonParametric() can also accept the PMF values
+incubation_time_influenza <-
+  EpiNow2::NonParametric(
+    pmf = influenza_incubation_pmf
+  )
+
+# epinow ------------------------------------------------------------------
+
+# Read data
+influenza_cleaned <-
+  outbreaks::influenza_england_1978_school %>%
+  select(date, confirm = in_bed)
+
+# Run epinow()
+epinow_estimates_igi <- epinow(
+  # cases
+  data = influenza_cleaned,
+  # delays
+  generation_time = generation_time_opts(generation_time_influenza),
+  delays = delay_opts(incubation_time_influenza)
+)
+
+plot(epinow_estimates_igi)


Suggested change

# EpiNow2::NonParametric() can also accept the PMF values

incubation_time_influenza <-

EpiNow2::NonParametric(

pmf = influenza_incubation_pmf

)

# epinow ------------------------------------------------------------------

# Read data

influenza_cleaned <-

outbreaks::influenza_england_1978_school %>%

select(date, confirm = in_bed)

# Run epinow()

epinow_estimates_igi <- epinow(

# cases

data = influenza_cleaned,

# delays

generation_time = generation_time_opts(generation_time_influenza),

delays = delay_opts(incubation_time_influenza)

)

plot(epinow_estimates_igi)

# EpiNow2::NonParametric() también puede recibit valores de PMF

incubation_time_influenza <-

EpiNow2::NonParametric(

pmf = influenza_incubation_pmf

)

# epinow ------------------------------------------------------------------

# Leer datos

influenza_cleaned <-

outbreaks::influenza_england_1978_school %>%

select(date, confirm = in_bed)

# Usar epinow

epinow_estimates_igi <- epinow(

# casos

data = influenza_cleaned,

# retrasos

generation_time = generation_time_opts(generation_time_influenza),

delays = delay_opts(incubation_time_influenza)

)

plot(epinow_estimates_igi)

jd-otero · 2024-06-26T18:14:16Z

locale/es/episodes/delay-functions.Rmd

+
+¿Cómo obtener la media y la desviación típica de un tiempo de generación con *sólo* parámetros de distribución pero sin estadísticas de resumen como `mean` o `sd` para `EpiNow2::Gamma()` o `EpiNow2::LogNormal()`?
+
+Mira el `{epiparameter}` viñeta en [extracción y conversión de parámetros](https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html) y su [casos de uso](https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html#use-cases) ¡!


Suggested change

Mira el `{epiparameter}` viñeta en [extracción y conversión de parámetros](https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html) y su [casos de uso](https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html#use-cases) ¡!

¡Mira en `{epiparameter}` la viñeta [extracción y conversión de parámetros](https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html) y sus [casos de uso](https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html#use-cases) !

jd-otero · 2024-06-26T18:17:47Z

locale/es/episodes/delay-functions.Rmd

+Consulta este excelente tutorial sobre la estimación del intervalo seriado y el período de incubación de la Enfermedad X teniendo en cuenta *la censura* utilizando la inferencia bayesiana con paquetes como `{rstan}` y `{coarseDataTools}`.
+
+- Tutoría en inglés: <https://rpubs.com/tracelac/diseaseX> <!-- para solicitar -->
+- Tutorial en Español: <https://epiverse-trace.github.io/epimodelac/EnfermedadX.html>
+
+**Entonces,** después de obtener tus valores estimados, puedes crear manualmente tus propios` <epidist>` objetos de clase con `epiparameter::epidist()` ¡! Echa un vistazo a su [guía de referencia sobre "Crear un `<epidist>` objeto"](https://epiverse-trace.github.io/epiparameter/reference/epidist.html#ref-examples) ¡!
+
+Por último, echa un vistazo al último [`{epidist}` paquete R](https://epidist.epinowcast.org/) que proporciona métodos para abordar los principales retos de la estimación de distribuciones, como el truncamiento, la censura por intervalos y los sesgos dinámicos.


Suggested change

Consulta este excelente tutorial sobre la estimación del intervalo seriado y el período de incubación de la Enfermedad X teniendo en cuenta *la censura* utilizando la inferencia bayesiana con paquetes como `{rstan}` y `{coarseDataTools}`.

- Tutoría en inglés: <https://rpubs.com/tracelac/diseaseX> 

- Tutorial en Español: <https://epiverse-trace.github.io/epimodelac/EnfermedadX.html>

**Entonces,** después de obtener tus valores estimados, puedes crear manualmente tus propios` <epidist>` objetos de clase con `epiparameter::epidist()` ¡! Echa un vistazo a su [guía de referencia sobre "Crear un `<epidist>` objeto"](https://epiverse-trace.github.io/epiparameter/reference/epidist.html#ref-examples) ¡!

Por último, echa un vistazo al último [`{epidist}` paquete R](https://epidist.epinowcast.org/) que proporciona métodos para abordar los principales retos de la estimación de distribuciones, como el truncamiento, la censura por intervalos y los sesgos dinámicos.

Consulta este excelente tutorial sobre la estimación del intervalo serial y el período de incubación de la Enfermedad X, teniendo en cuenta *la censura* por medio de inferencia bayesiana con paquetes como `{rstan}` y `{coarseDataTools}`.

- Tutorial en Inglés: <https://rpubs.com/tracelac/diseaseX> 

- Tutorial en Español: <https://epiverse-trace.github.io/epimodelac/EnfermedadX.html>

**Luego,** después de obtener tus valores estimados, ¡puedes crear manualmente tus propios objetos con clase ` <epidist>` por medio de `epiparameter::epidist()`! Echa un vistazo a su [guía de referencia sobre "Crear un objeto `<epidist>` "](https://epiverse-trace.github.io/epiparameter/reference/epidist.html#ref-examples) ¡!

Por último, echa un vistazo al último [paquete de R `{epidist}`](https://epidist.epinowcast.org/) que proporciona métodos para abordar los principales retos de la estimación de distribuciones, como el truncamiento, la censura por intervalos y los sesgos dinámicos.

jd-otero · 2024-06-26T18:18:48Z

locale/es/episodes/delay-functions.Rmd

+- Utilizar funciones de distribución con `<epidist>` para obtener estadísticas resumidas y parámetros informativos de las intervenciones de salud pública, como la Ventana de rastreo de contactos y la Duración de la cuarentena.
+- Utiliza `discretise()` para convertir distribuciones de retraso continuas en discretas.
+- Utiliza `{epiparameter}` para obtener los retardos de información necesarios en las estimaciones de transmisibilidad.


Suggested change

- Utilizar funciones de distribución con `<epidist>` para obtener estadísticas resumidas y parámetros informativos de las intervenciones de salud pública, como la Ventana de rastreo de contactos y la Duración de la cuarentena.

- Utiliza `discretise()` para convertir distribuciones de retraso continuas en discretas.

- Utiliza `{epiparameter}` para obtener los retardos de información necesarios en las estimaciones de transmisibilidad.

- Utilizar funciones de distribución con `<epidist>` para obtener estadísticas de resumen y parámetros informativos de las intervenciones de salud pública, como la Ventana de rastreo de contactos y la Duración de la cuarentena.

- Utilizar `discretise()` para convertir distribuciones de retraso continuas en discretas.

- Utilizar `{epiparameter}` para obtener los retrasos de información necesarios en las estimaciones de transmisibilidad.

CarmenTamayo

I've had a look until line 536, @juan-umana will check the rest and then we can review each other's translation?

CarmenTamayo · 2024-06-28T16:14:38Z