Code for R compatibility with GreenCode #17

modules/software-development-handson/exercises_codecarbon.md

@@ -1,5 +1,5 @@
 ---
-title: Code Carbon
+title: "Code Carbon"
 type: exercise
 order: 5
 ---
@@ -209,6 +209,108 @@ emissions can be reduced, mostly due to a shorter runtime. Feel free to experime
  - try an alternative implementation of the distance computation
  - add timers to the script to estimate if the average power, is it changing between the two provided implementation ?
 
+## Calling CodeCarbon in an R script
+------------------------------------
+
+The following workflow is available in this [R script](https://github.com/javimangal/green-digital-skills-r/blob/main/R/code-carbon-script.R), which you can reuse and adapt to test your code.
+
+First, you will need to install the `reticulate` package and load it into your session: 
+
+```
+install.packages("reticulate")
+library(reticulate)
+```
+Load the CodeCarbon module using the reticulate [`import`](https://rstudio.github.io/reticulate/reference/import.html) function.
+
+```
+codecarbon <- import("codecarbon")
+```
+
+Import the OfflineEmissionsTracker class
+
+```
+OfflineEmissionsTracker <- codecarbon$OfflineEmissionsTracker
+```
+
+Set the emission trackers parameter and initialize. This will automatically detect your system's characteristics and open a file to save the report later on. You can also specify the country code, timing of measurements, among others. See the [CodeCarbon documentation](https://mlco2.github.io/codecarbon/parameters.html#id6) for more details: 
+
+```
+tracker <- OfflineEmissionsTracker(
+  country_iso_code = "NLD",
+  measure_power_secs = 5
+)
+```
+
+Start tracking the emissions, run your code, and finish tracking once your code ran. 
+
+```
+tracker$start()
+
+# Your R code here
+
+tracker$stop()
+```
+
+This will terminate the report and save it in your default working directory, unless you specified earlier the path to save it according to the provided documentation.
+
+Note that CodeCarbon will continue to track until you explicitly stop it with `tracker$stop()`. Thus, you may want to run all lines of code from start -> code -> stop in one go to measure the consumption of that specific code. 
+
+## Calling CodeCarbon in an Quarto markdown (qmd) file in RStudio
+-----------------------------------------------------------------
+
+If you use Quarto, you can call Python and R code in the same document, using their native syntax. For this, you can specify the language of the code block using the `python` or `r` tags: 
+
+```
+{python}
+
+```
+
+```
+{r}
+
+```
+
+RStudio automatically process the programming language. R is the default language in Quarto when introducing code blocks. If you want to run Python code, you need to change `r` for the the `python` tag. 
+
+The advantage of using quarto is that you don't need to rewrite python code into the `reticulate` syntax. You can simply copy and paste the python code into the `python` code block. This is a great advantage when you are not familiar with Python, when you want to reuse well-documented native python code, or simply to efficiently communicate with python programmers.
+
+Because Rstudio loads the reticulate package once it reads the `python` tag, you don't need to load the `reticulate` package in the code block. Thus, we will start by calling the CodeCarbon module in Python. 
+
+```
+{python}
+# Load the CodeCarbon module
+from codecarbon import EmissionsTracker
+```
+
+Then, we will initialize the tracker and start tracking the emissions. 
+
+```
+{python}
+# Initialize the tracker
+tracker = OfflineEmissionsTracker(country_iso_code="NLD",
+                                 measure_power_secs=5)
+
+# Start tracking the emissions
+tracker.start()
+```
+
+You can now change to R language by using the `r` tag.
+
+```
+{r}
+# Introduce your R code here. For example, a fictitious computationally 
+# intensive function for which you would like to test it's energy consumption. 
+intense_computation(large_dataset)
+```
+
+Finally, you can stop tracking the emissions and print the results. You will again find the report as a csv file in your default working directory. 
+
+```
+{python}
+emissions: float = tracker.stop()
+print(emissions)
+```
+
 ## Using CodeCarbon: your own code
 ----------------------------------
 If your target code is based on Python, the simplest way to adapt the small example provided above to