include example for quarto exercise

_freeze/assets/report/execute-results/html.json

@@ -0,0 +1,16 @@
+{
+  "hash": "043ebefe1245c4fcad7c4f52d5bbb1d7",
+  "result": {
+    "markdown": "---\ntitle: \"Random report\"\nauthor: \n  - name: Jane Doe\n    orcid: 0000-0001-2345-6789\n    corresponding: true\n    email: [email protected]\n    \n---\n\n\n<!-- \n\nInstructions for the exercise\n\n1. Move the file to the top level of your repository\n4. Update the file _quarto.yml in your repository to include the report in your webpage\n2. Load a dataset from https://github.com/Hezel2000/GeoCosmoChemDataAndTools/tree/main/csv\n3. Update the YAML header of this file\n3. Create a short report, including plots created from the dataset (they can be interactive if you want -- the result is a webpage)\n  a. You can use the example code chunks below as inspiration and for reference for the chunk options\n  b. You might the following pages useful for reference: \n      Figures: https://quarto.org/docs/authoring/figures.html\n      Tables: https://quarto.org/docs/authoring/tables.html\n\nThe remainder of the document is a small example of how it might look like and provides some help to get started. Compare the unrendered and the rendered version to learn how it looks like! \n\nBy the way, to include Python code, replace write `python` instead of `r` in the curly brackets at the top of a code chunk. \n\n--> \n\n## The randomness of R\n\nBecause there must be some content for inspiration, let's see how random random numbers in R really are. \n\n## Uniformly distributed random numbers\n\nUniformly distributed random number means that all number have the same chance to get drawn from the pool. There are multiple methods to produce such numbers. \n\nThe most common command to create a uniform distribution of random numbers in R is `runif()`, standing for **r**andom **unif**orm. There is also the command `sample()` which is a bit more complex and therefore not further discussed here. \n\nBy default, `runif()` samples the range from 0 to 1: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nrunif(1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 0.9265252\n```\n:::\n\n```{.r .cell-code}\nrunif(10)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 0.02101488 0.27730595 0.11794147 0.75370403 0.46413168 0.59671102\n [7] 0.47710212 0.38529248 0.45986743 0.45998668\n```\n:::\n:::\n\n\nThis range can be changed by providing additional arguments: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nrunif(1, min = 6, max = 9)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 6.761828\n```\n:::\n\n```{.r .cell-code}\nrunif(10, min = 4, max = 20)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1]  9.600644 19.682894 19.497662 14.913211 11.988543 19.399974  6.373147\n [8] 14.364414 10.415595 15.042221\n```\n:::\n:::\n\n\nSometimes, integers are needed. This can be achieved with \n\n\n::: {.cell}\n\n```{.r .cell-code}\nfloor(runif(1, min = 1, max = 6))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2\n```\n:::\n:::\n\n\nWith this information, let's see how random R throws a D20! \n\n\n::: {.cell layout-ncol=\"2\"}\n::: {.cell-output-display}\n![10 rolls](report_files/figure-html/unnamed-chunk-4-1.png){width=672}\n:::\n\n::: {.cell-output-display}\n![100 rolls](report_files/figure-html/unnamed-chunk-4-2.png){width=672}\n:::\n\n::: {.cell-output-display}\n![1000 rolls](report_files/figure-html/unnamed-chunk-4-3.png){width=672}\n:::\n\n::: {.cell-output-display}\n![10000 rolls](report_files/figure-html/unnamed-chunk-4-4.png){width=672}\n:::\n:::\n\n\nAnd now let's check if the distribution is consistent within the same number of rolls: \n\n\n::: {.cell layout-ncol=\"5\"}\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-5-1.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-5-2.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-5-3.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-5-4.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-5-5.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-5-6.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-5-7.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-5-8.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-5-9.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-5-10.png){width=672}\n:::\n:::\n\n\n# Normally distributed random numbers\n\nR cannot also create numbers from a normal distribution with the command `rnorm()` (= **r**andom **normal**). The syntax of the function is similar to `runif()` but instead of defining minimum and maximum of a function, we give the mean and the standard deviation ($sd$) of the normal distribution: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nrnorm(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1]  0.09820888  1.14904771  0.57652705 -0.50144593 -0.31721441\n```\n:::\n\n```{.r .cell-code}\nrnorm(5, mean = 50, sd = 5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 44.05445 47.36258 55.18560 56.04320 54.03772\n```\n:::\n:::\n\n\nLet's repeat the same test as above: \n\n\n::: {.cell layout-ncol=\"2\"}\n::: {.cell-output-display}\n![10 draws](report_files/figure-html/unnamed-chunk-7-1.png){width=672}\n:::\n\n::: {.cell-output-display}\n![100 draws](report_files/figure-html/unnamed-chunk-7-2.png){width=672}\n:::\n\n::: {.cell-output-display}\n![1000 draws](report_files/figure-html/unnamed-chunk-7-3.png){width=672}\n:::\n\n::: {.cell-output-display}\n![10000 draws](report_files/figure-html/unnamed-chunk-7-4.png){width=672}\n:::\n:::\n\n\nAnd performance in repeated identical draws. However, this time we will draw 50 times because 10 is too few to make the normal distribution visible: \n\n\n::: {.cell layout-ncol=\"5\"}\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-8-1.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-8-2.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-8-3.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-8-4.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-8-5.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-8-6.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-8-7.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-8-8.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-8-9.png){width=672}\n:::\n\n::: {.cell-output-display}\n![](report_files/figure-html/unnamed-chunk-8-10.png){width=672}\n:::\n:::\n\n\n\nIf you forgot how the standard deviation is calculated (and because I wanted to include a more complex formula for instructive purposes): \n\n$$\n\\sigma = \\sqrt{\\frac{\\sum{(x_i-\\mu)^2}}{N}} \n$$\nwith: \n\n| Symbol || Explanation |\n|----:|----|:----|\n|$\\sigma$| = | Population standard deviation| \n|$N$| = | Size of population |\n|x~i~| = | each value from the population |\n|µ| = | mean of the population |\n\n# Conclusions\nAs expected, the number from random distributions are pretty random \n\nHave fun while doing the exercise and don't hesitate to ask for help! \n",
+    "supporting": [
+      "report_files"
+    ],
+    "filters": [
+      "rmarkdown/pagebreak.lua"
+    ],
+    "includes": {},
+    "engineDependencies": {},
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+    "postProcess": true
+  }
+}

assets/report.qmd

@@ -0,0 +1,159 @@
+---
+title: "Random report"
+author: 
+  - name: Jane Doe
+    orcid: 0000-0001-2345-6789
+    corresponding: true
+    email: [email protected]
+
+---
+
+<!-- 
+
+Instructions for the exercise
+
+1. Move the file to the top level of your repository
+4. Update the file _quarto.yml in your repository to include the report in your webpage
+2. Load a dataset from https://github.com/Hezel2000/GeoCosmoChemDataAndTools/tree/main/csv
+3. Update the YAML header of this file
+3. Create a short report, including plots created from the dataset (they can be interactive if you want -- the result is a webpage)
+  a. You can use the example code chunks below as inspiration and for reference for the chunk options
+  b. You might the following pages useful for reference: 
+      Figures: https://quarto.org/docs/authoring/figures.html
+      Tables: https://quarto.org/docs/authoring/tables.html
+
+The remainder of the document is a small example of how it might look like and provides some help to get started. Compare the unrendered and the rendered version to learn how it looks like! 
+
+By the way, to include Python code, replace write `python` instead of `r` in the curly brackets at the top of a code chunk. 
+
+--> 
+
+## The randomness of R
+
+Because there must be some content for inspiration, let's see how random random numbers in R really are. 
+
+## Uniformly distributed random numbers
+
+Uniformly distributed random number means that all number have the same chance to get drawn from the pool. There are multiple methods to produce such numbers. 
+
+The most common command to create a uniform distribution of random numbers in R is `runif()`, standing for **r**andom **unif**orm. There is also the command `sample()` which is a bit more complex and therefore not further discussed here. 
+
+By default, `runif()` samples the range from 0 to 1: 
+
+``` {r}
+
+runif(1)
+
+runif(10)
+
+``` 
+
+This range can be changed by providing additional arguments: 
+
+``` {r}
+
+runif(1, min = 6, max = 9)
+
+runif(10, min = 4, max = 20)
+
+``` 
+
+Sometimes, integers are needed. This can be achieved with 
+
+``` {r}
+
+floor(runif(1, min = 1, max = 6))
+
+``` 
+
+With this information, let's see how random R throws a D20! 
+
+``` {r}
+#| layout-ncol: 2
+#| fig-cap: 
+#|   - "10 rolls"
+#|   - "100 rolls"
+#|   - "1000 rolls" 
+#|   - "10000 rolls"
+#| echo: false
+
+hist(runif(10, min = 1, max = 20), breaks = seq(from = 0.5, to = 20.5), main = NULL)
+hist(runif(100, min = 1, max = 20), breaks = seq(from = 0.5, to = 20.5), main = NULL)
+hist(runif(1000, min = 1, max = 20), breaks = seq(from = 0.5, to = 20.5), main = NULL)
+hist(runif(10000, min = 1, max = 20), breaks = seq(from = 0.5, to = 20.5), main = NULL)
+
+```
+
+And now let's check if the distribution is consistent within the same number of rolls: 
+
+``` {r}
+#| layout-ncol: 5
+#| echo: false
+
+for (i in seq_along(1:10)) {
+  hist(runif(10, min = 1, max = 20), breaks = seq(from = 0.5, to = 20.5), main = NULL)
+}
+
+```
+
+# Normally distributed random numbers
+
+R cannot also create numbers from a normal distribution with the command `rnorm()` (= **r**andom **normal**). The syntax of the function is similar to `runif()` but instead of defining minimum and maximum of a function, we give the mean and the standard deviation ($sd$) of the normal distribution: 
+
+``` {r}
+
+rnorm(5)
+
+rnorm(5, mean = 50, sd = 5)
+
+```
+
+Let's repeat the same test as above: 
+
+``` {r}
+#| layout-ncol: 2
+#| fig-cap: 
+#|   - "10 draws"
+#|   - "100 draws"
+#|   - "1000 draws" 
+#|   - "10000 draws"
+#| echo: false
+
+hist(rnorm(10, mean = 50, sd = 5), main = NULL)
+hist(rnorm(100, mean = 50, sd = 5), main = NULL)
+hist(rnorm(1000, mean = 50, sd = 5), main = NULL)
+hist(rnorm(10000, mean = 50, sd = 5), main = NULL)
+
+```
+
+And performance in repeated identical draws. However, this time we will draw 50 times because 10 is too few to make the normal distribution visible: 
+
+``` {r}
+#| layout-ncol: 5
+#| echo: false
+
+for (i in seq_along(1:10)) {
+  hist(rnorm(50, mean = 50, sd = 5), main = NULL)
+}
+
+```
+
+
+If you forgot how the standard deviation is calculated (and because I wanted to include a more complex formula for instructive purposes): 
+
+$$
+\sigma = \sqrt{\frac{\sum{(x_i-\mu)^2}}{N}} 
+$$
+with: 
+
+| Symbol || Explanation |
+|----:|----|:----|
+|$\sigma$| = | Population standard deviation| 
+|$N$| = | Size of population |
+|x~i~| = | each value from the population |
+|µ| = | mean of the population |
+
+# Conclusions
+As expected, the number from random distributions are pretty random 
+
+Have fun while doing the exercise and don't hesitate to ask for help!